{"title":"在多接入边缘计算中提供经济高效且稳健的服务","authors":"Zhengzhe Xiang;Yuhang Zheng;Dongjing Wang;Javid Taheri;Zengwei Zheng;Minyi Guo","doi":"10.1109/TPDS.2024.3435929","DOIUrl":null,"url":null,"abstract":"With the development of multiaccess edge computing (MEC) technology, an increasing number of researchers and developers are deploying their computation-intensive and IO-intensive services (especially AI services) on edge devices. These devices, being close to end users, provide better performance in mobile environments. By constructing a service provisioning system at the network edge, latency is significantly reduced due to short-distance communication with edge servers. However, since the MEC-based service provisioning system is resource-sensitive and the network may be unstable, careful resource allocation and traffic scheduling strategies are essential. This paper investigates and quantifies the cost-effectiveness and robustness of the MEC-based service provisioning system with the applied resource allocation and traffic scheduling strategies. Based on this analysis, a \n<bold>c</b>\nost-\n<bold>e</b>\nffective and \n<bold>r</b>\nobust service provisioning \n<bold>a</b>\nlgorithm, termed \n<monospace>CERA</monospace>\n, is proposed to minimize deployment costs while maintaining system robustness. Extensive experiments are conducted to compare the proposed approach with well-known baseline algorithms and evaluate factors impacting the results. The findings demonstrate that \n<monospace>CERA</monospace>\n achieves at least 15.9% better performance than other baseline algorithms across various instances.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"35 10","pages":"1765-1779"},"PeriodicalIF":5.6000,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Cost-Effective and Robust Service Provisioning in Multi-Access Edge Computing\",\"authors\":\"Zhengzhe Xiang;Yuhang Zheng;Dongjing Wang;Javid Taheri;Zengwei Zheng;Minyi Guo\",\"doi\":\"10.1109/TPDS.2024.3435929\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the development of multiaccess edge computing (MEC) technology, an increasing number of researchers and developers are deploying their computation-intensive and IO-intensive services (especially AI services) on edge devices. These devices, being close to end users, provide better performance in mobile environments. By constructing a service provisioning system at the network edge, latency is significantly reduced due to short-distance communication with edge servers. However, since the MEC-based service provisioning system is resource-sensitive and the network may be unstable, careful resource allocation and traffic scheduling strategies are essential. This paper investigates and quantifies the cost-effectiveness and robustness of the MEC-based service provisioning system with the applied resource allocation and traffic scheduling strategies. Based on this analysis, a \\n<bold>c</b>\\nost-\\n<bold>e</b>\\nffective and \\n<bold>r</b>\\nobust service provisioning \\n<bold>a</b>\\nlgorithm, termed \\n<monospace>CERA</monospace>\\n, is proposed to minimize deployment costs while maintaining system robustness. Extensive experiments are conducted to compare the proposed approach with well-known baseline algorithms and evaluate factors impacting the results. The findings demonstrate that \\n<monospace>CERA</monospace>\\n achieves at least 15.9% better performance than other baseline algorithms across various instances.\",\"PeriodicalId\":13257,\"journal\":{\"name\":\"IEEE Transactions on Parallel and Distributed Systems\",\"volume\":\"35 10\",\"pages\":\"1765-1779\"},\"PeriodicalIF\":5.6000,\"publicationDate\":\"2024-07-30\",\"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/10614854/\",\"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}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Parallel and Distributed Systems","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10614854/","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}
Cost-Effective and Robust Service Provisioning in Multi-Access Edge Computing
With the development of multiaccess edge computing (MEC) technology, an increasing number of researchers and developers are deploying their computation-intensive and IO-intensive services (especially AI services) on edge devices. These devices, being close to end users, provide better performance in mobile environments. By constructing a service provisioning system at the network edge, latency is significantly reduced due to short-distance communication with edge servers. However, since the MEC-based service provisioning system is resource-sensitive and the network may be unstable, careful resource allocation and traffic scheduling strategies are essential. This paper investigates and quantifies the cost-effectiveness and robustness of the MEC-based service provisioning system with the applied resource allocation and traffic scheduling strategies. Based on this analysis, a
c
ost-
e
ffective and
r
obust service provisioning
a
lgorithm, termed
CERA
, is proposed to minimize deployment costs while maintaining system robustness. Extensive experiments are conducted to compare the proposed approach with well-known baseline algorithms and evaluate factors impacting the results. The findings demonstrate that
CERA
achieves at least 15.9% better performance than other baseline algorithms across various instances.
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.