In Cloud Computing model, users are charged according to the usage of resources and desired Quality of Service (QoS). multi-objective task scheduling problem based on desired QoS is an NP-Complete problem. Due to the ...
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In Cloud Computing model, users are charged according to the usage of resources and desired Quality of Service (QoS). multi-objective task scheduling problem based on desired QoS is an NP-Complete problem. Due to the NP-Complete nature of taskscheduling problems and huge search space presented by large scale problem instances, many of the existing solution algorithms cannot effectively obtain global optimum solutions. In this paper, a chaotic symbiotic organisms search (CMSOS) algorithm is proposed to solve multi-objective large scale taskscheduling optimization problem on IaaS cloud computing environment. Chaotic optimization strategy is employed to generate initial ecosystem (population), and random sequence based components of the phases of SOS are replaced with chaotic sequence to ensure diversity among organisms for global convergence. In addition, chaotic local search strategy is applied to Pareto Fronts generated by SOS algorithms to avoid entrapment in local optima. The performance of the proposed CMSOS algorithm is evaluated on CloudSim simulator toolkit, using both standard workload traces and synthesized workloads for larger problem instances of up to 5000. Moreover, the performance of the proposed CMSOS algorithm was found to be competitive with the existing with the existing multi-objective task scheduling optimization algorithms. The CMSOS algorithm obtained significant improved optimal trade-offs between execution time (makespan) and financial cost (cost) with no computational overhead. Therefore, the proposed algorithms have potentials to improve the performance of QoS delivery.
taskscheduling in cloud computing environments is a multi-objective optimization problem, which is NP hard. It is also a challenging problem to find an appropriate trade-off among resource utilization, energy consump...
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taskscheduling in cloud computing environments is a multi-objective optimization problem, which is NP hard. It is also a challenging problem to find an appropriate trade-off among resource utilization, energy consumption and Quality of Service(QoS) requirements under the changing environment and diverse tasks. Considering both processing time and transmission time, a PSO-based Adaptive multi-objective task scheduling(AMTS) Strategy is proposed in this paper. First, the taskscheduling problem is formulated. Then, a taskscheduling policy is advanced to get the optimal resource utilization, task completion time, average cost and average energy consumption. In order to maintain the particle diversity, the adaptive acceleration coefficient is adopted. Experimental results show that the improved PSO algorithm can obtain quasi-optimal solutions for the cloud taskscheduling problem.
In order to solve the problem that the traditional scheduling algorithm of sensor network node is constrained by the energy of the node itself, this paper proposes a new scheduling algorithm of sensor network node bas...
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In order to solve the problem that the traditional scheduling algorithm of sensor network node is constrained by the energy of the node itself, this paper proposes a new scheduling algorithm of sensor network node based on artificial neural network (ANN). Aiming at the sensor network of ANN, a multi-objective task scheduling model is established. The optimal solution of taskscheduling is obtained by particle swarm optimisation algorithm. The energy balance degree is set as the final decision-making index, and the energy consumption of the optimal solution centralised node is chosen as the final taskscheduling strategy to complete the scheduling of sensor network nodes. The experimental results show that the proposed algorithm has higher coverage and lower energy consumption in the scheduling process, which has certain advantages.
We propose a multi-objective optimization algorithm for cloud taskscheduling based on the Analytic network process(ANP) model to solve the problems in cloud taskscheduling,such as the deficiencies of mathematical de...
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We propose a multi-objective optimization algorithm for cloud taskscheduling based on the Analytic network process(ANP) model to solve the problems in cloud taskscheduling,such as the deficiencies of mathematical description,limited optimization abilities of the traditional multi-objective optimization algorithm and the selection of the Pareto optimal ***,we present the mathematical description of cloud taskscheduling using matrix ***,the improved Nondominated sorting genetic algorithm II(NSGA-II) multiobjective evolutionary algorithm whose optimization ability is improved by Gene expression programming(GEP)algorithm has been introduced into the cloud taskscheduling field to search the Pareto set among ***,ANP model has been combined with the improved NSGA-II to solve the selection problems of Pareto *** with the multi-objective optimization algorithm based on the weighted polynomial,the proposed algorithm can optimize multiple goals at the same time,and can avoid the additional iterations due to the change of users preferences *** simulation results indicate that the proposed algorithm is effective.
Cloud computing is the advancing technology that aims at providing services to the customers with the available resources in the cloud environment. When the multiple users request service from the cloud server, there ...
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Cloud computing is the advancing technology that aims at providing services to the customers with the available resources in the cloud environment. When the multiple users request service from the cloud server, there is a need of the proper scheduling of the resources to attain good customer satisfaction. Therefore, this paper proposes the Regressive Whale Optimization (RWO) algorithm for workflow scheduling in the cloud computing environment. RWO is the meta-heuristic algorithm, which schedules the task depending on a fitness function. Here, the fitness function is defined based on three major constraints, such as resource utilization, energy, and the Quality of Service (QoS). Therefore, the proposed taskscheduling requires minimum time and cost for executing the task in the virtual machines. The performance of the proposed method is analyzed using the four experimental setups, and the results of the analysis prove that the proposed multi-objective task scheduling method performs well than the existing methods. The evaluation metrics considered for analyzing the performance of the proposed workflow scheduling method are resource utilization, energy, cost, and time. Resource utilization is the process of making the most of the resources available for performing tasks. Energy is the quantitative property of the resource to perform tasks. The proposed method attains the maximum resource utilization at a rate of 0.0334, minimal rate of energy, scheduling cost, and time as 0.2291, 0.0181, and 0.0007, respectively.
In this paper, we have proposed a Bayesian optimization based novel approach for multi-objective task scheduling in real-time heterogeneous multiprocessor systems. taskscheduling problem in multi-processor real-time ...
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ISBN:
(纸本)9781509006229
In this paper, we have proposed a Bayesian optimization based novel approach for multi-objective task scheduling in real-time heterogeneous multiprocessor systems. taskscheduling problem in multi-processor real-time systems is a NP-hard problem. In such systems, scheduling of tasks becomes a huge challenge for the scheduler designers;especially when tasks are inter-dependent and have deadline constraints. Interdependent or precedence-constrained tasks are often represented as directed acyclic graphs. Most of the real life applications require state of the art planning and scheduling schemes for safer and efficient operations. Thus, we propose the algorithm `moBOA-RTS' (multi-objective Bayesian optimization algorithm for real time scheduling) to find an optimal schedule satisfying all the constraints within reasonable time. Here, learning of task graph is made through Bayesian networks. At first, tasks are allocated to different processors and then LDF (latest deadline first) based priority is used to determine the task execution on individual processors. The proposed approach can be applied on many processor real-time systems, where both the scenarios viz. homogenous and heterogeneous processing environments are prevalent. Experimental analysis shows that our approach produces optimal decisions for feasible scheduling that ensures the compliance of all real time and precedence related constraints.
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