虚拟化云中随机任务与资源调度方法

摘要

Task and resource scheduling is one of the key technologies for the cloud system.However,the existing research tends to ignore the dynamic nature of real-time tasks and the randomness of task execution time,which makes the pre-generated schedule may not being effective in real execution.To address this issue, a randomness aware scheduling architecture is designed;a heuristic scheduling algorithm,integrating proactive and reactive strategy (PRS),is proposed to schedule tasks dynamically,which improves the ability of the cloud system to guarantee the timeliness of real-time tasks;three strategies are proposed to scale up/down computing resources according to the system load to reduce the energy consumption.Finally,the performance of the algo-rithm PRS is compared with the other four algorithms.The experimental results show that compared with the existing algorithms the performance of the algorithm PRS is improved by 13.85% and 17.23% in terms of gua-rantee ratio and energy consumption.%任务和资源调度方法是云系统的关键技术之一。但是，现有的研究往往忽略实时任务的高动态性和任务执行时间的随机性，使得调度方案的实际性能与期望性能相差甚远。针对以上问题，本文设计一个随机性感知的调度框架；提出一个启发式调度算法集成前摄性和反应式策略（proactive and reactive strategy，PRS）来对任务进行调度，以提高云系统保障实时任务时效性的能力；并提出3个计算资源伸缩策略来动态调整计算资源，以减少能量消耗。最后，通过实验将算法 PRS 的性能与其他4个算法进行比较。实验结果表明，在任务完成率和能耗方面，算法 PRS 的性能比已有算法提高13．85％和17．23％。