1 research outputs found
A Multi-Objective Approach for Multi-Cloud Infrastructure Brokering in Dynamic Markets
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