Mobile cloud computing(MCC) combines mobile Internet and cloud computing to improve the performance of mobile applications. However, MCC faces the problem of energy efficiency because of randomly varying channels. A scheduling algorithm is proposed by introducing the Lyapunov optimization, which can dynamically choose users to transmit data based on queue backlog and channel statistics. The Lyapunov analysis shows that the proposed scheduling algorithm can make a tradeoff between queue backlog and energy consumption in the channel-aware mobile cloud computing system. The simulation results verify the effectiveness of the proposed algorithm.
针对移动云计算中的虚拟机(virtual machine,VM)管理问题,提出了一种VM定价与分配方案(VM pricing and allocation,VMPA)。该方案考虑了业务量引起网络拥塞对用户效用的影响,根据Stackelberg博弈模型对VM的定价和分配进行了分析,证明了纳什均衡点的存在性和唯一性,给出了最优的静态VM价格及分配,并利用粒子群算法搜索最优的动态VM价格及分配。仿真结果表明,该方案能快速获得最优的VM价格及分配,可有效地控制小区中的业务量,减少网络拥塞,能同时优化云提供商和用户的效用。
针对移动云计算中的虚拟机(virtual machine,VM)调度问题,考虑无线带宽限制对VM调度的影响,以云提供商的系统效益为目标函数,根据拍卖机制提出了一种带宽受限的VM动态调度(bandwidth-constrainted VM dynamic scheduling,BVMDS)算法。该算法首先根据用户的出价来判定拍卖成功方,然后根据拍卖成功方对计算资源的需求来配置VM,最后采用临界支付的方式来计算拍卖成功方的实际支付价格。仿真结果表明,算法能够有效地改善云提供商的系统效益和资源利用率。
