论文标题
在动态市场中多云基础设施经纪的多目标方法
A Multi-Objective Approach for Multi-Cloud Infrastructure Brokering in Dynamic Markets
论文作者
论文摘要
云服务经纪人（CSB）促进了复杂的资源分配决策，有效地将动态租户要求映射到动态提供商提供的报价中，理想地考虑了几个目标。这项工作首次提出了针对动态环境的面向经纪人的虚拟机放置（VMP）问题的纯多目标公式，同时优化了以下目标函数：（i）总基础结构CPU（TICPU），（ii）总基础架构存储器（ii）总体基础设施（III）总额（iii）总额（iii）总额（IIIII）总额（III）。为了解决公式的多目标问题，提出了多目标进化算法（MOEA）。考虑到每次发生需求（或要约）发生变化时，基于帕累托的算法发现一组非主导的解决方案是作为提出的一种，评估了不同的选择策略，以自动选择便捷的解决方案。此外，在不同情况下，将拟议的算法（包括考虑的选择策略）与单一最先进的替代方案进行了比较，并与实际市场提供商的真实数据进行了比较。实验结果表明，考虑到首选的解决方案选择策略（S3）的纯多目标优化方法（S3）优于其他单型观察性替代方案。
Cloud Service Brokers (CSBs) facilitate complex resource allocation decisions, efficiently mapping dynamic tenant demands onto dynamic provider offers, where several objectives should ideally be considered. This work proposes for the first time a pure multi-objective formulation of a broker-oriented Virtual Machine Placement (VMP) problem for dynamic environments, simultaneously optimizing the following objective functions: (i) Total Infrastructure CPU (TICPU), (ii) Total Infrastructure Memory (TIMEM) and (iii) Total Infrastructure Price (TIP) while considering load balancing across providers. To solve the formulated multi-objective problem, a Multi-Objective Evolutionary Algorithm (MOEA) is proposed. Considering that each time a demand (or offer) change occurs, a set of non-dominated solutions is found by Pareto-based algorithms as the one proposed, different selection strategies were evaluated in order to automatically select a convenient solution. Additionally, the proposed algorithm, including the considered selection strategies, was compared against mono-objective state-of-the-art alternatives in different scenarios with real data from providers in actual markets. Experimental results demonstrate that a pure multi-objective optimization approach considering the preferred solution selection strategy (S3) outperformed other mono-objective evaluated alternatives.
