Aims and scope
In recent years, the variety of web, mobile and Internet-of-Things (IoT) applications has been increasing rapidly. Many latency-sensitive applications have thrived to fulfil end-uses’ sophisticated needs, e.g. web gaming, virtual reality, autonomous vehicles, etc. Their need for low latency has motivated the emergence of edge computing, a novel computing paradigm that extends cloud computing. Edge computing allows applications to be deployed on edge servers attached to base stations and access points to serve nearby users. It is one of 5G’s key enabler technologies. As the 5G rolls out around the world, many edge applications will be deployed by app vendors and accessed by massive end-users. This raises many new opportunities as well as challenges in new models, techniques and mechanisms for allocating, deploying and utilizing various resources at the edge of the cloud, e.g., computational resources, storage resources, bandwidth, applications, etc.
Conventional cloud resources are often managed in a centralized manner across virtual machines and/or physical machines deployed and running in a public or private cloud data centre. Edge computing fundamentally changes the way resources are managed. First, edge servers, are attached to base stations and access points geographically distributed around the globe in close proximity to end-users. From an edge infrastructure provider’s perspective (e.g. a 5G mobile carrier), new decentralized models, techniques and mechanisms are needed to manage the resources on edge servers without incurring excessive network latency and network traffic. Second, various edge applications will be deployed and running on edge severs at the edge instead of cloud servers in the remote cloud. From an app vendor’s perspective, new models, techniques and mechanisms are needed to manage their edge applications, achieving cost-effectiveness and ensuring app users’ quality of experience. Third, unlike cloud servers, edge servers can only serve end-users within their coverage areas. From an end-user’s perspective, new models, techniques and mechanisms are needed to help them access the right applications on the right edge server with the aims to minimize latency and energy consumption on their devices.
Managing resources at the edge will enable and promote web, mobile and IoT applications that require low latency in the new 5G era. It is a new and open research field. The goal of this special issue is to explore new models, techniques and mechanisms for managing resources at the edge from the perspectives of edge infrastructure providers, the app vendors and the end-users. It will invite innovative contributions from both industry and academia to provide a forum to publish state-of-the-art research findings on different aspects of this research field.
Topics of interest include but are not limited to:
- System architectures for resource management at the edge
- Modelling, measurement and evaluation of resource management at the edge
- Decentralized resource algorithms resource management at the edge
- Resource management for specific edge applications, e.g., edge data analytics and edge artificial intelligence (AI)Security assurance and privacy preservation for resource management at the edge
- Communication protocols and technologies for resource management at the edge
- Mechanisms for computation offloading at the edge
- Data storage, distribution and management at the edge
- Architecture and implementation for edge applications, e.g. edge data analytics and edge AI
- Lightweight mechanisms, techniques and algorithms for resource management at the edge
- Task scheduling and management at the edge
- Resource management at the edge powered by blockchain
Manuscript Due: 30 July, 2021First Round of Reviews: 30 September, 2021Author revision deadline: 30 October, 2021Final decision notification: 30 November, 2021Publication Date: early 2022
Dr. Qiang He ([email protected])School of Software Electrical EngineeringSwinburne University of TechnologyAustralia
Prof. Fang Dong ([email protected]) School of Computer Science and EngineeringSoutheast UniversityChina
Dr. Chenshu Wu ([email protected])Department of Electrical and Computer EngineeringUniversity of MarylandUSA
Prof. Yun Yang ([email protected])School of Software Electrical EngineeringSwinburne University of TechnologyAustralia
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Guest Editor bios
Qiang He is an Associate Professor at Swinburne University of Technology. He received his first PhD degree from the Swinburne University of Technology, Australia, and the second PhD degree as well his BSc degree from Huazhong University of Science and Technology, China. Dr. He’ research interests mainly include edge computing, cloud computing, software engineering, and service computing. He has authored or coauthored papers published on leading conferences and journals, including IEEE TPDS, IEEE TSE, IEEE TDSC, IEEE TCC, IEEE TSC, IEEE TBD, IEEE WWW, ICSE,ICDE, IJCAI, ICDM, ICSOC, ICWS and CLOUD.
Fang Dong received the PhD degree in computer science from the Southeast University, China. He is currently a Professor and the Director of Big Data Computing Center at Southeast University, China. His research interests mainly include edge computing, cloud computing. He has authored or coauthored papers published on leading conferences and journals, including IEEE TSC, IEEE TII, WWWJ, Infocom and ICPP. He is the co-chair of ACM Nanjing Chapter and the general secretary of ACM SIGCOMM China.
Chenshu Wu is an Assistant Research Scientist at the University of Maryland, College Park. He is also the Chief Scientist of Origin Wireless Inc., a spotlight AIoT company. He received his B.S. degree and Ph.D. degree in Computer Science both from Tsinghua University. His research interests include the Internet of Things, wireless AI, mobile computing, edge computing, etc. He has published two books, 60+ papers in prestigious conferences and journals (such as SIGCOMM, NSDI, MobiCom, MobiSys, UbiComp, TMC, JSAC), and 40+ filed/granted patents. Part of his research has been commercialized as award-winning products, including LinkSys Aware (CES 2020 Innovation Award), HEX Home (CES 2021 Innovation Award), and Origin Health Remote Patient Monitoring (CES 2021 Best of Innovation Award). He served as TPC members for leading conferences such as IEEE INFOCOM and IEEE ICDCS. More information can be found on his website: https://cswu.me
Yun Yang received the PhD degree in computer science from the University of Queensland, Australia, in 1992. He is currently a full professor with the School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, Australia. His research interests include edge computing, cloud computing, software technologies, workflow systems, and service computing. He is a senior member of the IEEE. He has authored or co-authored over 300 papers published leading conferences and journals. He recently served or is serving on the editorial boards of IEEE Transactions on Cloud Computing (TCC) and IEEE Transactions on Parallel and Distributed Systems (TPDS).