The workflow scheduling problem is a fundamental task in cloud computing. This paper addresses the challenge of workflowscheduling in dynamic and uncertain cloud environments, where computing resources may become ina...
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ISBN:
(纸本)9798350354102;9798350354096
The workflow scheduling problem is a fundamental task in cloud computing. This paper addresses the challenge of workflowscheduling in dynamic and uncertain cloud environments, where computing resources may become inaccessible due to hardware or software failures. To tackle this challenge, we propose a novel algorithm called the Order Feature Guided Multi-Population (OFGMP) algorithm for dynamic workflowscheduling in cloud environments. The OFGMP algorithm utilizes a multi-population evolutionary framework, incorporating a knowledge-guided reproduction operator that leverages the order feature of solutions, as well as repair mechanisms to adapt to changing environmental conditions. Extensive experiments are conducted to validate the algorithm's performance against existing dynamic scheduling approaches. The experimental results demonstrate the superiority of our proposed method over others on a number of test cases.
There is no doubt that cloud computing provides a simple but efficient way to share computational resources we have for an information system. Developing a highly effective method for solving the workflowscheduling p...
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ISBN:
(纸本)9781538666500
There is no doubt that cloud computing provides a simple but efficient way to share computational resources we have for an information system. Developing a highly effective method for solving the workflow scheduling problem (WSP) is one critical issue for enhancing the performance of such a system that requires to minimize the execution time, rent costs of resources, and transmission costs of data in a cloud workflow management system. This paper presents an efficient search algorithm, which is an extended version of search economics (SE), to solve WSP in a cloud computing environment, called search economics for workflowscheduling algorithm (SEWSA). The proposed algorithm inherits the distinguishing features of SE that uses not only the objective value to make a decision for the later search directions but also the potential of the subsolution space. The experimental results show that the proposed algorithm can find a better scheduling strategy to optimize the makespan time and to reduce the total cost than other metaheuristic algorithms.
Estimation of the execution time is an important part of the workflow scheduling problem. The aim of this paper is to highlight common problems in estimating the workflow execution time and propose a solution that tak...
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ISBN:
(纸本)9781479970674
Estimation of the execution time is an important part of the workflow scheduling problem. The aim of this paper is to highlight common problems in estimating the workflow execution time and propose a solution that takes into account the complexity and the randomness of the workflow components and their runtime. The solution proposed in this paper addresses the problems at different levels from task to workflow, including the error measurement and the theory behind the estimation algorithm. The proposed estimation algorithm can be integrated easily into a wide class of schedulers as a separate module. We use a dual stochastic representation, characteristic / distribution functions, in order to combine tasks' estimates into the overall workflow makespan. Additionally, we propose the workflow reductions - the operations on a workflow graph that do not decrease the accuracy of the estimates, but simplify the graph structure, hence increasing the performance of the algorithm.
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