Self-Organizing maps for detecting abnormal thermal behavior in data centers

Aransay, Ignacio and Zapater Sancho, Marina and Arroba García, Patricia and Moya Fernández, José Manuel (2015). Self-Organizing maps for detecting abnormal thermal behavior in data centers. In: "8th IEEE International Conference on Cloud Computing (CLOUD)", 27/06/2015 - 02/07/2015, New York, EE.UU. pp. 138-145.

Description

Title: Self-Organizing maps for detecting abnormal thermal behavior in data centers
Author/s:
  • Aransay, Ignacio
  • Zapater Sancho, Marina
  • Arroba García, Patricia
  • Moya Fernández, José Manuel
Item Type: Presentation at Congress or Conference (Article)
Event Title: 8th IEEE International Conference on Cloud Computing (CLOUD)
Event Dates: 27/06/2015 - 02/07/2015
Event Location: New York, EE.UU
Title of Book: 8th IEEE International Conference on Cloud Computing (CLOUD)
Date: 2015
Subjects:
Freetext Keywords: Anomaly detection, data centers, self-organizing maps
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Ingeniería Electrónica
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

The increasing success of Cloud Computing applications and online services has contributed to the unsustainability of data center facilities in terms of energy consumption. Higher resource demand has increased the electricity required by computation and cooling resources, leading to power shortages and outages, specially in urban infrastructures. Current energy reduction strategies for Cloud facilities usually disregard the data center topology, the contribution of cooling consumption and the scalability of optimization strategies. Our work tackles the energy challenge by proposing a temperature-aware {VM} allocation policy based on a {Trust-and-Reputation} System ({TRS}). A {TRS} meets the requirements for inherently distributed environments such as data centers, and allows the implementation of autonomous and scalable {VM} allocation techniques. For this purpose, we model the relationships between the different computational entities, synthesizing this information in one single metric. This metric, called reputation, would be used to optimize the allocation of {VMs} in order to reduce energy consumption. We validate our approach with a state-of-the-art Cloud simulator using real Cloud traces. Our results show considerable reduction in energy consumption, reaching up to 46.16\% savings in computing power and 17.38\% savings in cooling, without {QoS} degradation while keeping servers below thermal redlining. Moreover, our results show the limitations of the {PUE} ratio as a metric for energy efficiency. To the best of our knowledge, this paper is the first approach in combining {Trust-and-Reputation} systems with Cloud Computing {VM} allocation.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainTEC-2012-33892UnspecifiedUnspecifiedTECNOLOGIAS HW/SW PARA LA EFICIENCIA ENERGETICA EN SISTEMAS DE COMPUTACION DISTRIBUIDOS
Government of SpainRTC-2014-2717-3UnspecifiedUnspecified3 OPTIMIZACIÓN ENERGÉTICA DE CENTROS DE DATOS DE INFRAESTRUCTURAS CLOUD BASADAS EN OPENSTACK
Government of SpainIPT-2012-1041-430000ConTVLABUnspecifiedConnectedTV Lab (ConTVLAB) SISTEMA DISTRIBUIDO DE VERIFICACIÓN, PRUEBA Y CERTIFICACIÓN DE APLICACIONES Y SERVICIOS DE TELEVISIÓN CONECTADA DE NUEVA GENERACIÓN (HbbTV, Android TV, Smart TV)

More information

Item ID: 42751
DC Identifier: http://oa.upm.es/42751/
OAI Identifier: oai:oa.upm.es:42751
Deposited by: Memoria Investigacion
Deposited on: 04 Sep 2016 07:35
Last Modified: 04 Sep 2016 07:35
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