软件学报
軟件學報
연건학보
JOURNAL OF SOFTWARE
2013年
8期
1937-1946
,共10页
云计算%虚拟资源评估%熵%动态负载%多目标优化
雲計算%虛擬資源評估%熵%動態負載%多目標優化
운계산%허의자원평고%적%동태부재%다목표우화
cloud computing%virtual resource evaluation%entropy%dynamic load%multi-objective optimization
云资源的动态变化和不确定性给资源管理及任务调度带来了很大的困难.为了准确地掌握资源动态负载和可用能力信息,提出一种基于熵优化和动态加权的资源评估模型,其中,熵优化模型利用最大熵和熵增原理的目标函数及约束条件,筛选出满足用户 QoS 和系统最大化的资源,实现最优调度,保障用户 QoS.对筛选后的资源再进行动态加权负载评估,对负载过重及长期不可用资源进行迁移、释放等,可减少能耗,实现负载均衡和提高系统利用率.设计了仿真实验,以验证所提评估模型的性能.实验结果表明,熵优化模型对用户 QoS 和系统最大化有很好的效果,动态加权负载评估有利于均衡负载,提高系统利用率.该评估模型实现了用户QoS保障、减少能耗、负载均衡以及提高系统利用率等多目标的优化.
雲資源的動態變化和不確定性給資源管理及任務調度帶來瞭很大的睏難.為瞭準確地掌握資源動態負載和可用能力信息,提齣一種基于熵優化和動態加權的資源評估模型,其中,熵優化模型利用最大熵和熵增原理的目標函數及約束條件,篩選齣滿足用戶 QoS 和繫統最大化的資源,實現最優調度,保障用戶 QoS.對篩選後的資源再進行動態加權負載評估,對負載過重及長期不可用資源進行遷移、釋放等,可減少能耗,實現負載均衡和提高繫統利用率.設計瞭倣真實驗,以驗證所提評估模型的性能.實驗結果錶明,熵優化模型對用戶 QoS 和繫統最大化有很好的效果,動態加權負載評估有利于均衡負載,提高繫統利用率.該評估模型實現瞭用戶QoS保障、減少能耗、負載均衡以及提高繫統利用率等多目標的優化.
운자원적동태변화화불학정성급자원관리급임무조도대래료흔대적곤난.위료준학지장악자원동태부재화가용능력신식,제출일충기우적우화화동태가권적자원평고모형,기중,적우화모형이용최대적화적증원리적목표함수급약속조건,사선출만족용호 QoS 화계통최대화적자원,실현최우조도,보장용호 QoS.대사선후적자원재진행동태가권부재평고,대부재과중급장기불가용자원진행천이、석방등,가감소능모,실현부재균형화제고계통이용솔.설계료방진실험,이험증소제평고모형적성능.실험결과표명,적우화모형대용호 QoS 화계통최대화유흔호적효과,동태가권부재평고유리우균형부재,제고계통이용솔.해평고모형실현료용호QoS보장、감소능모、부재균형이급제고계통이용솔등다목표적우화.
The dynamic and uncertainty of cloud resource makes resource allocation and task scheduling more difficult. In order to retrieve accurate resource information about dynamic loads and available capacity, this study proposes a resource evaluation model based on entropy optimization and dynamic weighting. The entropy optimization filters the resources that satisfy user QoS and system maximization by goal function and constraints of maximum entropy and the entropy increase principle, which achieves optimal scheduling and satisfied user QoS. Then the evaluation model evaluates the load of having filtered resources by dynamic weighted algorithm. In order to reduce energy consumption, achieve load balancing and improve system utilization, the study allows the migration or release the resources which overload and unavailable for a long time. Experimental results show the effect of entropy optimization on user QoS and system maximization, and dynamic weighted algorithm benefits load balancing and system utilization. The experimental results prove that the evaluation model achieves multi-objective optimization such as satisfying user QOS, reducing energy assumption, balancing load, improving system utilization and so on.