EDGE COMPUTING: RESEARCH AND OUTLOOK A Thesis Presented to the Faculty of California State Polytechnic University, Pomona In Partial Fulfillment Of the Requirements for the Degree Master of Science In Computer Science By Sunit Bhopal 2020 SIGNATURE PAGE PROJECT THESIS: EDGE COMPUTING:RESEARCH AND OUTLOOK. AUTHOR: Sunit Bhopal DATE SUBMITTED: Fall 2020 Department of Computer Science. Professor Yu Sun ____________________________________ Thesis Committee Chair Computer Science Professor Lan Yang ____________________________________ Computer Science Professor Gilbert Young ____________________________________ Computer Science ii ACKNOWLEDGEMENTS I would first like to thank my thesis advisor Professor Yusun whose valuable guidance helped me whenever I ran into a trouble spot or had a question about my research or writing. Professor Yusun consistently allowed this paper to be my own work but steered me in the right the direction whenever he thought I needed it. I would also like to thank my committee members:- Professor Lan Yang, Professor Gilbert Young for their participation. Without their passionate participation and input, this thesis could not have been successfully conducted. iii ABSTRACT In recent years, the Edge computing paradigm has gained considerable popularity in academic and industrial circles. It serves as a key enabler for many future technologies like 5G, Internet of Things (IoT), augmented reality and vehicle-to-vehicle communications by connecting cloud computing facilities and services to the end users. The Edge computing paradigm provides low latency, mobility, and location awareness support to delay-sensitive applications. Significant research has been carried out in the area of Edge computing, which is reviewed in terms of latest developments such as Mobile Edge Computing, Cloudlet, and Fog computing, resulting in providing researchers with more insight into the existing solutions and future applications. This article is meant to serve as a comprehensive survey of recent advancements in Edge computing highlighting the core applications. It also discusses the importance of Edge computing in real life scenarios where response time constitutes the fundamental requirement for many applications. The article concludes with identifying the requirements and discussing open research challenges in Edge computing. iv TABLE OF CONTENTS ACKNOWLEDGEMENTS iii ABSTRACT iv LIST OF TABLES viii LIST OF FIGURES ix CHAPTER 1 1 1. Introduction to Edge Computing 1 CHAPTER 2 5 2. Origin and Background 5 CHAPTER 3 8 3. Importance of Edge Computing 8 CHAPTER 4 10 4. Architecture of Edge Computing 10 4.1. IBM’s implementation of an edge computing architecture 13 CHAPTER 5 15 5. IOT(Internet of things) 15 5.1. How big is the Internet of Things? 16 5.2. What is the Industrial Internet of Things? 16 5.3. What are the benefits of the Internet of Things for consumers? 18 5.4. Issues or Challenges of Internet of things 18 5.5. IoT evolution: Where does the Internet of Things go next? 21 CHAPTER 6 22 v 6. Benefits of Edge computing 22 6.1. Low Latency 22 6.2. Security 23 6.3. Scalability 24 6.4. Versatility 25 6.5. Reliability 26 CHAPTER 7 28 7. Cloud computing 28 7.1. Service Models of Cloud Computing 28 7.2. Deployment Models of Cloud Computing 29 7.3. Benefits of Using Cloud Computing 29 CHAPTER 8 31 8. Edge Computing vs Cloud Computing 31 CHAPTER 9 32 9. Requirements for Edge Computing 32 9.1. User Requirements 37 CHAPTER 10 43 10. Edge Computing Technologies 43 10.1. Multi-Access Edge Computing 43 10.2. Fog Computing 44 10.3. Cloudlets 46 CHAPTER 11 48 11. Edge computing uses cases. 48 vi 11.1. Device Management 48 11.2. Security 49 11.3. Priority Messaging 51 11.4. Data Aggregation 53 11.5. Cloud Enablement 54 11.6. IoT Image and Audio Processing 56 CHAPTER 12 58 12. Industries using Edge Computing 58 12.1. Amazon Web Services 58 12.2. IBM 62 12.3. Dell Computers 66 12.4. 5G Network 67 CHAPTER 13 68 13. Challenges 68 13.1. Proliferation of devices, platforms and protocols 68 13.2. Open vs. Proprietary Systems 68 13.3. Time-Critical Performance 68 13.4. Hardware Constraints 69 13.5. Open edge ecosystems 69 CHAPTER 14 70 14. Conclusion 70 REFERENCES 71 vii LIST OF TABLES TABLE 1 COMPANIES USING AMAZON WEB SERVICES – I.……………….61 viii LIST OF FIGURES FIGURE 1. BASIC EDGE COMPUTING...…………………………………………....... 4 FIGURE 2. ORIGIN OF EDGE COMPUTING...………………………………………...7 FIGURE 3. EDGE COMPUTING LAYOUTS…...……………………………………..13 FIGURE 4. IBM’S IMPLEMENTATION OF AN EDGE………………………………14 FIGURE 5. BENEFITS OF LOW LATENCY…………………………………………..33 ix CHAPTER 1 1. Introduction to Edge Computing The notion of network-based computing dates to the 1960s, but many believe the first use of “cloud computing” in its modern context occurred on August 9, 2006, when then Google CEO Eric Schmidt introduced the term to an industry conference. It enabled information to be stored and processed on remote servers, which meant our devices could offer services beyond their technical capabilities. Using the cloud, a device with only a few gigabytes of memory can effectively host an infinite amount of data. As time has gone by, though, the cloud has started to impede certain technologies, especially IoT[1]. The scope of IOT(Internet of Things) is so vast that cloud computing alone cannot be a means of data processing. The data sent by IOT over a Wi-Fi or cellular network can slow down the entire network. Without the access to the central cloud IOT devices are useless because of the devices not having an internet connection. This is where edge computing comes in. According to Wikipedia,” Edge Computing is a distributed computing paradigm in which processing and computation are performed mainly on classified device nodes known as smart devices or edge devices as opposed to processed in a centralized cloud environment or data centers”[2]. It helps to provide server resources, data analysis, and artificial intelligence to data collection sources and cyber-physical sources like smart sensors and actuators. It brings the service and utilities of cloud computing closer to the end user and is characterized by fast processing and quick application response time. This doesn’t completely eliminate the need for a cloud, but it can reduce the amount of data that needs to be sent to the cloud. Edge computing allows for cloud-like functionality on our own 1 devices or at the network “edge,” which is a term used to describe the point where a device or network communicates with the internet. That could be a device’s processor, a router, an ISP, or a local edge server. Instead of sending data to a remote server, data is processed as close to the device as possible or even on the device itself. For example, say you have an autonomous car with a rearview camera used for accident prevention. Relying on a cloud computing system to process the image data and return results to the onboard systems for action would be impractical since the slow/intermittent data connection would result in poor performance. This setup would also use a lot of data for transferring large video files back and forth from the cloud, and it would strain the cloud server to process data from several cameras at once and send back critical results almost instantaneously. But if the car’s computing system can perform most of the process itself and send information to the cloud only when truly necessary, it results in faster and more reliable performance, lower costs for data transfer, and less strain put on cloud servers. Further there are some of the key components that form the edge ecosystem: Cloud This can be a public or private cloud, which can be a repository for the container-based workloads like applications and machine learning models. These clouds also host and run the applications that are used to orchestrate and manage the different edge nodes. Workloads on the edge, both local and device workloads, will interact with workloads on these clouds. The cloud can also be a source and destination for any data that is required by the other nodes. 2 Edge An edge is a special-purpose piece of equipment that also has compute capacity that is integrated into that device. Interesting work can be performed on edge devices, such as an assembly machine on a factory floor, an ATM, an intelligent camera, or an automobile. Often driven by economic considerations, an edge device typically has limited compute resources. It is common to find edge devices that have ARM or x86 class CPUs with 1 or 2 cores, 128 MB of memory, and perhaps 1 GB of local persistent storage. Although edge devices can be more powerful, they are the exception rather than the norm currently. Edge node:-An edge node is a generic way of referring to any edge device, edge server, or edge gateway on which edge computing can be performed. Edge Cluster/Server:-An edge cluster/server is a general-purpose IT computer that is located in a remote operations facility such as a factory, retail store, hotel, distribution center, or bank. An edge cluster/server is typically constructed with an industrial PC or racked computer form factor. It is common to find edge servers with 8, 16, or more cores of compute capacity, 16GB of memory, and hundreds of GBs of local storage. An edge cluster/server is typically used to run enterprise application workloads and shared services. Edge Gateway :-An edge gateway is typically an edge cluster/server which, in addition to being able to host enterprise application workloads and shared services, also has services that perform network functions such as protocol translation, network termination, tunneling, firewall protection, or wireless connection.