Dimensions for Evaluating Cloud Resource Orchestration Frameworks

COVER FEATURE CLOUD COMPUTING

Alireza Khoshkbarforoushha, Australian National University and Commonwealth Scientiﬁc and Industrial Research Organisation Meisong Wang, Australian National University Rajiv Ranjan, Newcastle University Lizhe Wang, Chinese Academy of Sciences Leila Alem, Commonwealth Scientiﬁc and Industrial Research Organisation Samee U. Khan, North Dakota State University Boualem Benatallah, University of New South Wales

Despite the proliferation of cloud resource orchestration frameworks (CROFs), DevOps managers and application developers still have no systematic tool for evaluating their features against desired criteria. The authors present generic technical dimensions for analyzing CROF capabilities and understanding prominent research to reﬁne them.

loud computing assembles a large network set of cloud resources to fit application architecture opti- of virtualized services—from storage and mized for the desired quality of service (QoS) remains a networking to databases and workload- challenging technical problem. In simple terms, cloud Cbalancing software—and enables instant resource orchestration is the selection, deployment, access to virtually unlimited software and hardware monitoring, and runtime management of software and resources. It offers a number of considerable advantages, hardware resources to ensure that applications meet including no upfront investment, lower operating costs, QoS targets, such as availability, throughput, latency, and infinite scalability. However, orchestrating the right security, cost, and reliability under uncertainties.1

Email CRM Crowdsourcing

Since the inception of cloud com- CloudFormation puting in mid-2000, academic groups PaaS and industry vendors have developed various cloud resource orchestration frameworks (CROFs) to simplify appli- Web server Database Load App server Monitoring cation management. A CROF aids soft- appliance appliance balancer appliance appliance ware engineers, DevOps managers, and infrastructure administrators in migrating to and managing in-house IaaS applications in cloud environments. Elastic IP Figure 1 shows how a CROF fits into CPU Cache memory VPN EBS the cloud computing ecosystem. Amazon Web Services (AWS) Elas- FIGURE 1. Cloud resource orchestration framework’s (CROF’s) position in the cloud tic Beanstalk is an example of an ecosystem. Despite the many cloud-standardization projects, the community has not yet easy-to-use CROF for deploying and deﬁned a comprehensive standard to cover all cloud layers including infrastructure as a scaling multitier Web applications service (IaaS), platform as a service (PaaS), and software as a service (SaaS). CRM: cus- developed with popular program- tomer relationship management; EBS: elastic block store; VPN: virtual private network. ming languages, including Java, .NET, PHP, Node.js, Python, and Ruby. Another example is Ooyala’s CROF administration overhead. Moreover, simplicity of comprehension, which for delivering multimedia content migration from one cloud provider to are essential to successful practical online. At the infrastructure layer, another is nontrivial, if not impossible. use. Resource orchestration in cloud Ooyala’s CROF leverages AWS Elas- Despite the many cloud standardiza- environments must also contend tic Compute Cloud (EC2) and Simple tion projects (http://cloud-standards with the scale and variety of resource

Storage Solution (S3) resources for .org),___ the community has no compre- types and the uncertainties inherent content distribution and storage. hensive standard.2 in a cloud environment. Finally, inte- From the service provider’s per- gration and interoperational depen- PROLIFERATION WITHOUT spective, the lack of standardiza- dencies intensify the challenge of STANDARDIZATION tion among resource-orchestration building application architecture With the proliferation of CROFs from techniques is no accident. Most over the cloud.1 competing vendors and academic approaches aim to improve productiv- groups has come an often bewilder- ity by automating the structured pro- EVALUATING CROFs ing array of frameworks with simi- cesses that IT departments or profes- The variety of CROFs offered can lar (if not identical) functionalities. sional programmers handle—those make selection challenging for soft- For example, any of the CROFs from more equipped for the hardware and ware engineers, solution architects, or RightScale, Bitnami, EngineYard, and software resource coordination that DevOps managers who want to migrate CloudSwitch are suitable for man- orchestration requires. their applications to the cloud. To aid aging Web applications over AWS In addition to traditional con- that process, we developed technical and other public cloud infrastruc- trol and dataflow programming dimensions for CROF analysis that tures. Similarly, CloudFront, Ooyala, constructs, there is a need for rich provide insights into existing frame- MetaCDN, or RackSpace Cloud Files abstractions to support consistency works by answering why, what, and could be used to manage content across physical and logical layers; how frameworks complete resource delivery–network (CDN) applications. exception handling; and flexible, orchestration. We then analyzed the This diversity makes selection a risky efficient resource coordination and strengths and weaknesses of popular task, as wrong decisions could lead management. All these supporting frameworks in light of these dimen- to vendor lock-in, poor application mechanisms must balance the ten- sions and surveyed recent research QoS, excessive costs, and unwanted sions between the rich semantics and work pertinent to each dimension.

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Application domain reinforced by parallelizing subtasks administrator or DevOps manager This dimension refers to the type of in a cluster. manipulates cloud resources. applications that the frameworks have been targeted and customized Resource type Command line. The command-line to, including multitier Web applica- This dimension refers to the resource interface wraps cloud-specific API tions, CDN, and large-scale data process- type the framework can orchestrate. actions as commands or scripts that ing (also known as big data). Multitier Infrastructure resources, which include are executable through Linux (bash Web applications refers to migrat- network, CPU, and blob storage, or sh) or Windows-based (command. ing in-house Web, mobile, or gaming require looking at aspects such as IP com or cmd.exe) shell environments. applications to public or private clouds type or ports, cores or addressing bit, Although command-line orches- for improved scalability, availability, and storage size and format. Platform tration frameworks can be easier to implement, their use requires sophisticated technical expertise and a deep understanding of cloud resources and CROFs WITH CLOUD INTEROPERABILITY— related orchestration operations. TO PORT APPLICATIONS TO OR USE RESOURCES FROM MULTIPLE CLOUDS— Web portal. The portal presents cloud resources as user-friendly artifacts, ARE MORE LIKELY TO BE ADOPTED. such as buttons and checkboxes, and resource catalogs. Visual artifacts and catalogs aim to simplify resource selection, assembly, and deployment. and so on, as well as for conducting resources include application servers A catalog manages resource-entity development and test activities in the (such as the Java application server), sets that can be instantiated to create cloud environment. monitoring services, database servers, CPU, storage, and network objects. CROFs that target CDN applications and the like. Software components and The portal also features an editor give businesses and Web application subprocesses include customer rela- that lets users assemble and deploy developers an easy and cost-effective tionship management (CRM) business applications by dragging appliance way to distribute content (including processes that are formed and exe- entities from the catalog, and integrat- images, videos, and webpages) to spo- cuted through software-component ing and configuring them through cus- radic geographical locations with low orchestration to deliver a business ser- tomized configuration-management latency and high data-transfer speeds. vice to end users. An example is the interfaces. CDN offerings normally use edge com- Salesforce CRM platform. These features make the Web puting, which pushes the frontier of All three resource categories (infra- portal simpler and more flexible for computing applications, data, and ser- structure, platform, and software) are the administrator to use than the vices away from centralized nodes to mapped to the so-called cloud service command-line tool. servers on the Internet’s edge. model that includes the infrastructure- CROFs with large-scale data- as-a-service (IaaS), platform-as-a- Web services API. The Web services intensive computing platforms rely service (PaaS), and software-as-a- API enables other tools and systems to predominantly on MapReduce, a service (SaaS) layers. integrate cloud-resource-management simple yet effective programming operations into their functional- model.3 MapReduce adopts an inher- Resource access component ities. As such, it provides the highest ently divide-and-conquer strategy This dimension captures what inter- abstraction and greatest simplicity of in which a single problem is broken faces the CROF uses to interact with the three interface types, particularly into multiple individual subtasks cloud resources: command line, Web when some cloud-platform function- that include instances of the map and portal, or Web services API. All three alities must be integrated into other reduce tasks. This strategy is further are abstractions through which an tools and systems.

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aws: AWS management :User cf: AWS CloudFormation wp: WordPress console SaaS 1: signin (account_info)

1.1: acknowledgment WordPress bootstrap script, parameters, and so on are set. 2: createStack(stack_info) Select and configure 2.1: createStack(stack_name,type,key) platform Upload application Select instance type

PaaS 2.1.1: initializeStack() Select and configure Create application Install Wordpress relational database 2.1.2: instantiateStack(stack_params) service (RDS) 2.1.3: stack_status

3: installWordpress(site_info) 3.1: install(site_title,username,pass) IaaS 3.1.1: updateStackstatus() Identity and access Amazon RDS Amazon S3 EC2 management 3.1.1.1: stack_status

FIGURE 2. An example of cloud orchestration operations in Amazon Web Service (AWS) involving the deployment of WordPress through AWS Elastic Beanstalk or AWS CloudFormation with homogeneous interoperability that lets administrators manage the application across different cloud services and coordinate and streamline communication among resources within the same cloud. EC2: Elastic Compute Cloud; S3: Simple Storage Solution.

Interoperability Resource selection interesting statistics.7 The total num- Cloud interoperability is a key fac- CROFs differ in the degree to which ber of distinct appliances required tor in the decision to switch to cloud they automate the selection of soft- for each application varied from 11 to computing, so any platform with high ware and hardware resources. Selec- over 100, depending on application interoperability has a greater chance tion involves identifying and analyz- type. Some applications required up of being adopted. Recognizing this ing alternatives (cloud resources) on to 19 distinct front-end webservers, 67 competitive edge, platform develop- the basis of the decision maker’s (typ- application servers, and 21 back-end ers are attempting to expand their ically the administrator’s) preference. databases. multicloud capability. This dimension Decision makers must not only iden- Clearly, to ensure that configura- refers to a CROF’s ability to port appli- tify as many feasible alternatives as tions are error free, any application- cations across multiple clouds or to possible but also choose the one that deployment technique must account use resources from multiple clouds to best fits their selection criteria. for dependencies across appliances. compose and host applications. In resource selection, approaches Existing deployment tools, which Although interoperability helps can be either manual or automated. provide varying automation lev- prevent cloud provider lock-in, it is In the manual approach, a resource els, fall into manual, script-based, nontrivial to design and implement orchestrator assumes that adminis- language-based, and model-based generic resource orchestrators that can trators have sophisticated technical approaches. Higher automation levels work with various clouds because APIs knowledge that can help them select are preferable to enhance correctness, must be specific to each cloud provider. the most appropriate cloud resources. speed, and ease of documentation. Interoperability can be hybrid or In an automated approach, the orches- In the manual approach, DevOps homogeneous. Hybrid resource orchestrator implements a recommendation managers configure and integrate trators operate across multiple clouds, system (RS) that helps with resource appliances manually by inserting the transparently integrating their re- selection to best fit the desired QoS, XML and text snippets into configu- sources (IaaS, PaaS, and SaaS) as part of features, and cost.4–6 The RS ranks ration files. The script-based approach a single-resource leasing abstraction. different resources according to pre- consists of shell scripts that execute With a hybrid orchestrator, admin- defined selection criteria and presents on CPU resources hosting the appli- istrators can manage and automate them to the administrator. ances. The scripts directly modify both application movement across the appliance-specific configuration clouds and the communication among Application deployment file. Both manual and script-based resources hosted in different clouds. In The scale and complexity of applica- approaches have limited ability contrast, a homogeneous orchestrator tions and cloud resources make them to express dependencies, react to can orchestrate only the resources in increasingly difficult and expensive changes, and verify configurations, a single cloud or from cloud providers to administer and deploy. A 2010 which results in erroneous appli- who apply a similar technology stack study of enterprise applications in For- cation configuration in large-scale in managing their resources. tune 100 companies contained some deployments.

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Language-based approaches are more relationship between an application’s well; however, Amazon and Google powerful and expressive, declaratively QoS targets, current hardware resource offer independent platforms: Ama- defining appliance configurations. allocation, and changes in applica- zon Elastic MapReduce and Google Model-based approaches define a desired tion-workload patterns to adjust hard- BigQuery. The solutions for large-scale state for each appliance. Once instanti- ware allocation. Overall, predictive data processing are built on Hadoop, ated on CPU resources, appliances auto- techniques build on the integration of an open source framework in which matically fetch and execute their state theoretical workload prediction and data and processes are distributed definition from a centralized reposi- resource performance models.6,8,9 across a resizable cluster of computing tory. Both language- and model-based Workload prediction models forecast server instances. Consequently, once approaches can handle dependencies workload behavior across applications the application and data are ready for across appliances and automatically in terms of CPU, storage, I/O, and net- processing, the administrator must react to changes, such as resource fail- work bandwidth requirements. specify and configure the number ure, by activating adaptation actions. Predictive models will be the foun- and types of computing resources to Figure 2 illustrates an instantiation dation of next-generation resource- crunch the deluge of data. This task is of cloud orchestration operations in provisioning frameworks, which challenging because large-scale data AWS, which features manual resource will completely understand work- processing platforms like Hadoop selection and a script-based appli- load and resource demands and will have as many as 200 configuration cation deployment in a single-cloud therefore have a higher resilience to parameters, which precludes ad hoc environment. uncertainties. settings or at least makes them risky for job performance. Runtime QoS adaptation FRAMEWORK ANALYSIS In light of these constraints, CROFs CROFs must be able to adapt to To give administrators a glimpse of should come with what-if analysis dynamic exceptions either manu- how our technical dimensions aid capabilities so that users can config- ally or automatically, such as acting CROF evaluation, we chose 14 CROFs ure and tune cloud resource param- appropriately when some threshold that formed a mix of proprietary and eters and data-intensive computing is reached. Manual adaptation does open source frameworks. All frame- platform settings at the IaaS and PaaS not provide any autoscaling facility; works have active users, reasonably layers. in the best case, the CROF alerts the stable versions, and good documenta- GoGrid and Rackspace also offer administrator through an email of tion. Table 1 shows the results of eval- CDN services, with Rackspace using the need to manually configure the uating the frameworks in terms of our the Akamai network to efficiently instances to adapt to new conditions. seven dimensions. deliver content to edge nodes. For CDN A CROF with automatic adaptation will We also investigated the lat- applications, Amazon’s CloudFront is adapt to exceptions through the use of est research studies, particularly in integrated with other AWS platform reactive and predictive techniques. resource selection, application deploy- to distribute content with low latency Reactive techniques respond to ment, and runtime QoS adaptations. and at high data-transfer speeds. events only after reaching a predefined threshold that is determined through Application domain Resource type monitoring the state of hardware Most off-the-shelf or open source Most cloud frameworks orchestrate and software resources. Although CROFs support managing and migrat- virtual appliances (application and these techniques are simple to define ing in-house multitier Web applica- database servers, for example) in the and implement (nothing more than tions over to a cloud environment. PaaS layer according to major infra- if-then-else statements), they are not CloudSwitch, CA AppLogic, Engine- structure assets, such as Amazon EC2 sufficient to ensure guaranteed QoS Yard, Bitnami, AWS Elastic Beanstalk, and S3 for computing and storage. in some cases, such as during a peak CloudBees, and RightScale are some Other frameworks, such as Google App demand for resources. frameworks in this category. Engine (GAE), operate on Google data- Predictive techniques can dynam- RightScale, GoGrid, and RackSpace centers and Windows Azure, building ically anticipate and capture the have large-scale data processing as on Microsoft’s infrastructure services.

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TABLE 1. Mapping cloud resource orchestration frameworks (CROFs) to evaluation dimensions. Resource Application Resource Resource access selection deployment Runtime QoS CROF Application domain type component Interoperability mode mode adaptation CloudSwitch Web (migration) PaaS Command line, Multicloud (Amazon EC2, Manual Script and model Reactive Web portal, Web Terremark) based services API RightScale Web, gaming, PaaS Command line, Multicloud (AWS, Datapipe, Manual Script and Reactive mobile apps, Web portal Google Compute language based large-scale data Engine, HP Cloud, IDC processing, Frontier, Rackspace, development and Softlayer, Windows Azure, test CloudStack, OpenStack) CA AppLogic Web application PaaS Command line, Single cloud Manual Script, language, Reactive Web portal, Web and model based services API Engine Yard Web and mobile PaaS Command line, Multicloud (AWS, Windows Manual Script and Reactive Web portal, Web Azure, CloudStack, language based services API Terremark) Bitnami Web PaaS Command line, Multicloud (AWS, Windows Manual Script based Reactive Web services API Azure, Amazon EC2, RightScale) AWS Elastic Web, development PaaS Command line, Single cloud (Amazon EC2, Manual Manual and Reactive Beanstalk and test, large-scale Web portal, Web S3 services) script based (for data processing services API bootstrapping with Amazon Elastic applications via MapReduce, CDN CloudFormation) with CloudFront CloudBees Web, development PaaS Command line, Multicloud (AWS, Manual Manual Reactive and test Web portal, Web OpenStack, HP Cloud services API Services) OpenNebula None IaaS Command line, Multicloud (vCloud, Manual Manual and Reactive Web portal, Web OpenStack, Eucalyptus, model based services API Amazon EC2) Eucalyptus Development and IaaS and Command line, Multicloud (AWS) Manual Manual Reactive test PaaS Web portal, REST-based API CohesiveFT Web application PaaS Web portal Multicloud (IBM Manual Model based Not known (migration) SmartCloud Enterprise, Amazon EC2, Amazon VPC, ElasticHosts, Cloud Sigma, Flexiant, Eucalyptus, OpenStack, vCloud) Google App Web and mobile, PaaS (IaaS Web portal, Single cloud Manual Manual Reactive Engine development and services REST-based API test, large-scale through data processing with Google BigQuery Compute Engine) Microsoft Web and mobile, PaaS Command line, Multicloud (Engine Yard) Manual Manual Reactive Azure development and Web portal, Web test services API GoGrid Web, large-scale IaaS, PaaS, Command line, Multicloud (Windows Manual Manual Reactive data processing, and PaaS Web portal, Azure) CDN, development REST-based API and test RackSpace Web, large-scale IaaS and Command line, Multicloud (OpenStack) Manual Manual Reactive data processing, PaaS Web portal, Web CDN (Akamai) services API

*AWS: Amazon Web Services; CDN: content-delivery network; EC2: Elastic Compute Cloud; IaaS: infrastructure as a service; PaaS: platform as a service; REST: Representational State Transfer; S3: Simple Storage Service; SaaS: software as a service.

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Apart from proprietary IaaS plat- still working on having the API as a supported by Bitnami, OpenNebula, forms are open source solutions, first-class citizen on their platform. EngineYard, and CloudBees. including OpenStack, which serves Recent developments aim to sim- the IaaS layer of frameworks such as Interoperability plify interoperability by implement- RackSpace, NeCTAR, OpenNebula, RightScale is one of the pioneers in ing a single API that abstracts APIs CloudStack, and Eucalyptus. Among promoting interoperability, support- in multiple clouds. Examples include these, OpenStack and CloudStack, ing more than 10 public or private Delta Cloud (http://deltacloud.apache both Apache-licensed cloud comput- clouds through its multicloud plat- .org) and JCloud (http://jclouds.apache ing programs, feature well-defined form. Cloud-specific differences are .org). The most recent release of Delta and documented APIs as well AWS. sufficiently abstract that users can Cloud abstracts dozens of cloud provid- focus on running applications and ers such as Amazon EC2, GoGrid, Open- Resource access component access resources on their own terms Nebula, OpenStack, Rackspace, Euca- All 14 CROFs we evaluated support through either the RightScale Dash- lyptus, and Windows Azure APIs into a Web-based interfaces. The provided board or API. single API. UIs from cloud frameworks generally CohesiveFT is another highly in- Although these APIs can sim- do not differentiate between public teroperable platform that provides a plify implementation across multiple users and administrators in their sim- kind of software factory for assembling clouds, developers must still accom- plicity and expressiveness. However, and deploying servers to many public modate the heterogeneities in appli- OpenNebula offers various kinds of or private cloud platforms. In the same ance packaging, virtualization tech- user interfaces for accessing the vir- manner, CloudSwitch (www.verizon nology, resource naming, and so on. tual computing environment through enterprise.com/solutions/cloud) pro-

two Web services—OCCI (http://____ vides a topology manager that abstracts Resource selection opennebula.org/documentation) and the cloud provider’s details from the Most CROFs have ad hoc resource EC2—and two Web interfaces, Open- provisioning and management infra- selection, which means that applica- Nebula Sunstone for administrators structure that the application requires. tion composition is largely up to the and OpenNebula Self-Service for pub- Consequently, CloudSwitch can ac- administrator, who needs specific lic users. commodate new providers by allowing and deep knowledge about workload demands and balancing. The growing use of cloud computing technologies is forcing industry to work on smarter RECENT DEVELOPMENTS AIM TO approaches such as an RS. In the near SIMPLIFY INTEROPERABILITY BY future, CROFs could guide administrators in more precise resource IMPLEMENTING A SINGLE API THAT selection. For example, CloudSwitch ABSTRACTS APIs IN MULTIPLE CLOUDS. features CloudFit (www.techrepublic .com/resource-library/whitepapers /______cloudswicth-architecture-overview), which evaluates the fit of the appli- Having a Web services API increases them to model and install their inter- cation composer’s requested con- the chance that administrators and faces without affecting any existing figurations against available cloud developers will be able to integrate cloud providers. resources such as Amazon EC2. By orchestrator functionalities into their The dominance of AWS and its user automatically selecting the appropri- tools as needed. Most of the 14 frame- diversity makes it a desirable interop- ate combination of resources (proces- works evaluated have APIs, although erability target for almost every cloud sor, memory, and storage, for exam- the functionalities provided are some- provider. CloudStack, OpenStack, ple), CloudFit ensures that cloud times more limited than their Web user and Windows Azure are other poten- deployments operate with sufficient interfaces. EngineYard, for example, is tial targets. As Table 1 shows, they are performance and reliability.

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A cloud framework could also different abstraction level relative to instantiates a template. An interesting employ log files along with best prac- CA AppLogic. CloudSwitch uses cloud feature of CloudFormation is automatic tices to adjust a fitness function that isolation technology, a sandboxing rollback on error, which guarantees balances a resource pool against user technology that hides the hosting that stacks are fully created or not cre- requirements, in much the same way environment complexity of the public ated at all. expert systems have done in the med- cloud from an application, enabling A recently proposed peer-to-peer ical domain. the administrator to automatically architecture uses a component repos- Many research efforts have focused assign cloud resources to applica- itory to manage software-component on automatic resource selection. One tion components. Every data item is deployment, enabling elasticity by proposed framework, CloudGenius, automates decision making on the basis of a model tailored for web- server migration to the cloud.4 The WITH MODEL-BASED APPROACHES, AN framework leverages the analytic APPLICATION WILL WORK IN THE CLOUD hierarchy process (AHP) to translate JUST AS IT WOULD IN THE DATACENTER cloud service selection steps into a multicriteria decision-making prob- WITH THE SAME CONFIGURATIONS AND lem. CloudRecommender5 adopts a IDENTITIES. novel declarative approach for selecting cloud-based infrastructure services, automatically mapping users’ encrypted end to end to ensure secu- using the underlying cloud infrastruc- specified application requirements to rity and privacy. ture provider.10 The provided peer- cloud-service configurations. Model-based approaches, such to-peer architecture has three logical Another proposed framework incor- as those in CA AppLogic and Cloud- layers: porates an online decision-support Switch, guarantee that the application system for resource management that will work in the cloud just as if it were › a design tier holds the descrip- addresses both task scheduling and in the datacenter, because all config- tion of services, resource-management optimization.6 urations (IP and MAC addresses) and › a management tier manages ser- The framework uses fuzzy and neural identities will be the same. vice provisioning, and network–based methods to predict vir- At the same abstraction level as › a cloud infrastructure tier tual machine workload patterns and CloudSwitch, CohesiveFT enables enables the creation of on- migration time. images to be created and configured demand infrastructure. dynamically on the basis of the users’ Application deployment preferences. The images can then be A proof-of-concept implementa- In this dimension, CA AppLogic is a uploaded to the cloud infrastructure. tion is proposed in which the design step ahead, providing a sound model- The AWS CloudFormation ser- tier contains a template designer for based deployment that lets adminis- vice complements the deployment of both the Eclipse and Netbeans inte- trators define appliance workflow by AWS’s Elastic Beanstalk by automat- grated development environments dragging and dropping resource icons ing the creation and management of and a cloud-infrastructure tier that and thus specifying dependencies related AWS infrastructure resources. supports EC2 and an internal HP cloud among appliances, which avoids using The service instantiates a template—a platform. Because the implementation all resources at the same time. Another text file in JavaScript Object Notation is open source, it can be extended to interesting feature is that stopping the that describes all the required AWS include new components and features. application stops appliances in the resources for running the applica- reverse of their start order. tion—and a stack, which is a set of AWS Runtime QoS adaptation CloudSwitch also features model- resources that are created and managed As Table 1 shows, all 14 evaluated based deployment, although at a as a single unit when CloudFormation frameworks have reactive QoS adap-

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ABOUT THE AUTHORS

ALIREZA KHOSHKBARFOROUSHHA is a doctoral student in British Computer Society (BCS). Contact him at ______lizhe.wang@ the College of Engineering and Computer Science at the Aus- gmail.com. tralian National University (ANU) and a graduate researcher in Data61 of the Commonwealth Scientiﬁc and Industrial LEILA ALEM is an adjunct professor in the User Centered Research Organisation (CSIRO) in Canberra, Australia. His Technology Design Research Centre Faculty of Engineering research interests include data-intensive systems and stream- and Information Technology at the University of Technology, ing workload management in the cloud. Khoshkbarforoushha Sydney. While conducting the research reported in this arti- received an MS in computer science from Tarbiat Modares cle, Alem was a principal research scientist at CSIRO. Her University, Tehran. He is a member of ACM. Contact him at research interests include human–computer interaction, com-

[email protected].______puter-supported cooperative work, and wearable computing. Alem received a PhD in artiﬁcial intelligence from University of MEISONG WANG is a master of research student in the Nice Sophia Antipolis. Contact her at [email protected]. College of Engineering and Computer Science at ANU and an assistant researcher at CSIRO. His research inter- SAMEE U. KHAN is an associate professor in the Department ests include cloud computing and data-intensive systems. of Electrical and Computer Engineering at North Dakota Wang received an MS in software engineering from the Har- State University. His research interests include the optimi-

bin Institute of Technology. Contact him at deanmeisong@______zation, robustness, and security of the cloud; grid, cluster, gmail.com. and big data computing; social networks; wired and wireless networks; power systems; smart grids; and optical networks. RAJIV RANJAN is an associate professor in the School of Com- Khan received a PhD in computer science from the University puting Science at Newcastle University. His research interests of Texas at Arlington. He is a Fellow of the IET and the BCS. include cloud computing, the Internet of Things, and big data. He is an ACM Distinguished Lecturer, a member of ACM,

Ranjan received a PhD in computer science from the Univer- and a Senior Member of IEEE. Contact him at ______samee.khan

sity of Melbourne. He is a member of IEEE. Contact him at raj_ @ndsu.edu.______

[email protected]. BOUALEM BENATALLAH is a Scientia Professor in the LIZHE WANG is a professor at the Institute of Remote School of Computer Science and Engineering at the Univer- Sensing & Digital Earth of the Chinese Academy of Sci- sity of New South Wales, Australia. His research interests ences and a ChuTian Chair Professor in the School of Com- include API engineering, Web services composition, feder- puter Science at the China University of Geosciences. His ated cloud services orchestration, crowdsourcing services, research interests include high-performance computing, and business process management. Benatallah received a e-science, and spatial data processing. Wang received a PhD in computer science from Grenoble University, France. D.Eng. from the University of Karlsruhe. He is a Fellow of He is a member of IEEE and ACM. Contact him at ______boualem@ the Institution of Engineering and Technology (IET) and the cse.unsw.edu.______

tation. Some CROFs provide different implementing a custom autoscaling researchers proposed predictive mod-

APIs, leaving the implementation of service through its APIs (https://wiki______els for resource provisioning for read- autoscaling and runtime adaptation .gogrid.com/index.php/API). intensive multtier applications in the to the developer. GoGrid, for exam- Work continues in predictive QoS cloud.9 Yet another effort produced ple, does not automate platform-in- adaptation. One proposed strategy uses a resource-prediction and provision frastructure scaling, allowing cloud prediction-based resource measure- scheme that uses time-series analysis users to manually scale server and ment and provisioning based on a neu- based on an autoregressive integrated storage resource configurations up ral network and linear regression.8 The moving average (ARIMA) as the foun- or down through the customer por- prediction method uses historical data dation for a prediction model.11 tal. When the computational server generated from running a standard Another interesting study resulted is virtualized, only physical memory client–server benchmark for training in an online temporal data-mining allocation changes; CPU and local forecasting models on Amazon EC2. system to model and predict virtual storage allocation remain the same. Taking a different tack from a machine demands.12 The system GoGrid gives developers the option of general predictive framework, other extracts high-level characteristics

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from the virtual machine–request “An Online Fuzzy Decision Support Multi-Tier Applications in the stream and notifi es the provisioning System for Resource Management Cloud,” Future Generation Computer system to prepare virtual machines. in Cloud Environments,” Proc. Systems, vol. 27, no. 6, 2011, Joint IEEE/IFSA World Congress and pp. 871–879. NAFIPS Ann. Meeting (IFSA/NAFIPS 10. J. Kirschnick et al., “Towards an ith the diversity of CROFs— 13), 2013, pp. 754–759. Architecture for Deploying Elastic many with the same func- 7. M. Hajjat et al., “Cloudward Bound: Services in the Cloud,” Software: Wtions—software engineers, Planning for Benefi cial Migration Practice and Experience, vol. 42, no. 4, solution architects, and DevOps man- of Enterprise Applications to the 2012, pp. 395–408. agers can fi nd it challenging to select Cloud,” ACM SIGCOMM Computer 11. W. Fang et al., “RPPS: A Novel the most suitable one for their needs. Comm. Rev., vol. 40, no. 4, 2010, Resource Prediction and Provision- Our seven dimensions facilitate CROF pp. 243–254. ing Scheme in Cloud Data Center,” evaluation by providing a common set 8. S. Islam et al., “Empirical Predic- Proc. 9th IEEE Int’l Conf. Services Com- of characteristics for analysis, breaking tion Models for Adaptive Resource puting (SCC 12), 2012, pp. 609–616. down framework capabilities, deepen- Provisioning in the Cloud,” Future 12. Y. Jiang et al., “ASAP: A Self-Adaptive ing the awareness of their strengths Generation Computer Systems, vol. 28, Prediction System for Instant Cloud and weaknesses, and enabling more no. 1, 2012, pp. 155–162. Resource Demand Provisioning,” informed selection decisions. 9. W. Iqbal et al., “Adaptive Resource Proc. 11th IEEE Int’l Conf. Data Mining Provisioning for Read Intensive (ICDM 11), 2011, pp. 1104–1109. REFERENCES 1. R. Ranjan et al., “Cloud Resource Orchestration Programming: Over- view, Issues, and Directions.” IEEE Internet Computing, vol. 19, no. 5, CONFERENCES 2015, pp. 46–56. in the Palm of Your Hand 2. G.A. Lewis, “Role of Standards in Cloud-Computing Interoperability,” IEEE Computer Society’s Conference Publishing Proc. 46th IEEE Hawaii Int’l Conf. Sys- Services (CPS) is now offering conference program tem Sciences (HICSS 12), 2012, mobile apps! Let your attendees have their conference schedule, conference information, and paper listings in pp. 1652–1661. the palm of their hands.

3. J. Dean and S. Ghemawat, “Map- The conference program mobile app works for Reduce: Simplifi ed Data Processing Android devices, iPhone, iPad, and the Kindle Fire. on Large Clusters,” Comm. ACM, vol. 51, no. 1, 2008, pp. 107–113 4. M. Menzel et al., “CloudGenius: A Hybrid Decision Support Method for Automating the Migration of Web Application Clusters to Public Clouds,” IEEE Trans. Computers, vol. 64, no. 5, 2014, pp. 1336–1348.

5. M. Zhang et al., “A Declarative For more information please contact [email protected]______Recommender System for Cloud Infrastructure Services Selection,” Proc. 9th Int’l Conf. Economics of Grids, Clouds, Systems, and Services (GECON 12), 2012, pp. 102–113. 6. F. Ramezani, J. Lu, and F. Hussain,

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