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1. Predictive Control for Energy-Aware Consolidation in Cloud Datacenters
Abstract:
Infrastructure-as-a-Service is one of the most used paradigms
of cloud computing and relies on large-scale datacenters with thousands of
nodes. As a consequence of this success, the energetic demand of the
infrastructure may lead to relevant economical costs and environmental
footprint. Thus, the search for power optimization is of primary importance. In
this perspective, this paper introduces an energy-aware consolidation strategy
based on predictive control, in which virtual machines are properly migrated
among physical machines to reduce the amount of active units. To this aim, a
discrete-time dynamic model and suitable constraints are introduced to describe
the cloud. The migration strategies are obtained by solving finite-horizon optimal
control problems involving integer variables. The proposed method allows one to
trade among power savings and violations of the service level agreement. To
prove its effectiveness, a simulation campaign is conducted in different scenarios
using both synthetic and real workloads, also by performing a comparison with
three heuristics selected from the reference literature.