论文标题
考虑公平性的云上基于聚类的调度的多瞄准性优化
Multi-objective Optimization of Clustering-based Scheduling for Multi-workflow On Clouds Considering Fairness
论文作者
论文摘要
分布式计算（例如云计算）提供了有希望的平台来执行多个工作流程。工作流程调度在具有多目标要求的多工作流执行中起重要作用。尽管存在许多多目标调度算法，但它们主要集中于优化单个工作流程的制造和成本。对于多工作流程计划的多目标优化的研究有限。考虑到多工作流程计划，还有一个其他关键目标可以使用资源维护工作流的公平性。为了解决此类问题，本文首先根据MakePAN，成本和公平定义了新的多目标优化模型，然后提出了用于资源分配的基于全局聚类的多工作流程计划策略。实验结果表明，所提出的方法的性能优于比较算法，而没有明显妥协整体制造商和成本以及个人公平性，这可以指导云上的仿真工作流程计划。
Distributed computing, such as cloud computing, provides promising platforms to execute multiple workflows. Workflow scheduling plays an important role in multi-workflow execution with multi-objective requirements. Although there exist many multi-objective scheduling algorithms, they focus mainly on optimizing makespan and cost for a single workflow. There is a limited research on multi-objective optimization for multi-workflow scheduling. Considering multi-workflow scheduling, there is an additional key objective to maintain the fairness of workflows using the resources. To address such issues, this paper first defines a new multi-objective optimization model based on makespan, cost, and fairness, and then proposes a global clustering-based multi-workflow scheduling strategy for resource allocation. Experimental results show that the proposed approach performs better than the compared algorithms without significant compromise of the overall makespan and cost as well as individual fairness, which can guide the simulation workflow scheduling on clouds.
