Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (6A): 230900099-10.doi: 10.11896/jsjkx.230900099

• Computer Software & Architecture • Previous Articles     Next Articles

Soft Real-time Cloud Service Request Scheduling and Multiserver System Configuration for ProfitOptimization

WANG Tian1, SHEN Wei4, ZHANG Gongxuan2, XU Linli3, WANG Zhen1, YUN Yu1   

  1. 1 School of Information Science,Jiangsu College of Finance and Accounting,Lianyungang,Jiangsu 222061,China
    2 School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China
    3 School of Science,Jiangsu Ocean University,Lianyungang,Jiangsu 222061,China
    4 Alibaba Cloud,Hangzhou 310030,China
  • Published:2024-06-06
  • About author:WANG Tian,born in 1990,Ph.D,lectu-rer,is a member of CCF(No.A6921M).His main research interests include cloud computing,edge computing,and hyperphysical system.
    WANG Zheng,born in 1972,master,professor. His main research interests include computer application technology,big data and e-commerce,and computer systems.
  • Supported by:
    National Natural Science Foundation of China(62272232),Industry University Research Innovation Foundation of the Ministry of Education of China(2022IT224),College Philosophy and Social Science Foundation of Jiangsu(2023SJYB1851),Lianyungang Research Foundation for Basic Research(JCYJ2328),Science Research Staring Foundation of Jiangsu College of Finance and Accounting(2023GC06) and Basic Research Foundation of Jiangsu College of Finance and Accounting(2023XJ19).

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Abstract: In cloud computing,there has been considerable research on multi-server systems based on the continuous innovation of multi-core technology.Establishing multi-server systems to provide cloud services to users and optimizing cloud service profitability is a hot topic in cloud computing.Research on these issues drives the continuous development of cloud computing technology.However,existing studies on multi-server systems either focus on optimizing cloud service profitability through the configuration of multi-server computing resources,neglecting the schedulability of cloud service requests themselves,or concentrate on developing service request scheduling strategies to improve cloud service profitability while overlooking the dynamic scalability of multi-server systems.However,when using coordinated optimization of cloud service scheduling and multi-server configuration to enhance cloud service profitability,the complexity of the problem increases exponentially.Therefore,it is essential to design a cloud service scheduling and multi-server configuration method for providers targeting soft real-time cloud service requests.Besides,existing research on configuring multi-server systems often overlooks the transient faults in processing cloud service requests.Numerous studies have demonstrated that soft real-time tasks can be affected by transient faults,leading to variations in the execution results of service requests and impacting cloud service profitability.In this study,we focus on soft real-time cloud service requests and develop a depth-search-based grey wolf algorithm to jointly optimize cloud service request scheduling and multi-server configuration,considering the prevalent computational performance heterogeneity of server resources in cloud environments,aiming to maximize cloud service profitability.Finally,extensive experiments validate the effectiveness of the proposed method.The empirical results demonstrate that,compared with the existing benchmark methods,the cloud service profits obtained by the proposed method increase by an average of 6.83%.

Key words: Cloud computing, Multi-server system configuration, Cloud service request scheduling, Cloud service profit maximization, Soft error reliability

CLC Number: 

  • TP391
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