Availability-Aware Revenue-Effective Application Deployment in Multi-Access Edge Computing

IF 5.6 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

IEEE Transactions on Parallel and Distributed Systems Pub Date : 2024-03-13 DOI:10.1109/TPDS.2024.3399840

Lu Zhao;Fu Xiao;Bo Li;Jian Zhou;Xiaolong Xu;Yun Yang

{"title":"Availability-Aware Revenue-Effective Application Deployment in Multi-Access Edge Computing","authors":"Lu Zhao;Fu Xiao;Bo Li;Jian Zhou;Xiaolong Xu;Yun Yang","doi":"10.1109/TPDS.2024.3399840","DOIUrl":null,"url":null,"abstract":"Multi-access edge computing (MEC) has emerged as a promising computing paradigm to push computing resources and services to the network edge. It allows applications/services to be deployed on edge servers for provisioning low-latency services to nearby users. However, in the MEC environment, edge servers may suffer from failures while the app vendor has to guarantee continuously available services to its users, thereby securing its revenue for application instances deployed. In this paper, we focus on available service provisioning when cost-effectively deploying application instances on edge servers. We first formulate a novel \nA\nvailability-aware \nR\nevenue-effective \nA\npplication \nD\neployment (ARAD) problem in the MEC environment with the aim to maximize the overall revenue by considering both service availability benefit and deployment cost. We prove that the ARAD problem is \n<inline-formula><tex-math>$\\mathcal {NP}$</tex-math></inline-formula>\n-hard. Then, we propose an approximation algorithm named \nARAD-A\n to find the ARAD solution efficiently with a constant approximation ratio of \n<inline-formula><tex-math>$\\frac{1}{2}$</tex-math></inline-formula>\n. We extensively evaluate the performance of \nARAD-A\n against five representative approaches. Experimental results demonstrate that our \nARAD-A\n can achieve the best performance in securing the app vendor's overall revenue.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":null,"pages":null},"PeriodicalIF":5.6000,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Parallel and Distributed Systems","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10529602/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}

引用次数: 0

Abstract

Multi-access edge computing (MEC) has emerged as a promising computing paradigm to push computing resources and services to the network edge. It allows applications/services to be deployed on edge servers for provisioning low-latency services to nearby users. However, in the MEC environment, edge servers may suffer from failures while the app vendor has to guarantee continuously available services to its users, thereby securing its revenue for application instances deployed. In this paper, we focus on available service provisioning when cost-effectively deploying application instances on edge servers. We first formulate a novel A vailability-aware R evenue-effective A pplication D eployment (ARAD) problem in the MEC environment with the aim to maximize the overall revenue by considering both service availability benefit and deployment cost. We prove that the ARAD problem is

$\mathcal {NP}$

-hard. Then, we propose an approximation algorithm named ARAD-A to find the ARAD solution efficiently with a constant approximation ratio of

$\frac{1}{2}$

. We extensively evaluate the performance of ARAD-A against five representative approaches. Experimental results demonstrate that our ARAD-A can achieve the best performance in securing the app vendor's overall revenue.

查看原文本刊更多论文

在多接入边缘计算中部署具有可用性意识的高收益应用

多接入边缘计算（MEC）已成为将计算资源和服务推向网络边缘的一种前景广阔的计算模式。它允许在边缘服务器上部署应用程序/服务，为附近的用户提供低延迟服务。然而，在 MEC 环境中，边缘服务器可能会出现故障，而应用程序供应商必须保证向用户提供持续可用的服务，从而确保所部署应用程序实例的收入。在本文中，我们将重点关注在边缘服务器上经济高效地部署应用实例时的可用服务供应。我们首先在 MEC 环境中提出了一个新颖的 "可用性感知的高收益应用部署（ARAD）"问题，旨在通过同时考虑服务可用性收益和部署成本来实现整体收益最大化。我们证明了 ARAD 问题的难度为 $\mathcal {NP}$。然后，我们提出了一种名为 ARAD-A 的近似算法，可以高效地找到 ARAD 解，其近似率恒定为 $\frac{1}{2}$。我们将 ARAD-A 与五种代表性方法进行了广泛的性能评估。实验结果表明，我们的 ARAD-A 在确保应用程序供应商的整体收入方面可以达到最佳性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

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.