1 A Survey on Edge Computing Systems and Tools Fang Liu, Guoming Tang, Youhuizi Li, Zhiping Cai, Xingzhou Zhang, Tongqing Zhou Abstract—Driven by the visions of Internet of Things and 5G Architecture Applications communications, the edge computing systems integrate comput- Cloudlet Cachier …… ing, storage and network resources at the edge of the network FocusStack Push from to provide computing infrastructure, enabling developers to CloudPath cloud quickly develop and deploy edge applications. Nowadays the edge Some other systems, e.g., Pull from IoT computing systems have received widespread attention in both PCloud Vigilia, HomePad, LAVEA, MUVR, OpenVDAP, Hybrid cloud- industry and academia. To explore new research opportunities ParaDrop and assist users in selecting suitable edge computing systems for SafeShareRide, VideoEdge edge analytics specific applications, this survey paper provides a comprehensive SpanEdge Programming models overview of the existing edge computing systems and introduces Cloud-sea AirBox Firework representative projects. A comparison of open source tools is computing presented according to their applicability. Finally, we highlight Supporting Supporting energy efficiency and deep learning optimization of edge com- puting systems. Open issues for analyzing and designing an edge Open-source systems Akraino computing system are also studied in this survey. CORD Edge Stack EdgeX Apache Azure IoT Foundry Edgent Edge I. INTRODUCTION In the post-Cloud era, the proliferation of Internet of Things Fig. 1. Categorization of edge computing systems. (IoT) and the popularization of 4G/5G, gradually changes the public’s habit of accessing and processing data, and challenges the linearly increasing capability of cloud computing. Edge AzureStack in 2017, which allows cloud computing ca- computing is a new computing paradigm with data processed pabilities to be integrated into the terminal, and data can at the edge of the network. Promoted by the fast growing be processed and analyzed on the terminal device. demand and interest in this area, the edge computing systems • Pull from IoT. Internet of Things (IoT) applications and tools are blooming, even though some of them may not pull services and computation from the faraway cloud be popularly used right now. to the near edge to handle the huge amount of data There are many classification perspectives to distinguish generated by IoT devices. Representative systems include different edge computing system. To figure out why edge PCloud, ParaDrop, FocusStack and SpanEdge. Advances computing appears as well as its necessity, we pay more in embedded Systems-on-a-Chip (SoCs) have given rise attention to the basic motivations. Specifically, based on dif- to many IoT devices that are powerful enough to run ferent design demands, existing edge computing systems can embedded operating systems and complex algorithms. roughly be classified into three categories, together yielding Many manufacturers integrate machine learning and even innovations on system architecture, programming models and deep learning capabilities into IoT devices. Utilizing edge various applications, as shown in Fig. 1. computing systems and tools, IoT devices can effectively • Push from cloud. In this category, cloud providers push share computing, storage, and network resources while services and computation to the edge in order to leverage arXiv:1911.02794v1 [cs.DC] 7 Nov 2019 maintaining a certain degree of independence. locality, reduce response time and improve user experi- • Hybrid cloud-edge analytics. The integration of advan- ence. Representative systems include Cloudlet, Cachier, tages of cloud and edge provides a solution to facilitate AirBox, and CloudPath. Many traditional cloud comput- both global optimal results and minimum response time ing service providers are actively pushing cloud services in modern advanced services and applications. Represen- closer to users, shortening the distance between customers tative systems include Firework and Cloud-Sea Comput- and cloud computing, so as not to lose market to mo- ing Systems. Such edge computing systems utilize the bile edge computing. For example, Microsoft launched processing power of IoT devices to filter, pre-process, and aggregate IoT data, while employing the power and F. Liu is with the School of Data and Computer Science, Sun Yat-sen Uni- versity, Guangzhou, Guangdong, China (e-mail: [email protected]). flexibility of cloud services to run complex analytics G. Tang is with the Key Laboratory of Science and Technology on Information on those data. For example, Alibaba Cloud launched its System Engineering, National University of Defense Technology, Changsha, first IoT edge computing product, LinkEdge, in 2018, Hunan, China (e-mail: [email protected]). Y. Li is with the School of Compute Science and Technology, Hangzhou Dianzi University, China. Z. which expands its advantages in cloud computing, big Cai and T. Zhou are with the College of Computer, National University of data and artificial intelligence to the edge to build a Defense Technology, Changsha, Hunan, China (e-mail: [email protected], cloud/edge integrated collaborative computing system; [email protected]). X. Zhang is with State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy Amazon released AWS Greengrass in 2017, which can of Sciences, China. extend AWS seamlessly to devices so that devices can 2 System View Edge Computing Systems & Tools (Sec. II) ) IV . Sec Open Source Edge Computing Projects ( (Sec. III) Energy EfficiencyEnergy Application View Deep Learning Optimization at the Edge (Sec. V) Fig. 3. Cloudlet Component Overview and Functions that support application Fig. 2. Major building blocks and organization of this survey paper. mobility. A: Cloudlet Discovery, B: VM Provisioning, C: VM Handoff. perform local operations on the data they generate, while data is transferred to the cloud for management, analysis, proposed to support the deep learning models at the edge (in and storage. Sec. V). From a research point of view, this paper gives a detailed Our main contributions in this work are as follows: introduction to the distinctive ideas and model abstractions of • Reviewing existing systems and open source projects for the aforementioned edge computing systems. Noted that the edge computing by categorizing them from their design three categories are presented to clearly explain the necessity demands and innovations. We study the targets, architec- of edge computing, and the classification is not the main line of ture, characteristics, and limitations of the systems in a this paper. Specifically, we review systems designed for archi- comparative way. tecture innovation first, then introduce those for programming • Investigating the energy efficiency enhancing mechanism models and applications (in Sec. II). Besides, some recently for edge computing from the view of the cloud, the edge efforts for specific application scenarios are also studied. servers, and the battery-powered devices. While we can find a lot of systems using edge computing • Studying the technological innovations dedicated to de- as the building block, there still lacks standardization to such ploying deep learning models on the edge, including a paradigm. Therefore, a comprehensive and coordinated set systems and toolkits, packages, and hardware. of foundational open-source systems/tools are also needed • Identifying challenges and open research issues of edge to accelerate the deployment of IoT and edge computing computing systems, such as mobility support, multi-user solutions. Some open source edge computing projects have fairness, and privacy protection. been launched recently (e.g., CORD). As shown in Fig. 1, We hope this effort will inspire further research on edge these systems can support the design of both architecture and computing systems. The contents and their organization in this programming models with useful APIs. We review these open paper are shown in Fig. 2. Besides the major four building source systems with a comparative study on their characteris- blocks (Sec. II∼Sec. V), we also give a list of open issues for tics (in Sec. III). analyzing and designing an edge computing system in Sec. VI. When designing the edge computing system described The paper is concluded in Sec. VII. above, energy efficiency is always considered as one of the major concerns as the edge hardware is energy-restriction. II. EDGE COMPUTING SYSTEMS AND TOOLS Meanwhile, the increasing number of IoT devices is bringing In this section, we review edge computing systems and tools the growth of energy-hungry services. Therefore, we also presenting architecture innovations, programming models, and review the energy efficiency enhancing mechanisms adopted applications, respectively. For each part, we introduce work by the state-of-the-art edge computing systems from the three- under the “push”, “pull”, and “hybrid” demand in turn. layer paradigm of edge computing (in Sec. IV). In addition to investigations from the system view, we also look into the emerging techniques in edge computing A. Cloudlet system from the application view. Recently, deep learning In 2009, Carnegie Mellon University proposed the concept based Artificial Intelligence (AI) applications are widely used of Cloudlet [1], and the Open Edge computing initiative was and offloading the AI functions from the cloud to the edge is also evolved from the Cloudlet project [2]. Cloudlet is a becoming a trend.