The steadily increasing success of Cloud Computing is causing a huge rise in its electrical power consumption, contributing to higher energy costs, as well as to the greenhouse effect and the global warming. One of the most common key strategies to reduce the power consumption of data centers is the consolidation of virtual machines, whose effectiveness strongly depends on a reliable forecasting of future computational resource needs. In fact, servers are typically configured to handle peak workload conditions even if they are often under-utilized, that results in a wastefulness of resources and inefficient energy consumption. Motivated by these issues, this paper describes a data-driven approach based on auto-regressive models to dynamically forecast virtual machine workloads, for energy-Aware allocations of virtual machines on Cloud physical nodes. Virtual machine migrations across physical servers are periodically done on the basis of the estimated virtual machine demands, by minimizing the number of active servers. Experimental results show encouraging benefits in terms of energy saving, while satisfying performance constraints and service level agreement established with users.

A Data-Driven Approach Based on Auto-Regressive Models for Energy-Efficient Clouds

Cesario E.
2017-01-01

Abstract

The steadily increasing success of Cloud Computing is causing a huge rise in its electrical power consumption, contributing to higher energy costs, as well as to the greenhouse effect and the global warming. One of the most common key strategies to reduce the power consumption of data centers is the consolidation of virtual machines, whose effectiveness strongly depends on a reliable forecasting of future computational resource needs. In fact, servers are typically configured to handle peak workload conditions even if they are often under-utilized, that results in a wastefulness of resources and inefficient energy consumption. Motivated by these issues, this paper describes a data-driven approach based on auto-regressive models to dynamically forecast virtual machine workloads, for energy-Aware allocations of virtual machines on Cloud physical nodes. Virtual machine migrations across physical servers are periodically done on the basis of the estimated virtual machine demands, by minimizing the number of active servers. Experimental results show encouraging benefits in terms of energy saving, while satisfying performance constraints and service level agreement established with users.
978-1-5090-6611-7
Cloud computing; Data-driven approaches for energy-Aware Cloud; Energy efficiency; Forecasting
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/303511
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