基于Q学习和双向ACO算法的云计算任务资源分配模型设计

Design of Task-Resource Allocation Model Based on Q-Learning and Double Orientation ACO Algorithm for Cloud Computing

摘要:

云计算异构环境中由于计算和存储资源物理分布的不一致性,往往容易导致在应用传统的调度算法进行任务资源分配时存在调度效率低和负载不均衡的问题,为此,设计了一种基于Q学习和双向ACO算法的云计算任务资源分配模型；首先,引入了基于主从结构的调度模型,并综合考虑任务计算完成时间、网络带宽和延迟等因素设计了资源分配目标函数,然后,设计了基于Q学习的云计算资源初始分配方法,将其获得的最优策略对应的Q值初始化网络中节点的Q值,最后,设计一种结合前向蚂蚁和后向蚂蚁的双向ACO算法实现任务资源的最终分配,并对算法进行了定义和描述；在CloudSim环境下进行仿真实验,结果证明文中方法能有效实现云计算异构环境下的任务资源分配,且与其它方法相比,负载均衡离差值平均约为0.071 5,是一种适用于云计算异构环境的有效资源分配方法。 

Abstract:

Owning to the inconsistency distribution of computing and storage resources in the cloud computing heterogeneous environment, and easily leading to the low scheduling efficiency and load unbalance when using the traditional scheduling algorithm to allocate the resource. Firstly, the scheduling model based on main-minor structure is introduced and the goal function is defined by considering the finishing time of computing, network bandwidth and delay, then the initial allocation method based on Q-learning is designed, and the Q value from the optimal strategy is used to initialize the Q value of the nodes in the network. Finally, the double orientation ACO algorithm based on front ant and back ant is proposed to realize the resource allocation, and the algorithm is defined and described. The simulation experiment in the CloudSim environment, and the result shows the method in this paper can realize the task-resource allocation, and compared with the other methods, it has the advantage of accurate predicting time and high load balance, therefore, it is an effective resource allocating method for cloud computing heterogeneous environment.