为多接入边缘网络部署经济高效的服务器:合作方案

IF 5.6 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS IEEE Transactions on Parallel and Distributed Systems Pub Date : 2024-07-11 DOI:10.1109/TPDS.2024.3426523
Rong Cong;Zhiwei Zhao;Linyuanqi Zhang;Geyong Min
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引用次数: 0

摘要

5G/6G 与边缘计算的结合被视为一种前景广阔的模式,可为物联网(IoT)提供无处不在的密集计算。高昂的部署成本是实现 5G/6G 边缘计算的主要障碍之一。大多数现有研究都试图通过避免覆盖重叠,部署最少数量的边缘服务器来覆盖目标区域。然而,按照这种框架,每台服务器的资源需求将因工作负载变化时的峰值需求而急剧增加。更糟糕的是,大多数资源在大部分时间都得不到充分利用。为了解决这个问题,我们提出了 CoopEdge,一种用于合作式多访问边缘计算的经济高效的服务器部署方案。CoopEdge 的主要理念是允许部署重叠的服务器,以合作的方式处理不同请求的工作负载。通过这种方式,峰值需求可以被分散到多个服务器上,每个服务器的资源需求也可以大大降低。我们提出了一种两步增量部署(TID)算法来共同决定服务器部署和合作策略。针对多个网络运营商不愿意相互合作的情况,我们将 TID 算法进一步扩展为基于博弈论的分布式 TID 算法。基于七个真实世界边缘应用的测量结果,我们进行了广泛的评估实验。结果表明,与最先进的工作相比,CoopEdge 大幅降低了 38.7% 的部署成本,提高了 36.2% 的资源利用率,而所提出的分布式算法可以实现与 CoopEdge 相当的部署成本,尤其是对于小覆盖范围的服务器。
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Cost-Effective Server Deployment for Multi-Access Edge Networks: A Cooperative Scheme
The combination of 5G/6G and edge computing has been envisioned as a promising paradigm to empower pervasive and intensive computing for the Internet-of-Things (IoT). High deployment cost is one of the major obstacles for realizing 5G/6G edge computing. Most existing works tried to deploy the minimum number of edge servers to cover a target area by avoiding coverage overlaps. However, following this framework, the resource requirement per server will be drastically increased by the peak requirement during workload variations. Even worse, most resources will be left under-utilized for most of the time. To address this problem, we propose CoopEdge, a cost-effective server deployment scheme for cooperative multi-access edge computing. The key idea of CoopEdge is to allow deploying overlapped servers to handle variable requested workloads in a cooperative manner. In this way, the peak demands can be dispersed into multiple servers, and the resource requirement for each server can be greatly reduced. We propose a Two-step Incremental Deployment (TID) algorithm to jointly decide the server deployment and cooperation policies. For the scenarios involving multiple network operators that are unwilling to cooperate with each other, we further extend the TID algorithm to a distributed TID algorithm based on the game theory. Extensive evaluation experiments are conducted based on the measurement results of seven real-world edge applications. The results show that compared with the state-of-the-art work, CoopEdge significantly reduces the deployment cost by 38.7% and improves resource utilization by 36.2%, and the proposed distributed algorithm can achieve a comparable deployment cost with CoopEdge, especially for small-coverage servers.
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来源期刊
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems 工程技术-工程:电子与电气
CiteScore
11.00
自引率
9.40%
发文量
281
审稿时长
5.6 months
期刊介绍: IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. It publishes a range of papers, comments on previously published papers, and survey articles that deal with the parallel and distributed systems research areas of current importance to our readers. Particular areas of interest include, but are not limited to: a) Parallel and distributed algorithms, focusing on topics such as: models of computation; numerical, combinatorial, and data-intensive parallel algorithms, scalability of algorithms and data structures for parallel and distributed systems, communication and synchronization protocols, network algorithms, scheduling, and load balancing. b) Applications of parallel and distributed computing, including computational and data-enabled science and engineering, big data applications, parallel crowd sourcing, large-scale social network analysis, management of big data, cloud and grid computing, scientific and biomedical applications, mobile computing, and cyber-physical systems. c) Parallel and distributed architectures, including architectures for instruction-level and thread-level parallelism; design, analysis, implementation, fault resilience and performance measurements of multiple-processor systems; multicore processors, heterogeneous many-core systems; petascale and exascale systems designs; novel big data architectures; special purpose architectures, including graphics processors, signal processors, network processors, media accelerators, and other special purpose processors and accelerators; impact of technology on architecture; network and interconnect architectures; parallel I/O and storage systems; architecture of the memory hierarchy; power-efficient and green computing architectures; dependable architectures; and performance modeling and evaluation. d) Parallel and distributed software, including parallel and multicore programming languages and compilers, runtime systems, operating systems, Internet computing and web services, resource management including green computing, middleware for grids, clouds, and data centers, libraries, performance modeling and evaluation, parallel programming paradigms, and programming environments and tools.
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