An Approach to Balance Maintenance Costs and Electricity Consumption in Cloud Data Centers

Autor: Nicola Blefari-Melazzi, Fabio D'Andreagiovanni, Claudia Canali, Luca Chiaraviglio, Mohammad Shojafar, Riccardo Lancellotti
Přispěvatelé: Department of Electronics [Torino] (DELEN), Politecnico di Torino = Polytechnic of Turin (Polito), Centre National de la Recherche Scientifique (CNRS), Heuristique et Diagnostic des Systèmes Complexes [Compiègne] (Heudiasyc), Université de Technologie de Compiègne (UTC)-Centre National de la Recherche Scientifique (CNRS)
Rok vydání: 2018
Předmět:
Control and Optimization
Computer science
Distributed computing
Cloud computing
02 engineering and technology
Maintenance engineering
[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI]
Server
0202 electrical engineering
electronic engineering
information engineering
ComputingMilieux_MISCELLANEOUS
Settore ING-INF/03 - Telecomunicazioni
Renewable Energy
Sustainability and the Environment
business.industry
020206 networking & telecommunications
[INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO]
Energy consumption
Load balancing (computing)
Computational Theory and Mathematics
Hardware and Architecture
020201 artificial intelligence & image processing
Data center
[MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC]
Central processing unit
Electricity
business
Software
Zdroj: IEEE Transactions on Sustainable Computing
IEEE Transactions on Sustainable Computing, IEEE, 2018, 3 (4), pp.274-288
ISSN: 2377-3790
2377-3782
Popis: We target the problem of managing the power states of the servers in a Cloud Data Center (CDC) to jointly minimize the electricity consumption and the maintenance costs derived from the variation of power (and consequently of temperature) on the servers’ CPU. More in detail, we consider a set of virtual machines (VMs) and their requirements in terms of CPU and memory across a set of Time Slot (TSs). We then model the consumed electricity by taking into account the VMs processing costs on the servers, the costs for transferring data between the VMs, and the costs for migrating the VMs across the servers. In addition, we employ a material-based fatigue model to compute the maintenance costs needed to repair the CPU, as a consequence of the variation over time of the server power states. After detailing the problem formulation, we design an original algorithm, called Maintenance and Electricity Costs Data Center (MECDC), to solve it. Our results, obtained over several scenarios from a real CDC, show that MECDC largely outperforms two reference algorithms, which instead either target the load balancing or the energy consumption of the servers.
Databáze: OpenAIRE
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