西安交通大学电子与信息工程学院,西安,710049
网络首发:2016-01-10,
纸质出版:2016
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陈鹏飞, 李昕怡, 齐勇, 等. 单步启发式策略的备份虚拟机复用策略[J]. 西安交通大学学报, 2016,50(1):100-107.
Multiplexing of Backup Virtual Machine Based on Single-Step Heuristic Policy[J]. 2016, 50(1): 100-107.
陈鹏飞, 李昕怡, 齐勇, 等. 单步启发式策略的备份虚拟机复用策略[J]. 西安交通大学学报, 2016,50(1):100-107. DOI: 10.7652/xjtuxb201601016.
Multiplexing of Backup Virtual Machine Based on Single-Step Heuristic Policy[J]. 2016, 50(1): 100-107. DOI: 10.7652/xjtuxb201601016.
针对云环境中的备份虚拟机(VM)利用率过低的问题
提出了基于不停歇多臂赌博机(RMAB)方法的备份VM分时复用策略
并给出了获得最优解的条件。该策略将每个备份VM形式化为具有“空闲”(1)和“占用”(0)两种状态的Markov过程
将多个备份VM的调度问题形式化为具有多个Markov过程的Markov决策问题(MDP)
最终目标是期望在有限的备份VM数量下
最大化备份VM的利用率同时保证系统整体的可用性不会明显降低。然而
利用传统的动态规划方法求解该问题时会出现维度爆炸的现象
从而导致问题不可解
故将该Markov决策问题转化为RMAB问题
然后利用简单易操作的单步启发式算法进行求解
并通过计算单步最优获得长期最优解
在特定条件下该策略可以保证得到的解为最优解。模拟实验结果表明:所提方法将备份VM与服务VM之间的备份比例从1:1扩展成1:M(M1)
同时保证失效VM的恢复比率不低于96%
相应地备份VM的利用率显著提高; 在VM失效率较低的条件下
备份VM利用率比1:1备份时提高了89%; 利用该备份VM调度策略
有助于减少整个云计算平台的建设和运维费用。
Based on the restless multi-arm bandit(RMAB)approach
a multiplexing strategy of backup virtual machines(VMs)is proposed to resolve the problem of low utilization of backup VMs in the cloud environment
and the optimal condition is given. This strategy regards an individual backup VM as a Markov process with two states
namely “idle”(1)and “backup”(0)
and models the scheduling of multiple backup VMs as a Markov decision problem(MDP)consisting of multiple Markov processes. The goal of this strategy is to maximize the utilization of backup VMs without obvious reduction in the system availability under the constraint of limited backup VMs. However
this problem is computationally intractable with traditional dynamic programming methods due to the curse of dimensionality. Therefore
this paper transforms the original MDP problem to a RMAB problem and adopts a simple single-step heuristic policy to resolve it. By calculating the single-step optimal solution
the long-term optimal solution can be obtained. Under specific conditions
the optimal solution of this strategy is guaranteed. The results of simulation experiments show that the proposed policy can achieve the goal of extending the backup ratio between backup VMs to service VMs from 1:1 to 1:M(M 1)while the failed VM assurance rate is no lower than 96%. Correspondingly
the utilization of backup resources is significantly enhanced. When the failure rate of service VM is low
the utilization of backup resources can be raised 89% compared with the 1:1 backup. The building and operation costs of a cloud platform can be reduced with the help of this backup VM scheduling strategy.
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