Research On Task Scheduling Strategies For Energy-saving In Heterogeneous Cloud

With the popularity and rapid development of Cloud Computing technology, tasks coming toCloud Computing are infinite variety. In order to meet kinds of demands of tasks, computing nodesin the Cloud Computing platform have to remain open to wait for tasks, which makes energyefficiency of cloud data center has characteristic of low utilization, high waste. As an important partof Cloud Computing platform, task scheduling is the process of tasks directly mapped to resourcesof cloud data center, which directly responds energy cost of data center resources. Therefore, energyoptimization of cloud data center can be implemented by reasonable task scheduling strategy. Clouddata center is usually composed of large-scale heterogeneous computing nodes which are linked bydifferent transmission rates. From the dependence of the division of tasks, this thesis takesenergy-saving independent task scheduling technology and energy-saving dependent taskscheduling technology in heterogeneous cloud computing platform as research objectives. Themajor work in this thesis can be included from three aspects:(1) The existing sources of high-energy consumption of the cloud data center are analyzed, theenergy optimization methods in existing data centers, research status are expounded, the problem ofindependent task scheduling for energy-saving and dependent task scheduling for energy-saving ofheterogeneous Cloud Computing platform are analyzed.(2) Tasks coming to the Cloud Computing are random and computing nodes of CloudComputing have to remain open to wait for tasks, which produces much wasted energy. Anenergy-saving task scheduling algorithm based on vocation queuing model for the CloudComputing is presented. First, task scheduling model of the heterogeneous Cloud Computing isestablished by using exhaustive service, vacation queuing model system. And then, the averageresponse time of tasks and the average power of heterogeneous compute nodes are analyzed byusing the busy period and busy spin under steady state conditions in the Cloud Computing. Afterthat, a task scheduling algorithm based on similar tasks is proposed to reduce energy consumption.Simulation results show that the proposed algorithm can ensure the task performance, and reducethe energy consumption of the Cloud Computing effectively.(3) For the dependent task presented as DAG graph of the cloud computing platform, thisthesis proposes a task scheduling algorithm based on time and energy consumption cost inheterogeneous cloud. During the process of determining scheduling order of tasks, computing factor and communication factor are introduced by integrating time and energy costs to determine theorder of task scheduling; in the phase of selecting the computing nodes for task, computing node’sload is redefined by combining with its ability to calculate task, rate of executing energyconsumption and the amount of tasks in the local task queue. Then the set of computing nodes thatdo not exceed the threshold value of load limit is determined. Task is assigned to the computingnode which can first begin to perform a task in the set of computing nodes and DVFS technique iscombined for voltage regulation. Simulation results show the good energy-saving effect of theproposed algorithm.