首页|面向利润优化的软实时云服务调度与多服务器系统配置方法研究

面向利润优化的软实时云服务调度与多服务器系统配置方法研究

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在云计算中,由于多核技术的不断革新,近年来有许多工作研究了基于多核处理器的多服务器系统.云服务提供商通过建立多服务器系统给用户提供云服务并优化云服务利润是目前云计算领域的一个热点问题,对这些问题的研究推动着云计算技术的不断发展.然而,现有的关于多服务器系统的研究要么局限于通过对多服务器计算资源的配置来优化云服务利润而忽视了云服务请求本身的可调度性,要么局限于开发服务请求调度策略来提升云服务利润而忽视了多服务器系统的动态扩展性.但若使用云服务请求调度与多服务器配置协同优化来提升云服务利润,则会使问题规模的复杂性呈指数增长.因此,为云服务提供商设计一个面向软实时云服务请求的云服务调度与多服务器配置方法是十分必要的.此外,现有的研究在配置多服务器系统时大多忽略了处理云服务请求会遭受瞬时故障的情况.而许多研究表明,软实时任务在遭受瞬时故障时会影响服务请求的执行结果从而影响云服务利润.本研究面向软实时云服务请求,针对云环境中普遍存在的计算性能异构的服务器资源,开发了一个基于深度搜索的灰狼算法来协同优化云服务请求调度和多服务器配置以最大化云服务利润.最后,为了验证所提方法的有效性,进行了大量实验,实证结果表明,与现有的基准方法相比,所提方法得到的云服务利润平均增加了6.83％.
Soft Real-time Cloud Service Request Scheduling and Multiserver System Configuration for Profit Optimization
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 profit-ability is a hot topic in cloud computing.Research on these issues drives the continuous development of cloud computing technolo-gy.However,existing studies on multi-server systems either focus on optimizing cloud service profitability through the configura-tion of multi-server computing resources,neglecting the schedulability of cloud service requests themselves,or concentrate on de-veloping 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.Be-sides,existing research on configuring multi-server systems often overlooks the transient faults in processing cloud service re-quests.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 envi-ronments,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 ob-tained by the proposed method increase by an average of 6.83％.

Cloud computingMulti-server system configurationCloud service request schedulingCloud service profit maximiza-tionSoft error reliability

王添、沈伟、张功萱、徐林丽、王震、郁云

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江苏财会职业学院信息工程学院 江苏连云港 222061

阿里云(中国) 杭州 310030

南京理工大学计算机科学与工程学院 南京 210094

江苏海洋大学理学院 江苏连云港 222061

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云计算 多服务器系统配置 云服务请求调度 云服务利润最大化 软错误可靠性

国家自然科学基金教育部产学研创新基金江苏省高等学校哲学社会科学研究项目连云港市基础研究计划江苏财会职业学院科研启动基金江苏财会职业学院基础科研项目

622722322022IT2242023SJYB1851JCYJ23282023GC062023XJ19

2024

10.11896/jsjkx.230900099

计算机科学
重庆西南信息有限公司（原科技部西南信息中心）

计算机科学

CSTPCD北大核心
影响因子：0.944
ISSN：1002-137X
年,卷(期)：2024.51(z1)
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