云计算环境下的大数据业务可生存性调度策略  

Survivability Scheduling Strategy of Big Data Service in Cloud Computing Environment

作　　者：范佳庆 李罡 成晨雨 武韬 FAN Jiaqing;LI Gang;CHENG Chenyu;WU Tao(School of Information and Control Engineering,Jilin Institute of Chemical Technology,Jilin City 132022,China;School of Mechanical and Control Engineering,Baicheng Normal University,Baicheng 137000,China;China Telecom Jilin Branch,Jilin City 132013,China)

机构地区：[1]吉林化工学院信息与控制工程学院,吉林吉林132022 [2]白城师范学院机械与控制工程学院,吉林白城137000 [3]中国电信吉林市分公司,吉林吉林132013

出　　处：《吉林化工学院学报》2023年第3期26-32,共7页Journal of Jilin Institute of Chemical Technology

基　　金：2022年度吉林省教育厅科学技术研究项目(JJKH20220013KJ);吉林省教育科学“十三五”规划2019年度重点课题(ZD19077)。

摘　　要：云计算可生存性是网络应用安全研究的新方向。在云计算的广域网环境下构建了以动态服务漂移技术和任务调度方法技术的系统可生存性应急响应策略,实现在大规模服务节点情况下服务的动态漂移。将云服务和数据动态分布于多个服务节点,能消除传统服务的单一失效点。为增强服务漂移的触发机制,提出了一种自适应的随机自治调度算法,该算法对比传统的漂移机制增加了触发条件,并引入了蚁群算法进行调度。结论表明,改进后的服务漂移模型的抗毁能力、漂移时间以及漂移效率有了明显的优化,更加保证所提供服务的连续性和可靠性,从而提高云计算环境下的大数据业务可生存性。Cloud computing survivability is a new direction in network application security research.A system survivability emergency response strategy was constructed in the wide area network environment of cloud computing,which employed dynamic service drift technology and task scheduling method technology to achieve dynamic service drift in the case of a large number of service nodes.Dynamically distributing cloud services and data across multiple service nodes can eliminate the single failure point of traditional services.In order to enhance the triggering mechanism of service drift,an adaptive random autonomous scheduling algorithm was proposed,which compared with the traditional drift mechanism,added triggering conditions and introduced ant colony algorithm for scheduling.The conclusions show that the improved service drift model have significantly optimized anti-destruction ability,drift time,and drift efficiency,which can better ensure the continuity and reliability of the services provided,thus improving the survivability of big data service in the cloud computing environment.

关 键 词：云计算 服务漂移 任务调度 蚁群算法 可生存性 

分 类 号：TP393.2[自动化与计算机技术—计算机应用技术]