  Underutilization of computing resources and high power consumption are two
primary challenges in the domain of Cloud resource management. This paper deals
with these challenges through offline, migration impact-aware, multi-objective
dynamic Virtual Machine (VM) consolidation in the context of large-scale
virtualized data center environments. The problem is formulated as an NP-hard
discrete combinatorial optimization problem with simultaneous objectives of
minimizing resource wastage, power consumption, and the associated VM migration
overhead. Since dynamic VM consolidation through VM live migrations have
negative impacts on hosted applications performance and data center components,
a VM live migration overhead estimation technique is proposed, which takes into
account pragmatic migration parameters and overhead factors. In order to tackle
scalability issues, a hierarchical, decentralized dynamic VM consolidation
framework is presented that helps to localize migration-related network traffic
and reduce network cost. Moreover, a multi-objective, dynamic VM consolidation
algorithm is proposed by utilizing the Ant Colony Optimization (ACO)
metaheuristic, with integration of the proposed VM migration overhead
estimation technique. Comprehensive performance evaluation makes it evident
that the proposed dynamic VM consolidation approach outpaces the
state-of-the-art offline, migration-aware dynamic VM consolidation algorithm
across all performance metrics by reducing the overall power consumption by up
to 47%, resource wastage by up to 64%, and migration overhead by up to 83%.

Distributed, Parallel, and Cluster Computing