Abstract
In cloud computing resource allocation, Virtual Machine Placement (VMP) is one of the most studied problems with several possible formulations and different optimization criteria. The present work summarizes a doctoral dissertation focused on studying Many-Objective Virtual Machine Placement (MaVMP) problems. As first contributions, novel taxonomies were proposed for VMP problems in cloud computing environments, in order to gain a systematic understanding of the existing approaches. Additionally, first formulations of MaVMP problems were proposed in: (1) static MaVMP for initial placement, (2)semi-dynamic MaVMP with recon guration of VMs and (3) dynamic two-phase MaVMP for complex cloud computing environments under uncertainty. Considering the novelty of the proposed formulations, several methods and algorithms were also proposed to address main identi ed issues on solving each particular MaVMP problem. Experimental results prove the correctness, effectiveness and scalability of the proposed methods and algorithms in different experimental scenarios even when comparing to state-of-the-art alternatives.
Highlights
Significant research challenges for delivering computational resources as a fifth utility has already been identified [1]
The research summarised in this paper focused on resource allocation, in one of the most studied problems for resource allocation in cloud computing datacenters: the process of selecting which virtual machines (VMs) should be hosted at each physical machine (PM) of a cloud computing infrastructure, known as Virtual Machine Placement (VMP)
This section focuses on summarizing experimental results related to the evaluation of the lower and upper bounds proposed to address issues associated to ManyObjective Virtual Machine Placement (MaVMP) problems for initial placement previously described
Summary
Significant research challenges for delivering computational resources as a fifth utility (like water, electricity, gas, and telephony) has already been identified [1]. Determining which solution to keep and which to discard in order to converge toward the Pareto set is still a relevant issue to be addressed [12], making more difficult to solve MaOPs. Clearly, existing difficulties in solving MaOPs explain why Many-Objective Optimization was considered an unexplored domain in resource management of cloud computing datacenters before this research work [14] and no many-objective formulation was proposed for the VMP problem [8, 9, 10].
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