[DBWorld] WWWJ Special Issue on Resource Management at the Edge for Future Web, Mobile and IoT Applications

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 center. 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, u!
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 ud 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. Its topics 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

IMPORTANT DATES

Manuscript Due: 30 July, 2021
First Round of Reviews: 30 September, 2021
Decision of Acceptance: 30 November, 2021
Publication Date: early 2022

GUEST EDITORS

Dr. Qiang He
School of Software Electrical Engineering
Swinburne University of Technology

Prof. Fang Dong
School of Computer Science and Engineering
Southeast University

Dr. Chenshu Wu
Department of Electrical and Computer Engineering
University of Maryland

Prof. Yun Yang
School of Software Electrical Engineering
Swinburne University of Technology
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