Experimental Setup for Investigating the Efficient Load Balancing

sensors

Article Experimental Setup for Investigating the Eﬃcient Load Balancing Algorithms on Virtual Cloud

Bhavya Alankar 1, Gaurav Sharma 1, Harleen Kaur 1,*, Raul Valverde 2 and Victor Chang 3,* 1 Department of Computer Science and Engineering, School of Engineering Sciences and Technology, Jamia Hamdard, New Delhi 110062, India; [email protected] (B.A.); [email protected] (G.S.) 2 John Molson School of Business, Concordia University, Montreal, QC G1X 3X4, Canada; [email protected] 3 Artiﬁcial Intelligence and Information Systems Research Group, School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough TS1 3BX, UK * Correspondence: [email protected] (H.K.); [email protected] or [email protected] (V.C.)

Received: 2 November 2020; Accepted: 16 December 2020; Published: 21 December 2020

Abstract: Cloud computing has emerged as the primary choice for developers in developing applications that require high-performance computing. Virtualization technology has helped in the distribution of resources to multiple users. Increased use of cloud infrastructure has led to the challenge of developing a load balancing mechanism to provide optimized use of resources and better performance. Round robin and least connections load balancing algorithms have been developed to allocate user requests across a cluster of servers in the cloud in a time-bound manner. In this paper, we have applied the round robin and least connections approach of load balancing to HAProxy, virtual machine clusters and web servers. The experimental results are visualized and summarized using Apache Jmeter and a further comparative study of round robin and least connections is also depicted. Experimental setup and results show that the round robin algorithm performs better as compared to the least connections algorithm in all measuring parameters of load balancer in this paper.

Keywords: cloud computing; load balancing/balancer; round robin; least connections; virtual machine; HAProxy; Apache Jmeter

1. Introduction Technological innovations are surprising the human race every day. Cloud computing is one of them. It is emerging as a new umbrella field of various new applications as well as services. Rarely any field remains untouched by it. Its importance is significantly seen and felt today. There is no pre-defined or standard definition of cloud computing. Usually, it contains a cluster of servers known as controller servers that necessitate services and assets to different users in a network with scalability and reliability of the datacenter [1]. Virtualization technology allows us to work with powerful and multiple virtual machines, tweak-based configurations, and obtain the resources with core, RAM, and storage, to run on the same physical machine. The cloud provides 24/7 cost-free support, an open-source platform, automation tools, speed up the delivery, cloud-based applications, and much more. High the demand for cloud services and high-traffic web applications requires a highly capable well-organized load balancer for a smooth process of their industry. In a cloud environment, load balancing is applied on a cluster of servers and is used to achieve a variety of daemons, including Hypertext Transfer Protocol, Hypertext Transfer Protocol Secure, File Transfer Protocol, Simple Mail Transfer Protocol, Domain Name System, Post Office Protocol/Internet Message Access Protocol, and other requests [2].

Sensors 2020, 20, 7342; doi:10.3390/s20247342 www.mdpi.com/journal/sensors Sensors 2020, 20, 7342 2 of 26

Load balancing refers to proficiently allocating incoming network traffic to a cluster of backend servers. The primary key function of the load balancer is to make sure that each workstation of the network is utilized with an equally distributed amount of work. The load is not only for the web application traffic; it has various types like CPU load, network load, memory capacity concerns, etc. In the cloud computing environment, load balancing is used to allocate virtual machines across all end-user devices to improve asset utilization and provide high gratification to the audience [3]. This means neither of the workstations in the cloud goes overloaded or underutilized. In general, a load balancer acts as a reverse proxy that allocates network or web application traffic across a number of servers. Load balancers endorse three primary purposes. First, refining complete servers’ performance by accomplishing a high resource consumption ratio. Second, evading the server’s bottleneck that happens due to improper load balance. Third, it is important to attain high providers’ and audience’s gratification by increasing the servers’ throughput and decreasing the processing time load balancing assistances in preserving workstations’ insistence, performance, and security against workstation failures [4]. Recent high-visitors’ net programs serve more than thousands of concurrent requests from audiences or clients and return the precise text, photographs, video, or application data, completely short and consistent. In order to correctly price the scale to satisfy these high potentials, contemporary computing great practices typically call for including extra servers. A load balancer acts as the “website visitors cop” sitting in the front of your servers and routing customer requests across all clusters of servers, proficient in accomplishing one’s requests in a way that will increase pace and capacity usage and confirm that no one server is overloaded (which could affect overall performance). If a single server is going down, the load balancer relays traffic to the active closing servers. Whilst a new server is delivered to the server cluster, the load balancer routinely starts to send requests to it. In enormous datacenters, an additional quantity of load balancers is essential to balance the work across various web servers. Different load balancing algorithms like round robin, weighted round robin, and IP-based hashing are used in load balancers. All these algorithms do not see the live traffic before accelerating the request to the servers and promise not to run random algorithms on the load balancers as they are dealer specific and not run on product hardware and custom software [5]. The load balancer equally allocates the information subject to how demanding the server is. Without a load balancer, the audience would face delays in processing the information that might be annoying. At the time of load balancing progression, information like incoming tasks and vCPU (virtual CPU) processing rates are substituted among the vCPUs. Any downtime in the web application of load balancers can amount to severe penalties such as statistics loss. Several corporations use different types of load balancers, along with various load balancing algorithms. One of the most commonly used algorithms is the “round robin” or “least connections” load balancing. This paper aims to investigate the role of using load balancer algorithms on live cloud infrastructure and open-source services, which leads to the reduction of the cost of the services. After experimental results, this paper also suggests that the round robin algorithm gives better performance results that help users choose the best algorithm for load balancing in their high web traffic applications. This paper is organized as follows: Section2 describe the background, types of load balancers, and its measurement parameters. Section3 describes related works. Section4 is on the methodology and experimental setup and practical implementation of the paper. Section5 includes comparing two used load balancing algorithms and shows the results and evaluation of the test cases. The final Section6 describes the conclusions of this paper.

2. Background

2.1. Types of Load Balancer In the Introduction section, we discussed the load balancer. Our main aim is to give a brief idea of types of load balancers and how cloud users can choose the load balancer in cloud applications Sensors 2020, 20, 7342 3 of 26 according to their requirements. There are some unique styles of load balancing that exist. We all know about network infrastructure, inclusive of Maria DB Server or SQL. Server load balancing for a relational database, worldwide server load balancing for troubleshooting across several geographic locations, and domain name server load balancing to assure domain name call functionality. We can also recall varieties of load balancers in phrases of the numerous cloud-based balancers: AWS Elastic Load Balancer, DigitalOcean Load Balancer, and so forth.

Network Load Balancer: A network load balancer or Layer 4 Balancer uses the fourth layer of • the OSI Model, which means it picks the information from the network layer for the route the network visitors that are dead through layer four load balancing and should handle the incoming request of TCP/UDP website visitors. Among a variety of load balancers, Network Load Balancer is the quickest load-balancer. Sometimes, it performs incline to drop once it routes the incoming network visitors across web application servers. HTTP(S) Load Balancer: The HTTP(S) Load balancer works on the application layer or seventh • layer of the OSI model. HTTP uses session’s ids, cookies, and HTTP headers to decide how the network traffic or web visitors will be routed across all the web application clusters. Internal Load Balancer: Internal load balancing works on the layer 4 OSI model, similar to network • load balancing. Internal load balancing is mostly implemented in onsite infrastructure to manage, stabilize, and balance the physical servers and virtual machines, including network area storage. Hardware Load Balancer: A hardware load balancer is a physical device that comes with a • pre-installed operating system. Its role is to distribute or allocate the web users’ traffic across all the web application server farms (A server farm is a collection of webservers with network area storage on which web application hosted). Hardware Load Balancer requires a minimum of two virtual machines. It is configured by the system administrator with their custom rules to ensure the best performance, and the virtual machines are not overloaded. Hardware Load Balancer is not affordable for every user because it is very expensive, and it is dependent on the architecture and, hardware appliance of the infrastructure. Software Load Balancer: A software load balancer is a software-defined balancer that can be • easily installed and configured on x86/64 bit servers or virtual machines. A minimum of four virtual machines are required for the software load balancer setup—one VM is used as a software load balancer and the other three virtual machines are used for web server farms. It is easily scale-able in real-time traffic and free from the architecture and configuration of virtual machines. The software load balancer is open-source and falls under commercial service as well. Virtual Load Balancer: A virtual load balancer acts as a software load balancer, but it is different • from software load balancers. The virtual load balancer distributes the web traffic by taking the software program of the hardware load balancer, which was installed on virtual machines.

2.2. Load Balancing Measurement Parameter The main purpose of this section is to give the idea of which parameters we can measure to gauge the performance of the load balancing algorithm. There are always some limitations, restrictions, and some dimension constraints to calculate the load balancing methods, which allow checking whether the given method is worthy of balancing the load or not, which is given as follows: Throughput: This parameter imitates the capability of the server. The capability of the server • means how much weight it can take. It is one of the important parameters that support calculating the performance of web applications. Maximum throughput is always expected. Throughput is calculated as the number of requests in a given time or transactions per second. Average Response Time: It is the aggregate of time used to start satisfying the request of the user • after the process of the request. Fault tolerance: The capability of the load balancing algorithm that permits the structure to work • in some falls down the state of the system. Sensors 2020, 20, 7342 4 of 26

Scalability: The algorithm can scale itself according to requisite situations. • Performance: It is the complete check of the algorithms functioning by seeing precision, price, • and quickness. Resource utilization: It is used to retain a check on the consumption of the number of resources. • 2.3. Categorization of Load Balancing Algorithms Load balancing algorithms can be categorized into two types: static and dynamic algorithms. The static algorithm methodology largely considers the traffic at each instance in the network and distributes the traffic uniformly between all the instances. Dynamic algorithm methodology conversely considers only the current state of the system while making verdicts of allocating the load. In cloud computing, the dynamic algorithm is commonly used for huge networks. round robin and source hashing algorithms are some static algorithms, whereas least-connection, weighted least connection, and least bandwidth algorithms are some examples of dynamic algorithms. The summarized features, advantage, disadvantage and differences of the different load balancing algorithms are presented in the Table1.

Table 1. The summarized features, advantage, disadvantage and diﬀerences of the diﬀerent load balancing algorithms.

Algorithm Request Serve Method Nature Advantage Disadvantage The request is given to the Easy to configure, Servers can overload and crash if Round Robin server in a rotated Dynamic deploy and widely they have different resources and Sequential Manner used algorithm processing capacities. Avoids The request is given to the overloading a When calculating the number of Least Connections server with the lowest Dynamic server by verifying existing connections, the server number of connections. the number of capacity cannot be considered. server connections All the estimation requires Can send more implementing this algorithm, and Weighted Round Every server is used, in turn, requests to more this is a major drawback and also Static Robin by weight. capable and loaded requires estimating IP networks servers with different packet sizes, which are difficult to do. Users connect to a The source IP address shall still active session be hashed and divided by Internet Service Provider (ISP) after disconnection Source Hash the total number of servers Static provides dynamic IP addresses, so and reconnection. operating to decide which it is difficult to maintain them. It will increase server the request receives. performance. Consider both the Simple performing virtual server’s capacity, machines are used, then the The request is given to the response time and Least Response unequal route of traffic might be server with the lowest Dynamic the number of Time shown and this algorithm is not response time. current connections recommended for cookie-based to avoid overload session applications. and crash. Can send more It requires approximate network requests to more bandwidth, which is difficult to do Every server is used, in turn, capable and Least Bandwidth Static in networks where the packet size by network bandwidth. network of the data varying, and network bandwidth loaded bandwidth might be exhausted. servers.

2.3.1. Round Robin Algorithm The round robin algorithm depends on a rotation system or rotationally selects the servers. An incoming request is a substitute to the primary reachable server, and then the server is bounced to the cease of the line, as shown in Figure1. This algorithm is by and large beneﬁcial while functioning with servers of identical importance. It is the simplest and easy to implement a load balancing algorithm, as it performs best when the resources of the virtual machines are the same. Sensors 2020, 20, x FOR PEER REVIEW 4 of 26

• Average Response Time: It is the aggregate of time used to start satisfying the request of the user after the process of the request. • Fault tolerance: The capability of the load balancing algorithm that permits the structure to work in some falls down the state of the system. • Scalability: The algorithm can scale itself according to requisite situations. • Performance: It is the complete check of the algorithms functioning by seeing precision, price, and quickness. • Resource utilization: It is used to retain a check on the consumption of the number of resources.

2.3. Categorization of Load Balancing Algorithms Load balancing algorithms can be categorized into two types: static and dynamic algorithms. The static algorithm methodology largely considers the traffic at each instance in the network and distributes the traffic uniformly between all the instances. Dynamic algorithm methodology conversely considers only the current state of the system while making verdicts of allocating the load. In cloud computing, the dynamic algorithm is commonly used for huge networks. round robin and source hashing algorithms are some static algorithms, whereas least-connection, weighted least connection, and least bandwidth algorithms are some examples of dynamic algorithms. The summarized features, advantage, disadvantage and differences of the different load balancing algorithms are presented in the Table 1

2.3.1. Round Robin Algorithm The round robin algorithm depends on a rotation system or rotationally selects the servers. An incoming request is a substitute to the primary reachable server, and then the server is bounced to the cease of the line, as shown in Figure 1. This algorithm is by and large beneficial while functioning with servers of identical importance. It is the simplest and easy to implement a load balancing Sensors 2020, 20, 7342 5 of 26 algorithm, as it performs best when the resources of the virtual machines are the same.

FigureFigure 1. 1. RoundRound robin robin algorithm. algorithm.

2.3.2.2.3.2. Weighted Weighted Round Round Robin Robin Algorithm Algorithm EachEach weight weight server, server, is is used used in in turn. The The weight weighteded round round robin robin algorithm algorithm is is similar similar to to the the round round robinrobin one, one, but but is is static static in in nature, nature, so so it it doesn’t doesn’t work work by by changing changing the the weight weight of of the the server server on on the the wing. wing. OnOn the the other other hand, hand, the the number number of of servers servers is is not not lim limitedited and and frequently frequently replicated replicated immediately immediately after after a a fullfull map map is is re-published re-published when when a aserver server increase. increase. It Ituses uses less less CPU CPU to torun run too. too. A weight A weight is allocated is allocated to thetoSensors therespective 2020 respective, 20, x serverFOR server PEER established REVIEW established on on norms norms chosen chosen by by the the cloud cloud system system administrator; administrator; the the 5most most of 26 frequentlyfrequently used used ways ways are are the the serv server’ser’s visitors-handling visitors-handling capability. capability. The The higher higher the the weight, weight, the the higher higher thethe percentagepercentage ofof visitorvisitor requestsrequests thethe serverserver accepts,accepts, asas inin FigureFigure2 2.. For For example, example, server1 server1 is is allocated allocated aa weight of 4 4 and and server server 2 2a aweight weight of of 2. 2.The The load load balancer balancer forwards forwards four four requests requests to server to server 1 for 1each for eachtwo it two sends itsends to server to server2. If some 2.If virtual some machines virtual machines have more have resources more resources in vCPU, inRam, vCPU, GPU, Ram, and GPU,other andspecifications, other specifications, then the weighted then the weighted round robin round algo robinrithm algorithm is used. isWith used. the With help the of helpweighted of weighted round roundrobin, cloud robin, admin cloud admincan easi canly distribute easily distribute the web the traf webfic on tra highffic onresource high resource virtual machines virtual machines by defining by definingweight on weight it. In onadvance, it. In advance, all the estimation all the estimation requires requires implementing implementing this algorithm, this algorithm, and this and is thisa major is a majordrawback. drawback.

Figure 2. Weighted round robinrobin algorithm.algorithm. 2.3.3. Source Hash 2.3.3. Source Hash The source (user’s) IP address absolutely determines which server receives its request on this The source (user’s) IP address absolutely determines which server receives its request on this sincere load balancing algorithm, as in Figure3. This algorithm is used when the application provider sincere load balancing algorithm, as in Figure 3. This algorithm is used when the application provider wants the same user route to the same server where the last request has been served. It is very diﬃcult wants the same user route to the same server where the last request has been served. It is very difficult to serve the request of the same server because internet service provider (ISP) provides dynamic IP addresses, so it is difficult to maintain it.

Figure 3. IP hash algorithm.

2.3.4. Least Connections As its name states, the least connection algorithm instantly sites visitors to whichever server has the least extent of active connections that are supportive throughout heavy visitor’s durations. It facilitates preservation, even distribution amongst all available servers, as shown in Figure 4. Least connections algorithm widely used when longer sessions are required, like MariaDB or SQL, where the transaction rate high, active directory, lightweight directory access protocol (LDAP), etc. This algorithm is not recommended for short sessions applications like HTTP.

Sensors 2020, 20, x FOR PEER REVIEW 5 of 26 the percentage of visitor requests the server accepts, as in Figure 2. For example, server1 is allocated a weight of 4 and server 2 a weight of 2. The load balancer forwards four requests to server 1 for each two it sends to server 2. If some virtual machines have more resources in vCPU, Ram, GPU, and other specifications, then the weighted round robin algorithm is used. With the help of weighted round robin, cloud admin can easily distribute the web traffic on high resource virtual machines by defining weight on it. In advance, all the estimation requires implementing this algorithm, and this is a major drawback.

Figure 2. Weighted round robin algorithm.

2.3.3. Source Hash The source (user’s) IP address absolutely determines which server receives its request on this Sensors 2020, 20, 7342 6 of 26 sincere load balancing algorithm, as in Figure 3. This algorithm is used when the application provider wants the same user route to the same server where the last request has been served. It is very difficult toto serve serve the the request request of of the the same same server server because because internet internet service service provider provider (ISP) (ISP) provides provides dynamic dynamic IP IP addresses,addresses, so so it it is is difficult diﬃcult to to maintain maintain it.

Figure 3. IP hash algorithm. Figure 3. IP hash algorithm. 2.3.4. Least Connections 2.3.4. Least Connections As its name states, the least connection algorithm instantly sites visitors to whichever server has the leastAs extent its name of active states, connections the least connection that are supportive algorithm throughout instantly heavysites visitors visitor’s to durations.whichever It server facilitates has thepreservation, least extent even of distributionactive connections amongst that all are available supportive servers, throughout as shown inheavy Figure visitor’s4. Least durations. connections It facilitatesalgorithm preservation, widely used wheneven longerdistribution sessions amongst are required, all available like MariaDB servers, or as SQL, shown where in Figure the transaction 4. Least connectionsrate high, active algorithm directory, widely lightweight used when directory longer sessions access protocol are required, (LDAP), like etc. MariaDB This algorithm or SQL, where is not Sensorstherecommended transaction 2020, 20, x FOR forrate PEER short high, REVIEW sessions active applicationsdirectory, lightw like HTTP.eight directory access protocol (LDAP), etc.6 Thisof 26 algorithm is not recommended for short sessions applications like HTTP.

Figure 4. Least connection algorithm. Figure 4. Least connection algorithm. 2.3.5. Least Response Time 2.3.5. Least Response Time The least response time algorithm directs visitors to the server with the least amount of active connectionsThe least and response the lowest time common algorithm response directs time,visitors as shownto the server in Figure with5. Leastthe least connections amount of are active used connectionswhere two parametersand the lowest (least common active connectionsresponse time, and as low shown response in Figure time) 5. areLeast combined connections with are per used VM. whereHowever, two itparameters requires high (least performing active connections virtual machines; and low if simpleresponse performing time) are virtualcombined machines with per are used,VM. However,then the unequal it requires route high of tra performingﬃc might bevirtual shown. machines; This algorithm if simple is notperforming recommended virtual for machines cookie-based are used,session then applications. the unequal route of traffic might be shown. This algorithm is not recommended for cookie-based session applications.

Figure 5. Least response time.

2.3.6. Least Bandwidth Algorithm This least bandwidth algorithm processes visitors in megabits (Mbps) per second, sending users’ requests to the server with the smallest Mbps of visitors as given in Figure 6. The least bandwidth algorithm is very handy when all virtual machines in cloud infrastructure have different network bandwidth. It requires approximate network bandwidth, which is difficult to do in networks where the packet size of the data varying, and network bandwidth might be exhausted.

Sensors 2020, 20, x FOR PEER REVIEW 6 of 26

Figure 4. Least connection algorithm.

2.3.5. Least Response Time The least response time algorithm directs visitors to the server with the least amount of active connections and the lowest common response time, as shown in Figure 5. Least connections are used where two parameters (least active connections and low response time) are combined with per VM. However, it requires high performing virtual machines; if simple performing virtual machines are used, then the unequal route of traffic might be shown. This algorithm is not recommended for cookie-basedSensors 2020, 20 session, 7342 applications. 7 of 26

Figure 5. Least response time. Figure 5. Least response time. 2.3.6. Least Bandwidth Algorithm 2.3.6. Least Bandwidth Algorithm This least bandwidth algorithm processes visitors in megabits (Mbps) per second, sending users’ requestsThis least to the bandwidth server with algorithm the smallest processes Mbps visitors of visitors in megabits as given (Mbps) in Figure per 6second,. The least sending bandwidth users’ requestsalgorithm to the is very server handy with whenthe smallest all virtual Mbps machines of visitors in cloudas given infrastructure in Figure 6. have The least diﬀerent bandwidth network algorithmbandwidth. is very It requires handy approximatewhen all virtual network machines bandwidth, in cloud which infrastructure is diﬃcult have to do different in networks network where bandwidth.Sensorsthe packet 2020, 20 It size, x requires FOR of PEER the dataapproximate REVIEW varying, network and network bandwidt bandwidthh, which might is difficult be exhausted. to do in networks where7 of 26 the packet size of the data varying, and network bandwidth might be exhausted.

Figure 6. Least bandwidth. Figure 6. Least bandwidth. 3. Related Works TableSome 1. people The summarized have been donefeatures, diff advantage,erent kinds disadvanta of worksge to evaluateand differences the load of balancingthe different performance load of thebalancing many loadalgorithms. balancing algorithms in cloud computing. We will reflect on some of this work in this section. Request Serve Algorithm Nature Advantage Disadvantage In recent years,Method result outputs in [6–8] have seen incredible growth in communication and computing devices and correlated applications. With this, the necessityServers for CPU, can RAM,overload bandwidth, and The request is given to Easy to configure, storage, and computing power has developed exponentially. In recent timescrash many if they computing have different service Round Robin the server in a rotated Dynamic deploy and widely resources and processing providers haveSequential moved towardsManner the cloud for enhancedused algorithm scalability and decreased infrastructure price. The work presented incorporates a load balancer at cloud infrastructurecapacities. with software-defined When calculating the networking (SDN).The request The round is given robin to job schedulingAvoids is implemented overloading a on the load balancer to assign the number of existing tasksLeast to the webthe servers. server with Time the is analyzed to processserver by the verifying requests the of the users with and without a Dynamic connections, the server loadConnections balancer andlowest display number the of results to help in consideringnumber of server the benefits and drawbacks of using load capacity cannot be connections. connections balancers in the cloud. considered. Feasibly, the most fascinating part of cloud computing is its flexibility.All the estimation To be able requires to use the flexibility of cloud infrastructure, the scalability of infrastructure needs toimplementing be scaled horizontally, this i.e., it is probably necessary to add and remove assetsCan proposingsend more similar abilitiesalgorithm, as and the this existing is a ones. InWeighted such circumstances, Every server a loadis used, balancer is commonlyrequests used. to more In order tomajor balance drawback the physical and also labor, Static Round Robin in turn, by weight. capable and loaded requires estimating IP servers networks with different packet sizes, which are difficult to do. The source IP address shall be hashed and Users connect to a still Internet Service Provider divided by the total active session after (ISP) provides dynamic IP Source Hash number of servers Static disconnection and addresses, so it is difficult to operating to decide reconnection. It will maintain them. which server the increase performance. request receives. Simple performing virtual Consider both the machines are used, then the server’s capacity, Least The request is given to unequal route of traffic response time and the Response the server with the Dynamic might be shown and this number of current Time lowest response time. algorithm is not connections to avoid recommended for cookie- overload and crash. based session applications. Can send more It requires approximate Every server is used, requests to more network bandwidth, which Least in turn, by network Static capable and network is difficult to do in networks Bandwidth bandwidth. bandwidth loaded where the packet size of the servers. data varying, and network

Sensors 2020, 20, 7342 8 of 26 the load balancer automatically able to allocate the load to the recently added servers. Authors show how to renovate the widespread Apache Web Server [9], which is acts as a static load balancer into a cloud-ready dynamic load balancer [10,11]. In cloud load balancing, allocating computing resources is a vital key. Requests should be assigned to avoid the overload of virtual machines and improve the average response time of the audience. It is seen that during hours when request occurrence is high, active VM load balancer (bundled in cloud Analyst) [12] over-allocates initial virtual machines and under-allocates the next ones forming load imbalance. This author suggests a novel [13] VM load balancing algorithm that confirms even the allocation of requests to virtual machines even during hours when requests received in the datacenter are high to ensure quicker response times to the audience. The analysis results propose that the algorithm allocates requests to VM uniformly even during uttermost traffic conditions. In recent years, cloud computing has led to enormous progress in traffic volume and the number of services demands from cloud servers. This progress leaning of load stances can pose some challenges to the cloud load balancer in the resourceful balancing of the load, which is already an intimidating job. Cloud load balancing is an extremely explored field where plentiful solutions to balance load have been suggested. But no papers provided a wide-ranging review focusing on load balancer as a service model [14]. The authors understand the theories of load balancing, its significance and desired features in the cloud and provide a wide-ranging review of the present load balancing approaches, strengths, and deficiencies. They present a load balancer as a service model adopted by some major market cloud service providers. The rapid rise in the demand for methodical, commercial, and web applications has led to large scale computation [15]. Cloud computing has arisen as a reasonable, accessible, dependable, scalable, flexible source for web or other applications [16]. To manage web applications requires suitable load balancing and scheduling methods. The authors propose an algorithm based on-time scheduling and priority for load balancing. The method involves the partition of time into various portions and assigning each process to a specific time interval based on priority. The processor attends to the audience request inside the assigned time period. At the end of the time period, the next line up audience request is prepared for implementation. The audience request exits from the line upon accomplishment request; otherwise, the audience waits for its next time period. The rise in waiting time increases the time period the audience requests get in the VM and moderates the overhead of context substituting. Tasks scheduling algorithms [17] reflect the primary and elementary factors in monitoring cloud computing performance. In this, the authors try to improve scheduling algorithms by emphasizing the load balance to get the best asset consumption, decreasing waiting and executing time performance. They attempt to improve the scheduling algorithm’s performance by suggesting a new approach called “selecting virtual machine with the least load” that can be functional in aggregation with any assignment. Selecting virtual machine with the least load depends on computing the overall load in each VM without deliberation the figure of assignments allotted to it. For measuring the performance accomplished by this technique, it will be applied to conventional simple scheduling algorithms, such as first come first served, shortest job first, and max-min scheduling algorithms. In cloud load balancing, the static load balancing algorithms share the user request between virtual machines in a cloud datacenter for processing. But there are some problems that arise pertaining to the existing load of each virtual machine. The dynamic algorithms such as “efficient response time load balancer” and “mini time processing load balancer” demonstrate a solution to such problems [18,19]. The importance of these algorithms, before assigning a task, they search the allocation tables on the VM, response time, or the processing time. In this paper, the authors propose a new enhancement of the load balancing by the algorithm “estimated finish time load balancer” [18]. This algorithm takes account of the existing load of the VM of a cloud datacenter and estimates the processing completion time of a task before further any distribution, and try to overcome the glitches caused by static algorithms. The algorithm “estimated finish time load balancer” allows cloud service providers Sensors 2020, 20, 7342 9 of 26 to expand, develop and increase the performance, accessibility and maximize utilization of the use of virtual machines in datacenter. The author suggested a technique for analyzing the conditions and splits the load balancing methodology into multiple layers. The multi-queue management policy is used to form and split requests in multiple queues depending upon their execution priorities and the rest of the layer handles internal requests of the queue in the cloud network with the help of the network administrator [20]. Due to the handling of both segments, the problem of heavy load processing reduces in many terms like energy utilization, average response time, and network load. All these terms are used to analyze the performance of a VM along with a heavy load on the network. The planned metrics are designed on a .net platform and it attains a total 28% approximate improvement in all the cases. The cloud environment is a modern development in the IT sector field, which provides computing resources to the users’ on-demand basis. Cloud admin continuously monitors all the computer resources like RAM, vCPU, storage, network, etc., to maintain the load and usage of the resources, so they can get optimal performance of the resources. Ref. [21] did the load balancing in two ways, first non-linear algorithms, and second is scheduling tasks. Non-linear use is for internal load balancing, and second for allocation of computing resources for optimal performance. The experiment will be carried out at the CloudSim [12,22], simulator taking more span because of the parameter for evaluating the outcomes of one of kind algorithm. Cloud computing is an internet-based methodology where all the IT compute resources are deployed on a cloud with a different datacenter. The HBB load balancing isn’t assigned the task to the right virtual gadget and additionally, it does not recall the quality of service. In order to conquer the disadvantage of the honeybee algorithm, every other algorithm called particle swarm optimization set of rules is used. In the particle swarm optimization set of rules, the venture might be assigned to the virtual system appropriately. IE undertaking will test all of the virtual gadgets and assigns the task to the proper virtual device, which will have minimum memory leak has been considering in quality of services [23]. And the results of the paper suggest methodology can discover superior trade-off answers for assignment scheduling troubles that constitute the possible high-quality compromises the various conflicting objectives. The author discussed the new hybrid algorithm with the existing load balancing algorithms such as equally spread current execution load, round robin, bee colony optimization, and ant colony optimization [24]. The brand-new hybrid algorithm offers a green load balancing of nodes and performs green scheduling. The hybrid set of rules works on fundamental swarm smart algorithms and ant colony optimization and priority-based bee colony. The above algorithms for scheduling are carried out and cargo balanced within a cloud scenario where the overall performance of load balancing determines which virtual resource is loaded heavily or under loaded. The hybrid algorithm proves to outperform the prevailing processes like Round Robin, Equally Spread Current Execution and ant colony optimization. It highlights the challenges and features of cloud computing. It discusses the various offerings that cloud computing offers, service layers, virtualization, and load balancing. It introduces the procedures of load balancing static and dynamic, declaring that static load balancing algorithms divide the site visitors equivalently among all servers at the same time as dynamic load balancing is a non-centralized approach. The paper additionally discusses the prevailing load balancing algorithms together with round robin, opportunistic load balancing, randomized algorithm, min-min algorithm, max-min algorithm, active clustering, lock-loose multiprocessing answer for load balancer, ant colony optimization, shortest response time first, based random sampling, active clustering, and honey bee foraging behavior [25]. Reference [26] investigates the different techniques of load balancing algorithms to find the solution for the issues associated with load balancing and venture scheduling in cloud computing. The paper contrasts the static and dynamic load balancing algorithms, pointing out that the static algorithms in overhead phrases are very efficient because they do not need to screen the assets through Sensors 2020, 20, 7342 10 of 26 run-time. Therefore, they might work properly in stable surroundings as their operational residences will not exchange over time and masses are normally regular and unique. On the alternative hand, the dynamic algorithms give higher solutions in order that the properties of the resources at run time can be adjusted dynamically at run-time. In cloud computing, managing and allocating the responsibilities are the major and most difficult tasks. It requires the ideal deployment of computing resources and also monitoring the overload of the resources. This article uses the ant colony optimization [27] load balancing technique for the route the incoming traffic load dynamically. They describe the two policies first max-min and the second ahead-backward ant tool. The ahead-backward ant tool always checks the neighboring resources for a quick and optimal load transfer process. This paper also shows that pre-planned requirements cannot route the optimal performance of dynamic load balancer for the cloud with much less time. However, it also can also bring network routine below average and severely loaded environments. Cloud computing has developed one of the most noteworthy services for its power in every part of networking and a new variety of technologies such as security, volume, quality of service, charge, and availability, etc. The major challenge in the cloud network is load balancing. Multiple algorithms were proposed to solve the issues. Some measured fixed variables, while others measured through dynamic variables. In this paper, fixed variables procedures are used with the fresh projected algorithm “MEMA technique” [28]. In the projected algorithm, a few phases are additional to the weighted round robin. Moreover, a comparative study is an analysis between the weighted round robin and MEMA technique is obtainable. After the evolution of virtualization and cloud infrastructure, cloud computing offers the pay-as-you-go model. It supports cloud computing infrastructure by enlightening the overall performance, enhanced computing assets deployment, power consumption administration, increasing the cloud facilities’ quality of service. In [29,30] the authors study the requirements of assets, design, and web application architecture. They implement the load balancer using general, architectural, and artificial intelligence algorithms according to architecture and requirements. In the end, they evaluate the algorithms and enumerate the positive and negative aspects of the load balancing algorithm. In this paper, we investigate the load balancer algorithms round robin and least connection algorithm with the help of live cloud infrastructure, which was deployed on the DigitalOcean cloud. We have used open-source services like PHP for a dynamic web application, HAProxy for implementing load balancer algorithms, MariaDB for database, Cloud Firewall for securing the webservers and database server, and Apache Jmeter for testing and analyzing the load on the website [31]. Meanwhile, in the other papers, most authors use the cloud analyst simulation tool to analyze their load balancing algorithms [2–5]. The Cloud Analyst simulation tool is a pre-defined simulation tool for analyzing algorithms. The use of the cloud analyst simulation tool is now a very important technique for analyzing the load test. We use the quality-of-service parameters to compare the performance and analysis of the algorithms with their overall execution time of each algorithm in milliseconds, average response time, throughput, network bandwidth, and other parameters. The comparison of the works of different authors is summarized in Table2. Sensors 2020, 20, 7342 11 of 26

Table 2. Comparison of Related Works.

Author (Year) [Reference] Objective Method/Technique/Tool Analysis Adding few steps in weighted Fixed variables Reduce response time for round robin (wrr) improves the Manaseer (2019) [29] algorithm “MEMA vital request distribution of traffic through Technique” servers Scheduling algorithms Selecting Virtual Get the best assets consumption, for monitoring and Tahani Aladwani. (2017) [17] Machine with the least decreasing waiting and executing improve cloud load time performance. computing performance. Analyses the conditions Reduces energy utilization, and splits the load average response time, and M. Al-Ayyoub (2016) [20] Multi-agent framework balancing methodology network load. An approximate into multiple layers. 28% improvement showed. Ahead-backward a tool Uses Ant Colony Optimization to to find nearest resources Ant Colony Ren Gao (2015) [28] route the incoming traffic load for a quick and optimal Optimization dynamically. load transfer. Focus on load balancer Present load balancer as a service as a business model and Load Balancer as a M. Rahman (2014) [14] model, and adopt the best service importance in a cloud Service Model for optimal performance. environment Increase the performance, Try to overcome the Estimated finish time accessibility and maximize Y. Fahim (2014) [18] glitches caused by static load balancer utilization of the use of virtual algorithms. machines Reduce the dynamic load MapReduce is performed in imbalance in a MapReduce parallel over the servers and H.C. Hsiao (2013) [27] distributed file system in programming paradigm improve the performance and a cloud environment reduce the imbalance of load.

4. Methodology This section shows a comparative study of two of the most commonly used load balancing algorithms. We perform an analysis on the round robin and least connections algorithms. The cloud environment in this paper contains the load balancer server and several target web servers. The virtual users send requests to the load balancer server, which then forwards the request to the target web servers following a standing procedure. The target web servers are supposed to be organized in a parallel topology, which means that once the server ﬁnishes processing the requests, it will return the result straight to the virtual user. We used the HAProxy service to implement the algorithms and the JMeter tool for analyzing the results of the algorithms. In the following subcategories, we deﬁne the commonly used cloud simulation tool for introduction purpose only and appropriate comparative research works that apply load balancing algorithms from the cloud sources or cloud audience perceptions.

4.1. HAProxy High Availability Proxy is a free or open-source load balancer, which can load balance very fast, and consistent solutions provide high accessibility, load balancing and a proxy for TCP and HTTP/HTTPS-created web applications. In recent times, with an increase in user traffic, HAProxy is the highly demanding load balancer that easily handles a larger number of audiences. It became the most regular open-source load balancer and distributed with the majority of Linux distributions like RedHat, CentOS, Ubuntu, Fedora, etc. It is also installed by default in cloud virtual machines. Each load balancer server has its own public IP address but shares the same fully qualified domain name. HAProxy [32] backend configuration file is responsible for the load balancing algorithm. HAProxy has nine algorithms, but in this paper, we are testing the two major load balancer algorithms widely used by cloud architecture and cloud service providers. Sensors 2020, 20, 7342 12 of 26

4.2. Round Robin Algorithm The ﬁrst and simplest algorithm is Round Robin. We can go on by entering “balance round robin” in the backend section ﬁle of HAProxy. With this selection, HAProxy will forward the request over the servers and evenly load your “cluster”. The code example:

backend

balance roundrobin server server1 webserver01:80 server server2 webserver02:80 server server3 webserver02:80

In this case, webserver01, webserver02 and webserver03 are the hostname of the servers, we can enter IP addresses also instead of hostname.

4.3. Least Connections Algorithm This load balancer algorithm called “least connections” calculates the number of lively connections for each server. Thus, each following request is distributed to the server with the lowermost number of active connections. We can turn this algorithm on by entering “balance leastconn” in the backend section ﬁle of HAProxy. Least Connections is dynamic in nature, which means that server loads may be adjusted on the gentle starts for nodes:

backend

balance leastconn server server1 lcserver01:80 server server2 lcserver02:80 server server3 lcserver02:80

In this case, lcserver01, lcserver02, and lcserver03 are the hostnames of the servers; we can enter IP addresses also instead of hostname.

4.4. Apache Jmeter Apache JMeter is a free or open-source, pure Java-based, performance, stress and load testing tool that can be used to analyze the eﬃcient behavior of organizations and calculate the performance of web applications under various load tests. A load test will simulate end-user behavior that methodology the bounds of a web application’s conditions. This tool can be used to design varying or heavy loads on a single or cluster of servers, networks, and concurrent users to test web applications or organizational structure. The work ﬂow chart of the apache jmeter is shown in Figure7. Apache JMeter includes various applications, server/protocols:

Web—HTTP, HTTPS (Java, NodeJS, PHP, ASP.NET) • SOAP/REST Web services • FTP • Database via JDBC • LDAP • Message-oriented middleware (MOM) via JMS • Mail Services like SMTP, POP3 and IMAP • Native commands or shell scripts • TCP • Java Objects • Sensors 2020, 20, x FOR PEER REVIEW 13 of 26

• SOAP/REST Web services • FTP • Database via JDBC • LDAP • Message-oriented middleware (MOM) via JMS • Mail Services like SMTP, POP3 and IMAP • Native commands or shell scripts • TCP Sensors 2020, 20, 7342 13 of 26 • Java Objects

Figure 7. Workflow diagram of Apache Jmeter. Figure 7. Workflow diagram of Apache Jmeter. 4.5. Cloud Analyst Simulation Tool 4.5. Cloud Analyst Simulation Tool Cloud Analyst is a java-based framework designed at the University of Melbourne to support the regionalCloud distribution Analyst ofis usersa java-based and datacentres framework to analyze designed the cloudat the infrastructure,University of resources,Melbourne and to networksupport tools.the regional This simulation distribution tool of defines users and and datacentres creates user to groups, analyze physical the cloud hardware infrastructure, details resources, and generates and highnetwork traffi tools.c for This testing simulation the cloud tool infrastructure. defines and Itcreates offers user a virtualization groups, physical technology hardware with details complete and modelsgenerates for high virtual traffic infrastructure for testing to the be cloud created infrastruc and operatedture. inIt offers a datacentre. a virtualization This tool providestechnology an with easy tocomplete use graphical models user for interface.virtual infrastructure The table, diagram to be created and explanation and operated of the in potentially a datacentre. high This numbers tool ofprovides statistics an earned easy to duringuse graphical simulation user isinterface. given for The graphical table, diagram results. and This explanation effective presentation of the potentially helps identifyhigh numbers the respective of statistics patterns earned in performance during simulati parameterson is given and facilitatesfor graphical associations results. betweenThis effective them. presentation helps identify the respective patterns in performance parameters and facilitates 4.6.associations Experimental between Setup them. In order to implement the suggested round robin and least connections algorithm and compare both4.6. Experimental algorithms’ Setup effectiveness and performance, the HAProxy load balancer has been configured and utilized.In order Apacheto implement JMeter the is usedsuggested to analyze round the robi resultsn and ofleast both connections algorithms. algorithm This section and providescompare informationboth algorithms’ about effectiveness the cloud service and performance, provider, virtual the HAProxy machines, load resources, balancer has cost been and configured other software and usedutilized. for loadApache testing JMeter setup. is used It also to explainsanalyze thethe designresults ofofthe both HAProxy algorithms. load This balancer section and provides Apache JMeterinformation and the about necessary the cloud modules service involved, provider, followed virtual bymachines, the implementation resources, cost of theand least other connection software algorithmused for load and testing round robinsetup. algorithm. It also explains the design of the HAProxy load balancer and Apache JMeter and the necessary modules involved, followed by the implementation of the least connection 4.6.1.algorithm Virtual and Machines round robin Setup algorithm. and Software DigitalOcean Inc. [33] is an American cloud infrastructure company. Its headquarters are situated4.6.1. Virtual in New Machines York CitySetup and and datacenters Software are present worldwide. DigitalOcean offers creators cloud facilities that help set up and scale applications that run concurrently on several workstations. Since January 2018, DigitalOcean was the third-largest cloud hosting provider in the world in positions of web-facing workstations. We designed simple cloud architecture for the comparative and analysis of different load balancer algorithms. we used a total of ten virtual machines for the cloud architecture implemented in DigitalOcean. Five virtual machines are used for the RR algorithm and the remaining five virtual machines are used for LC algorithm, as shown in Table3 The following are the configuration of virtual machines, which was used in the demonstration. Sensors 2020, 20, 7342 14 of 26

Table 3. Conﬁguration of virtual machines used in cloud architecture for analysis of load balancer Algorithm.

S. No. Name vCPU Memory Storage Qty Cost 1 Web Servers for Round Robin 1 Core 2 GB 50 GB SSD 3 $10/month 2 Database Server for Round Robin 1 Core 1 GB 25 GB SSD 1 $5/month Web Servers for Least 3 1 Core 2 GB 50 GB SSD 3 $10/month Connections Database Server for Least 4 1 Core 1 GB 25 GB SSD 1 $5/month Connections Load Balancer Virtual Machine 5 1 Core 2 GB 50GB SSD 1 $10/month for Round Robin Load Balancer Virtual Machine 6 1 Core 2 GB 50GB SSD 1 $10/month for Least Connections

4.6.2. Primary Setup of Virtual Machines The important sections and services are set up in all virtual machines to use for demonstration and each service is explained in detail below.

Datacentre Regions This section is used to specify the terrestrial sites of the cloud architecture where the resources are allotted. In general, DigitalOcean provides eight datacenter regions of different continents in total. The datacenter provides the infrastructure to the cloud architecture design by audience/users or cloud design. Each datacenter consists of a number of virtual machines. All the resources: Ram, storage, IP Address, firewall, core, etc. and virtual machines have their own specifications.

Apache Server The Apache HTTP Server or Apache is an open-source web server software program, launched under Apache License 2.0. Apache has been evolved and maintained with the aid of public builders under the sponsorships of the Apache software program foundation [34]. The large majority of Apache net Server nodes run on a Linux operating system; however, present variations also run on Microsoft home windows Server and a varied variety of Unix-like operating systems. Apache permits customers to serve content like textual content, photographs, audio and video, and so forth on the internet- therefore the name “webserver”.

PHP PHP Hypertext Pre-processor is a server-side programming language and a dominant program for designing the making of dynamic and interactive web applications and pages. PHP is the commonly used, free, and well-organized scripting language and it is an alternative to challengers such as Microsoft’s ASP. PHP 7 is the newest stable release.

MariaDB MariaDB is a community-advanced, commercially supported department of the MySQL Relational Database Management System (RDBMS), expected to stay unfastened and open-source software beneath the GNU General Public License. Improvement is led by a number of the unique programmers of MySQL who left it because of concerns over its getting hold of by way of Oracle organization in 2009.

Cloud Firewall Firewalls place a barricade between servers and other digital devices or other devices on the network to guard them against exterior malicious traffic like viruses, DDOS attacks, and hackers. Firewalls can be host-based, which are constructed on a per-server source using daemons like IPTables or UFW. Cloud firewalls are network-based and halt traffic at the network layer before it reaches the server. Sensors 2020, 20, x FOR PEER REVIEW 15 of 26 commonly used, free, and well-organized scripting language and it is an alternative to challengers such as Microsoft’s ASP. PHP 7 is the newest stable release.

Cloud Firewall Firewalls place a barricade between servers and other digital devices or other devices on the network to guard them against exterior malicious traffic like viruses, DDOS attacks, and hackers. Firewalls can be host-based, which are constructed on a per-server source using daemons like IPTables or UFW. Cloud firewalls are network-based and halt traffic at the network layer before it reaches the server.

4.6.3. Implementation of the Round Robin Algorithm In this subsection, we described step by step implementation of the round robin algorithm, and the figure described the basic model of round robin how the requests are served in the backend shown in Figure 8. The implementation steps of Round Robin Algorithm is given below. Sensors1. The2020 first, 20, 7342request of users comes to HAPrxoy Load Balancer, as shown in Figure 8. 15 of 26 2. HAProxy Load Balancer selects which VM should get incoming requests. 4.6.3.3. The Implementation first request of of the the user Round is assigned Robin Algorithm to any random VM. 4. Once the first request is assigned, virtual machines are ordered in a cyclic manner. 5. VirtualIn this subsection, machine which we described received step the byfirst step user implementation request is moved of the back round to all robin virtual algorithm, machines. and the ﬁgure6. The described next request the basic of users model is assigned of round robinto the hownext theVM requests in cyclic are order. served in the backend shown in Figure7. Go8. to The Step implementation 3 for each user steps request of Round until Load Robin Balancer Algorithm processes is given all below. requests.

FigureFigure 8.8.Model Model ofof thethe roundround robinrobin algorithm.algorithm.

1.4.6.4. The Implementation first request of of users the Least comes Connections to HAPrxoy Algorithm Load Balancer, as shown in Figure8. 2. HAProxy Load Balancer selects which VM should get incoming requests. This subsection described step by step implementation of the least connections algorithm shown 3. The first request of the user is assigned to any random VM. below. The figure described the basic model of least connections how the requests are served by the 4.least Once connections the first requestin the isbackend assigned, shown virtual in machines Figure 9. are The ordered implementation in a cyclic manner. steps of the Least 5.Connections Virtual machine Algorithm which is given received below. the first user request is moved back to all virtual machines. 6. The next request of users is assigned to the next VM in cyclic order. 1) The first request of users comes to HAPrxoy Load Balancer, as shown in Figure 8. 7.Sensors Go 2020 to, Step20, x FOR 3 for PEER each REVIEW user request until Load Balancer processes all requests. 16 of 26 2) HAProxy Load Balancer selects which VM should get incoming requests. 4.6.4.3)4) The ImplementationOnce first the request first requestof the user Least is isassigned, Connections assigned virtual to Algorithmany randommachines virtual are orderedmachine. in the least amount of connections that have the least amount of requests, then assign the new request to that VM. 5) ThisVM, subsection which received described the first step user by step request, implementation is moved back of theto all least virtual connections machines algorithm after completing shown below.all The the figure user requests. described the basic model of least connections how the requests are served by the least6) connectionsThe next request in the backendof users is shown assigned in Figure to the9. next The implementationVM in the least number steps of theof connections. Least Connections Algorithm7) Go to is Step given 3 for below. each user’s request until load balancer processes all requests.

FigureFigure 9. 9.Model Model of of the the least least connections connections algorithm. algorithm.

How to configure the round robin and least connections in the algorithm in the HAProxy configuration file are mentioned in Figure 10.

Figure 10. The configuration code of the RR and LC algorithms.

5. Results & Evaluation This section features a detailed discussion about the setup of cases for performing load testing on load balancer servers and explains the results of each case. The quality of service parameters used to compare the performance and analysis of the suggested algorithms with their overall execution time of each algorithm in milliseconds, response time, average execution time, 90% percentile, 95%

Sensors 2020, 20, x FOR PEER REVIEW 16 of 26

4) Once the first request is assigned, virtual machines are ordered in the least amount of connections that have the least amount of requests, then assign the new request to that VM. 5) VM, which received the first user request, is moved back to all virtual machines after completing all the user requests. 6) The next request of users is assigned to the next VM in the least number of connections. 7) Go to Step 3 for each user’s request until load balancer processes all requests.

Sensors 2020, 20, 7342 16 of 26

(1) The first request of users comes to HAPrxoy Load Balancer, as shown in Figure8. (2) HAProxy Load Balancer selects which VM should get incoming requests. (3) The first request of the user is assigned to any random virtual machine. (4) Once the first request is assigned, virtual machines are ordered in the least amount of connections that have the least amount of requests, then assign the new request to that VM. (5) VM, which received the first user request, is moved back to all virtual machines after completing all the user requests.

(6) The next request of users is assigned to the next VM in the least number of connections. (7) Go to Step 3 for each user’sFigure request9. Model until of the load least balancer connections processes algorithm. all requests.

How to to configure configure the the round round robin robin and and least least connections connections in the in thealgorithm algorithm in the in HAProxy the HAProxy configurationconfiguration file file are mentioned in Figure 1010..

Figure 10. TheThe configuration conﬁguration code of the RR and LC algorithms. 5. Results & Evaluation 5. Results & Evaluation This section features a detailed discussion about the setup of cases for performing load testing on This section features a detailed discussion about the setup of cases for performing load testing load balancer servers and explains the results of each case. The quality of service parameters used to on load balancer servers and explains the results of each case. The quality of service parameters used compare the performance and analysis of the suggested algorithms with their overall execution time of to compare the performance and analysis of the suggested algorithms with their overall execution each algorithm in milliseconds, response time, average execution time, 90% percentile, 95% percentile, time of each algorithm in milliseconds, response time, average execution time, 90% percentile, 95% 99th percentile throughput, network bandwidth, active threads over time, latency over time and more. In this task, we set up cloud architecture on DigitalOcean and host one dynamic website to test RR and LC algorithms, as shown in Figure 11. This experimental result focuses on diﬀerent cases that were designed for scheduling incoming requests.

5.1. First Load Test We perform our test with 1500 samples (samples means users), 1500 samples are divided into three pages and each page serves 500 samplers and every 20 s, 25 samples are hitting the server. After completing all samplers’ requests, we generate tabular reports as well as graphs. Performance analysis with diﬀerent parameters on two algorithms and their statistical results and graphs obtained from the ﬁrst load test are shown in Table4 and in Figures 12 and 13. Sensors 2020, 20, x FOR PEER REVIEW 17 of 26

percentile, 99th percentile throughput, network bandwidth, active threads over time, latency over time and more. In this task, we set up cloud architecture on DigitalOcean and host one dynamic website to test Sensors 2020, 20, 7342 17 of 26 RR and LC algorithms, as shown in Figure 11. This experimental result focuses on different cases that were designed for scheduling incoming requests.

SensorsSensors 2020 2020, ,20 20, ,x x FOR FOR PEER PEER REVIEW REVIEW 1818 ofof 2626 FigureFigure 11. 11.Cloud Cloud architecture architecture implemen implementationtation on onDigitalOcean. DigitalOcean.

5.1. First Load Test We perform our test with 1500 samples (samples means users), 1500 samples are divided into three pages and each page serves 500 samplers and every 20 s, 25 samples are hitting the server. After completing all samplers’ requests, we generate tabular reports as well as graphs. Performance analysis with different parameters on two algorithms and their statistical results and graphs obtained from the first load test are shown in Table 4 and in Figures 12 and 13.

Table 4. Statistics table of the RR algorithm and LC algorithm for the first load test.

# Labe Aver Medi 90% 95% 99% Mi M Error Throug Received Sent Sampl l. age an Line Line Line n ax % hput KB/sec KB/sec es RR 11 24 0.00 Hom 500 305 232 519 729 1239 19.69047 499.93 2.38 9 18 % e RR 22 0.00 Term 500 215 150 441 606 1013 77 19.9984 466.76 2.62 70 % s FigureRR 12.FigureFigureAggregate 12.12. AggregateAggregate graph graphgraph using usingusing Table TableTable3 data 33 data data that thatthat showsshshowsows averageaverage average responseresponse response time,time, time, median,median, median, 90%,90%, 95%,95%, 90%, 95%, 12 0.00 Cont99%,99%, 500 min,min, max,max,190 andand throughputthroughput137 344 parameter parameter531 ofof combinedcombined748 76 graphgraph ofof thethe RRRR and19.98082and LCLC algorithms.algorithms.431.23 2.52 99%, min, max, and throughput parameter of combined graph81 of% the RR and LC algorithms. act TOT 24 0.00 1500 236 181 438 615 1191 76 58.53202 1371.83 7.37 AL 18 % LC 12 15 0.00 Hom 500 340 246 616 896 1300 19.49774 495.04 2.3 9 19 % e LC 20 0.00 Term 500 245 172 493 608 1153 80 19.47268 454.49 2.49 38 % s LC 16 0.00 Cont 500 229 155 453 565 1141 81 19.42351 419.2 2.39 01 % act TOT 20 0.00 1500 271 208 525 672 1255 80 57.03205 1336.67 7.02 AL 38 %

Figure 13 Throughput or transactions per second of the RR and LC algorithms. FigureFigure 13. Throughput 13 Throughput or or transactions transactions perper second second of of the the RR RR and and LC algorithms. LC algorithms. 5.2. Second5.2.5.2. Second LoadSecond Test LoadLoad TestTest InIn thethe secondsecond case,case, wewe performperform aa testtest withwith 30003000 samples,samples, divideddivided intointo threethree pagespages andand eacheach pagepage In the second case, we perform a test with 3000 samples, divided into three pages and each page servesserves 10001000 samplerssamplers andand everyevery 4040 s,s, 2525 samplessamples araree hittinghitting thethe server.server. AfterAfter completingcompleting allall samplers’samplers’ serves 1000requests,requests, samplers wewe generategenerate and every tabulartabular 40 s,rere 25portsports samples asas wellwell are asas hitting graphs.graphs. the PerformancePerformance server. After analysisanalysis completing withwith differentdifferent all samplers’ requests,parametersparameters we generate onon thethe tabular twotwo algorithmsalgorithms reports as andand well theirtheir as statis graphs.statisticaltical Performanceresultsresults andand graphsgraphs analysis obtainedobtained with fromfrom diﬀ theerentthe secondsecond parameters on the twoloadload algorithms testtest areare shownshown and inin TableTable their 5,5, statistical FiguresFigures 1414 results andand 1515 below.below. and graphs obtained from the second load test are shown in Table 5, Figures 14 and 15 below.

Sensors 2020, 20, 7342 18 of 26

Table 4. Statistics table of the RR algorithm and LC algorithm for the ﬁrst load test.

Label # Samples Average Median 90% Line 95% Line 99% Line Min Max Error % Throughput Received KB/s Sent KB/s RR 500 305 232 519 729 1239 119 2418 0.00% 19.69047 499.93 2.38 Home RR 500 215 150 441 606 1013 77 2270 0.00% 19.9984 466.76 2.62 Terms RR 500 190 137 344 531 748 76 1281 0.00% 19.98082 431.23 2.52 Contact TOTAL 1500 236 181 438 615 1191 76 2418 0.00% 58.53202 1371.83 7.37 LC 500 340 246 616 896 1300 129 1519 0.00% 19.49774 495.04 2.3 Home LC 500 245 172 493 608 1153 80 2038 0.00% 19.47268 454.49 2.49 Terms LC 500 229 155 453 565 1141 81 1601 0.00% 19.42351 419.2 2.39 Contact TOTAL 1500 271 208 525 672 1255 80 2038 0.00% 57.03205 1336.67 7.02

Table 5. Statistics table of the round robin algorithm and least connection algorithm of the second load test.

Received Label # Samples Average Median 90% Line 95% Line 99% Line Min Max Error % Throughput Sent KB/s KB/s RR About Us 1000 428 284 810 1233 2254 136 6512 0.00% 36.70264 727.17 4.87 RR Car 1000 326 213 617 816 1246 85 5748 0.00% 36.63004 970.62 4.65 Listing RR Terms 1000 330 238 631 786 1439 83 4454 0.00% 36.5992 854.22 4.79 TOTAL 3000 361 245 668 987 1594 83 6512 0.00% 106.37166 2469.61 13.85 LC About Us 1000 627 367 1270 1594 3100 185 8492 0.00% 34.93938 692.24 4.54 LC Car 1000 538 383 1052 1351 2491 119 4549 0.00% 35.44465 939.21 4.4 Listing LC Terms 1000 604 450 1196 1531 2878 112 5001 0.00% 35.04223 817.88 4.48 TOTAL 3000 590 387 1212 1520 2833 112 8492 0.00% 102.06165 2369.54 12.99 Sensors 2020, 20, x FOR PEER REVIEW 19 of 26

Table 5. Statistics table of the round robin algorithm and least connection algorithm of the second load test.

# Aver Med 90% 95% 99% M M Erro Throug Received Sent Label Sampl age ian Line Line Line in ax r % hput KB/sec KB/sec es RR 13 65 0.00 36.7026 1000 428 284 810 1233 2254 727.17 4.87 About Us 6 12 % 4

RR Car 57 0.00 36.6300 1000 326 213 617 816 1246 85 970.62 4.65 Listing 48 % 4

RR 44 0.00 1000 330 238 631 786 1439 83 36.5992 854.22 4.79 Terms 54 %

65 0.00 106.371 TOTAL 3000 361 245 668 987 1594 83 2469.61 13.85 12 % 66

LC 18 84 0.00 34.9393 1000 627 367 1270 1594 3100 692.24 4.54 About Us 5 92 % 8

LC Car 11 45 0.00 35.4446 1000 538 383 1052 1351 2491 939.21 4.4 Listing 9 49 % 5

LC 11 50 0.00 35.0422 1000 604 450 1196 1531 2878 817.88 4.48 Terms 2 01 % 3

Sensors 2020, 20, 7342 11 84 0.00 102.061 19 of 26 TOTAL 3000 590 387 1212 1520 2833 2369.54 12.99 2 92 % 65

Figure 14. Aggregate graph using Table 4 data, showing the average response time, median, 90%, Figure 14. Aggregate graph using Table4 data, showing the average response time, median, 90%, 95%, 95%, 99%, min, max, and throughput parameter of combined graph of the RR and LC algorithms. Sensors99%, 2020 min,, 20, x max, FOR andPEER throughput REVIEW parameter of combined graph of the RR and LC algorithms. 20 of 26

Figure 15. Throughput or transactions per second of the RR and LCLC algorithms.algorithms.

5.3. Third Load Test InIn thethe third third case, case, we we test test 4500 4500 samples samples divided divided into three into pagesthree andpages each and page each serves page 1500 serves samplers 1500 andsamplers all samplers’ and all samplers’ hitting the hitting server the in theserver span in timethe span of 1 min.time Afterof 1 min. completing After completing all samplers’ all samplers’ requests, werequests, generate we tabular generate reports tabular as well reports as graphs. as well Performance as graphs. analysis Performance with diﬀ erentanalysis parameters with different on two algorithmsparameters andon two their algorithms statistical and results their and statistical graphs results obtained and from graphs the obtained second load from test the are second shown load in Tabletest are6, Figuresshown in16 Tableand 17 6,. Figures 16 and 17.

Table 6. Statistics table of the round robin algorithm and least connection algorithm of the third load test.

# Aver Med 90% 95% 99% M Ma Erro Throug Received Sent Label Samp age ian Line Line Line in x r % hput KB/sec KB/sec les RR-Vechile- 12 513 0.00 54.5454 1500 470 315 933 1299 1762 1391.02 7.56 Detail-01 3 6 % 5 RR-Vechile- 10 481 0.00 52.9885 1500 423 275 817 1164 2269 1391.05 7.35 Detail-02 3 0 % 5 RR-Vechile- 508 0.00 52.9960 1500 463 306 901 1214 2377 85 1335.3 7.35 Detail-03 1 % 4 513 0.00 156.217 TOTAL 4500 452 309 884 1246 2246 85 4006.99 21.66 6 % 5 LC-Vehicle- 15 180 0.00 49.6869 1500 790 435 1495 2224 4379 1267.11 6.74 Detail-01 7 01 % 7 LC-Vehicle- 12 180 0.00 47.7874 1500 726 442 1336 1904 5031 1254.51 6.49 Detail-02 7 46 % 4 LC-Vehicle- 11 167 0.00 45.9474 1500 769 531 1480 2211 4561 1157.7 6.24 Detail-03 9 27 % 4 11 180 0.00 132.131 TOTAL 4500 762 475 1439 2096 4569 3389.18 17.94 9 46 % 4

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Table 6. Statistics table of the round robin algorithm and least connection algorithm of the third load test.

Received Label # Samples Average Median 90% Line 95% Line 99% Line Min Max Error % Throughput Sent KB/s KB/s RR-Vechile-Detail-01 1500 470 315 933 1299 1762 123 5136 0.00% 54.54545 1391.02 7.56 RR-Vechile-Detail-02 1500 423 275 817 1164 2269 103 4810 0.00% 52.98855 1391.05 7.35 RR-Vechile-Detail-03 1500 463 306 901 1214 2377 85 5081 0.00% 52.99604 1335.3 7.35 TOTAL 4500 452 309 884 1246 2246 85 5136 0.00% 156.2175 4006.99 21.66 LC-Vehicle-Detail-01 1500 790 435 1495 2224 4379 157 18001 0.00% 49.68697 1267.11 6.74 LC-Vehicle-Detail-02 1500 726 442 1336 1904 5031 127 18046 0.00% 47.78744 1254.51 6.49 LC-Vehicle-Detail-03 1500 769 531 1480 2211 4561 119 16727 0.00% 45.94744 1157.7 6.24 TOTAL 4500 762 475 1439 2096 4569 119 18046 0.00% 132.1314 3389.18 17.94 Sensors 2020, 20, 7342 21 of 26 Sensors 2020,, 20,, xx FORFOR PEERPEER REVIEWREVIEW 21 of 26

Figure 16. Aggregate graph using Table 5 data. It shows the average response time, median, 90%, FigureFigure 16.16. AggregateAggregate graph graph using using Table Table5 data. 5 data. It shows It show thes averagethe average response response time, time, median, median, 90%, 95%,90%, 95%, 99%, min & max parameters of the combined graph of the RR and LC algorithms of Table 4. 99%,95%, min99%, & min max & parameters max parameters of the of combined the combined graph gr ofaph the of RR the and RR LC and algorithms LC algorithms of Table of4 Table. 4.

Figure 17. Throughput or transaction per second of the RR and LC algorithms. Figure 17. Throughput or transaction per second of the RR and LC algorithms. 5.4. Fourth Load Test 5.4. Fourth Load Test In the fourth case, we perform the test with 6000 samples divided into three pages and each pageIn serves the fourth 2000 samplers case, we perform and all samplers’ the test with hitting 6000 the samples server divided at the same into time.three Afterpages completingand each page all samplers’serves 2000 requests, samplers we generateand all samplers’ tabular reports hitting as th welle server as graphs. at the Performance same time. analysis After completing with diﬀerent all parameterssamplers’ requests, on the wo we algorithms generate ta andbular the reports statistical as well results as graphs. and graphs Performance obtained analysis from the with second different load testparameters are shown on inthe Table wo algorithms7, Figures 18 and and the 19 statistical. results and graphs obtained from the second load test are shown in Table 7, Figures 18 and 19.

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Table 7. Statistics table of the round robin and least connections algorithms for the fourth load test.

Received Label # Samples Average Median 90% Line 95% Line 99% Line Min Max Error % Throughput Sent KB/s KB/s RR-Search 2000 136 140 185 209 249 81 549 0.00% 65.12113 1446.27 8.59 RR-Privacy 2000 103 97 123 136 170 74 422 0.00% 65.30612 1295.92 8.67 RR-FAQ 2000 103 96 123 138 169 76 383 0.00% 65.29973 1244.07 8.48 TOTAL 6000 114 100 154 175 233 74 549 0.00% 194.1496 3954.47 25.53 LC-Search 2000 200 167 276 388 689 84 2761 0.00% 61.50062 1365.87 7.93 LC-Privacy 2000 147 122 217 281 548 82 1116 0.00% 62.751 1245.22 8.15 LC-FAQ 2000 153 124 216 316 632 82 1244 0.00% 62.81012 1196.64 7.97 TOTAL 6000 167 136 255 336 618 82 2761 0.00% 182.9324 3726 23.52 Sensors 2020, 20, x FOR PEER REVIEW 22 of 26

Table 7. Statistics table of the round robin and least connections algorithms for the fourth load test.

# Aver Med 90% 95% 99% M M Error Throug Received Sent Label Sampl age ian Line Line Line in ax % hput KB/sec KB/sec es RR- 54 0.00 2000 136 140 185 209 249 81 65.12113 1446.27 8.59 Search 9 % RR- 42 0.00 2000 103 97 123 136 170 74 65.30612 1295.92 8.67 Privacy 2 % RR- 38 0.00 2000 103 96 123 138 169 76 65.29973 1244.07 8.48 FAQ 3 % TOTA 54 0.00 6000 114 100 154 175 233 74 194.1496 3954.47 25.53 L 9 % LC- 27 0.00 2000 200 167 276 388 689 84 61.50062 1365.87 7.93 Search 61 % LC- 11 0.00 2000 147 122 217 281 548 82 62.751 1245.22 8.15 Privacy 16 % LC- 12 0.00 2000 153 124 216 316 632 82 62.81012 1196.64 7.97 FAQ 44 % SensorsTOTA2020, 20, 7342 27 0.00 23 of 26 6000 167 136 255 336 618 82 182.9324 3726 23.52 L 61 %

Figure 18. Aggregate graphgraph usingusing Table Table6 data,6 data, showing showin theg the average average response response time, time, median, median, 90%, 95%,90%, Sensors95%,99%, 2020 99%, min, 20, &xmin FOR max & PEER parametermax REVIEW parameter of the of combined the combined graphs grap ofhs the of RR the and RR LC and algorithms LC algorithms of Table of 5Table. 5. 23 of 26

FigureFigure 19.19. ThroughputThroughput oror transactionstransactions perper secondsecond ofof thethe RRRR andand LCLC algorithms.algorithms.

AfterAfter performingperforming allall thethe samplesample loadload tests,tests, wewe foundfound thatthat thethe averageaverage responseresponse time,time, throughputthroughput time,time, receivedreceived KBKB/sec,/s, Sent Sent KB KB/sec/s results results show show that thethat roundthe round robin robin algorithm algorithm performs performs better better than than the leastthe least connections connections algorithm. algorithm. Tables Tables2–5 2–5 show show that th theat the average average response response times times of of the the round round robin robin algorithmalgorithm are 236, 236, 361, 361, 452 452 and and 114 114 milliseconds, milliseconds, respectively. respectively. In contrast, In contrast, the least the leastconnections connections times timesare 271, are 590, 271, 762 590, and 762 167 and milliseconds, 167 milliseconds, respectively respectively.. When comparing When comparing average averageresponse response time, we time,have wethus have found thus that found the round that therobin round algorithm robin takes algorithm less time takes to serve less timeall sample to serve requests. all sample For the requests. second Forparameter, the second the throughput parameter, thetimes throughput of the round times robi ofn algorithm the round are robin 58.53, algorithm 106.37, are156.21 58.53, and 106.37,194.14 156.21milliseconds, and 194.14 respectively, milliseconds, and for respectively, the least connecti and foron the algorithm least connection they are algorithm 57.03, 102.06, they 132.13 are 57.03, and 102.06,182.92 132.13milliseconds, and 182.92 so in milliseconds, the throughput so in result the throughputs, the round results, robin thealgorithm round robinalso performs algorithm better also performsbecause a better higher because throughput a higher value throughput is always valuebetter. is The always third better. parameter The thirdis network parameter bandwidth, is network and bandwidth,it includes received and it includes KB/sec, receivedand sent KBKB/sec./s, and The sent round KB/ s.robin The received round robin KB/sec received values KBare/s 1371.83, values 2469.61, 4006.99, 3954.47, and the sent KB/sec values are 7.37, 13.85, 21.66 and 25.53 KB/sec, while for the least connections algorithm, the receive KB/sec values are 1336.37, 2369.54, 3389.18 and 3726 KB/sec, and the sent KB/sec values are 7.02, 12.99, 17.94 and 23.52 KB/sec. This result also shows that the round robin algorithm network bandwidth utilization also performs better as compared to the least connections algorithm. We have also compared the statistics tables and graphs of the different cases, and the overall average response time of the round robin algorithm is low, and the throughput is high as compared to the least connections algorithm. The network bandwidth is also high in the the round robin algorithm. Therefore, the round robin algorithm performs better than the least connections algorithm. The summarized result analysis is shown in Table 8.

Table 8. The summarized comparison result of the round robin and least connections algorithms.

Average Response Time Throughput Received Sent (KB/sec) (in milliseconds) (in milliseconds) (KB/sec) Round Robin 290.75 128.815 2950.73 17.10 Least 447.5 118.54 2705.35 15.37 Connections Higher Value Lower Value (290.75) is Higher Value Higher Value Desired Value (2950.73) is better (128.815) is better (17.10) is better better Result Round Robin Round Robin Round Robin Round Robin

6. Conclusions

Sensors 2020, 20, 7342 24 of 26 are 1371.83, 2469.61, 4006.99, 3954.47, and the sent KB/s values are 7.37, 13.85, 21.66 and 25.53 KB/s, while for the least connections algorithm, the receive KB/s values are 1336.37, 2369.54, 3389.18 and 3726 KB/s, and the sent KB/s values are 7.02, 12.99, 17.94 and 23.52 KB/s. This result also shows that the round robin algorithm network bandwidth utilization also performs better as compared to the least connections algorithm. We have also compared the statistics tables and graphs of the diﬀerent cases, and the overall average response time of the round robin algorithm is low, and the throughput is high as compared to the least connections algorithm. The network bandwidth is also high in the the round robin algorithm. Therefore, the round robin algorithm performs better than the least connections algorithm. The summarized result analysis is shown in Table8.

Table 8. The summarized comparison result of the round robin and least connections algorithms.

Average Response Throughput Received Time Sent (KB/s) (in milliseconds) (KB/s) (in milliseconds) Round Robin 290.75 128.815 2950.73 17.10 Least Connections 447.5 118.54 2705.35 15.37 Lower Value Higher Value Higher Value Higher Value Desired Value (290.75) is better (128.815) is better (2950.73) is better (17.10) is better Result Round Robin Round Robin Round Robin Round Robin

6. Conclusions Cloud computing is widely used in industry. However, there are numerous standing issues like server migration, load balancing, power management, network management, virtual machine relocation, etc. In a cloud infrastructure, load balancing is a major task and a key challenge to distribute the incoming or dynamic traffic workload proficiently and rightfully to all the cloud datacenters to achieve audience satisfaction and optimal resource usage apportion. It is a huge challenge to choose an algorithm that reduces the overall average response time and it should be cost-effective too. In this paper, a comparison of the two round robin and least connections load balancing algorithms helps choose the best load balancing algorithm for any particular situation. The statistical results of the paper showed that if the average response time of the algorithm is low and throughput is high, that means the algorithm performs better on the provided cloud infrastructure. This research work can promote a superior algorithm by comparing and adding the different parameters like sticky sessions, redirection rules, cookie-based sessions, etc. As future work, we will extend the current work in multiple ways, like comparing existing load balancing algorithms such as weighted round robin, source hash, least response time, and least bandwidth. We can also use the load balancing algorithms on the database server where database transactions are high and compare the performance of the database server with or without a load balancing algorithm. In this manner, the objective is to balance the audience traffic of the cloud infrastructure while enhancing the execution, reducing the overall response time, increasing the throughput for a particular number of jobs, and proficiently handle resource usage.

Author Contributions: Conceptualization, B.A. and G.S.; methodology, H.K. and V.C.; software, B.A. and G.S. validation, R.V.; formal analysis, H.K.; investigation, V.C.; resources, B.A. data curation, H.K.; writing—original draft preparation, H.K.; writing—review and editing, H.K., G.S and V.C.; visualization, B.A.; supervision, B.A.; project administration, H.K.; funding acquisition, V.C., R.V. All authors have read and agreed to the published version of the manuscript. Funding: This research work was catalyzed and supported by the National Council for Science and Technology Communications (NCSTC), Department of Science and Technology (DST), Ministry of Science and Technology (Govt. of India), New Delhi, India [grant recipient: Dr. Harleen Kaur and grant No. 5753/IFD/2015-16]. This work is supported by VC Research (VCR 0000110) for Prof. Chang Conﬂicts of Interest: The authors declare no conﬂict of interest. Sensors 2020, 20, 7342 25 of 26

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