Research On Task Scheduling Strategy Of Cloud Computing

Cloud computing system has a large number of servers and broad users, and it has toschedule and manage all kinds of application tasks frequently. Thus, so as to obtain a betterscheduling result in a relatively balanced state of system load, how to achieve reasonableallocation of computing resources and complete efficient scheduling execution of a largenumber of application tasks, has became a hot topic in the field of cloud computing. A certaindegree of similarity between the task scheduling strategies of cloud computing andconventional distributed computing system makes that the majority of the existing taskscheduling strategies of cloud computing are developed or improved on the basis of the taskscheduling methods of conventional distributed environment, which leads to some limitations.In addition, as a commercial service, so as to improve its service performance, cloudcomputing system should not only take the optimization of its task scheduling strategy intoconsideration, but also pay attention to the service revenue of cloud service providers. Atpresent, some discussions are appeared on this point, but there is no mature method. Therefore,it is valuable in theory and significant in practice to study on the task scheduling strategy ofcloud computing for improving the service performance of cloud computing system.On this basis, this dissertation analyzes and studies the task scheduling strategies of cloudcomputing systemically and roundly. The main research content of this dissertation includesthrees aspects, which are the problem of the QoS objective constraint of application taskssubmitted by system users, the executing efficiency of task scheduling strategy and theservice revenue of cloud service providers in the cloud computing environment. After theprofound study on the above problems, a task scheduling architecture is given at the end ofthis dissertation in cloud computing environment.The main research works and innovations of this dissertation are as follows:(1) Focusing on the scheduling issue of a large number of application tasks with differentQoS objective constraint requirements in the cloud computing environment, a multi-QoSobjective constrained task scheduling strategy of cloud computing is proposed. Thecomputing resources are dynamical and variable in cloud computing environment, and thepreferences of users are also diversiform, and moreover, the QoS objective constraintrequirements of application tasks always involve more than one parameter. At the same time,the meeting degree to the QoS objective constraint requirements of application tasks affectsthe performance of cloud task scheduling strategy seriously. Focusing on the different QoSobjective constraint requirements of a large number of application tasks, the proposed method respectively constructs the corresponding QoS objective constraint condition, and then use aconstructed subjection degree function to transform the multi-QoS objective constrained issueinto a single objective constrained optimization issue. Compared with traditional methods, theproposed method in this dissertation achieves a better scheduling result in the measurementsof the violating ratio of deadline, the average scheduling makespan, and the average taskexecution cost, under the condition of satisfying the multi-QoS objective constraintrequirements of application tasks.(2) Focusing on the scheduling efficiency of a large number of application tasks in cloudcomputing environment, a genetic-ant colony optimization algorithm based task schedulingstrategy of cloud computing is put forwarded. How to achieve efficient scheduling andexecution of numerous application tasks in cloud computing environment and obtain a betterscheduling result in a shorter makespan for every task, is a technical difficulty in the field ofcloud computing. A fast global search is executed at first based on genetic algorithm in theproposed task scheduling strategy, and then the global searching information obtained at theend of the initial search stage is transformed into the initialization of pheromone of ant colonyalgorithm. Finally, we get a satisfactory scheduling solution of application tasks based on theant colony optimization algorithm. The proposed method integrates the fast global searchcapability of genetic algorithm and the high solving precision of ant colony optimizationalgorithm, whereas, it avoids the deficient local search capability of genetic algorithm, andalso overcomes the inefficiency of ant colony optimization algorithm at its initial search stage.In a large-scale application task scheduling scene, the proposed method improves thescheduling efficiency of a large number of application tasks effectively as well as remains arelatively balanced system load in cloud computing environment.(3) In order to improve the service revenue of cloud computing system, a servicecost-driven task scheduling strategy of cloud computing is proposed from the standpoint ofcloud service providers. The majority of the existing task scheduling strategies of cloudcomputing aim at meeting the resource requests and QoS objective constraint requirements ofapplication tasks from the view of system users, rather than paying more attention to theservice revenue of cloud service providers. As a kind of commercial service, cloud computingsystem should try its best to improve its service revenue. In the proposed service cost-drivencloud computing task scheduling strategy, the scheduling expenditure becomes an importantconsideration when cloud users send their scheduling requests of application tasks, and oncondition that the resource requests and QoS objective constraint requirements of applicationtasks are satisfied, the users will prefer to choose the cheaper computing resources rather than choose those high-powered ones, which makes the scheduling decision of application tasksmore reasonable and efficient, and the management and allocation of computing resourcesmore fair and economical. The proposed method improves the resource utility of cloudcomputing system and the service revenue of cloud service providers in a certain extent, andultimately promotes the healthy and sustainable development of cloud computing market.(4) A task scheduling architecture is proposed in the cloud computing environment bytaking all kind of performance evaluation criterions of cloud task scheduling issue intoconsideration. The existing studies on the task scheduling issue of cloud computing are oftenbased on a certain assumption, and emphasize particularly on one or more given performanceevaluation criterions, which leads that the corresponding performance testing results are alitter subjective and one-sided. All kind of functional modules are definitely defined and allthe attribute parameters of both application tasks and processing elements are also describedin detail in the proposed task scheduling architecture of this dissertation, in which, thetechnical developer just instantiate the corresponding attribute parameters based on thespecific testing requirements and scheduling objectives, rather than making any conditionassumption when demonstrating and evaluating a given task scheduling strategy of cloudcomputing. In addition, a dynamic data replica management strategy is introduced in theproposed task scheduling architecture due to the frequent mistakes occurred in cloudcomputing system, which not only improves the reliability and availability of cloudcomputing system, but also improves its data file accessing efficiency and the balance level ofsystem load.