本文選題：云計算 + 工作流調(diào)度��； 參考：《模式識別與人工智能》2016年09期

【摘要】：云工作流系統(tǒng)研究集中在工作流任務(wù)執(zhí)行的時間效率優(yōu)化,然而時間最優(yōu)的任務(wù)調(diào)度方案可能存在不同能耗,因此,文中求解滿足時間約束時能耗最優(yōu)的調(diào)度方案.首先改進任務(wù)執(zhí)行能耗模型,設(shè)計適用于評價任務(wù)調(diào)度方案執(zhí)行能耗的適應(yīng)度計算方法.然后基于精準(zhǔn)調(diào)整粒子速度的自適應(yīng)權(quán)重,提出解決任務(wù)調(diào)度能耗優(yōu)化問題的自適應(yīng)粒子群算法.實驗表明,文中算法收斂穩(wěn)定,調(diào)度方案執(zhí)行能耗較低.
[Abstract]:The research of cloud workflow system focuses on the optimization of time efficiency of workflow task execution, but the scheduling scheme of task scheduling with optimal time may have different energy consumption. Therefore, the optimal scheduling scheme of energy consumption when meeting the time constraints is solved in this paper. Firstly, the model of task execution energy consumption is improved, and a method for evaluating the performance energy consumption of task scheduling scheme is designed. Then an adaptive particle swarm optimization (APSO) algorithm is proposed to solve the problem of task scheduling energy consumption optimization based on the adaptive weight of accurately adjusting particle velocity. Experiments show that the proposed algorithm is stable in convergence and low in energy consumption.
【作者單位】： 安徽大學(xué)計算機科學(xué)與技術(shù)學(xué)院;
【基金】：國家自然科學(xué)基金項目(No.61672034) 教育部社科研究青年基金項目(No.16YJCZH048) 安徽省教育廳自然科學(xué)研究重點項目(No.KJ2016A024)資助~~
【分類號】：TP18
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本文編號：2091370