动态市场中多云基础设施代理的多目标方法
A Multi-Objective Approach for Multi-Cloud Infrastructure Brokering in Dynamic Markets
摘要：
云服务经纪人（CSB）促进复杂的资源分配决策，有效地将动态租户需求映射到动态提供商产品，理想情况下应考虑多个目标。这项工作首次提出了一个纯粹的多目标制定面向代理的虚拟机布局（VMP）问题的动态环境，同时优化以下目标函数：（i）总基础设施CPU（TICPU），（ii）总基础设施内存（TIMEM）和（iii）总基础设施价格（TIP），同时考虑跨供应商的负载均衡。为了解决这一多目标问题，提出了一种多目标进化算法。考虑到每次需求（或报价）发生变化时，采用Pareto算法寻找一组非支配解，并对不同的选择策略进行评估，以自动选择一个方便的解。此外，所提出的算法，包括所考虑的选择策略，进行了比较，对单目标国家的最先进的替代品在不同的情况下，从实际市场中的供应商的真实数据。实验结果表明，一个纯粹的多目标优化方法考虑的首选解决方案选择策略（S3）优于其他单目标评估的替代品。
英文摘要：
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.
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