Observatorio de I+D+i UPM

Descripción
Cloud computing addresses the problem of costly computing infrastructures by providing elasticity to the dynamic resource provisioning on a pay-as-you-go basis, and nowadays it is considered as a valid alternative to owned high performance computing clusters. There are two main appealing incentives for this emerging paradigm: first, utility-based usage models provided by Clouds allow clients to pay per use, increasing the user satisfaction; then, there is only a relatively low investment required for the remote devices that access the Cloud resources. Computational demand on data centers is increasing due to growing popularity of Cloud applications. However, these facilities are becoming unsustainable in terms of power consumption and growing energy costs. Nowadays, the data center industry consumes about 2% of the worldwide energy production. Also, the proliferation of urban data centers is responsible for the increasing power demand of up to 70% in metropolitan areas where the power density is becoming too high for the power grid. In two or three years, this situation will cause outages in the 95% of urban data centers incurring in annual costs of about US$2 million per infrastructure. Besides the economical impact, the heat and the carbon footprint generated by cooling systems in data centers are dramatically increasing and they are expected to overtake airline industry emissions by 2020. The main objective of this Ph.D. thesis is to address the energy challenge in Cloud data centers from a thermal and power-aware perspective using proactive strategies. Our research proposes the design and implementation of models and global optimizations that jointly consider energy consumption of both computing and cooling resources while maintaining QoS from a new holistic perspective. Thesis Contributions: To support the thesis that our research can deliver significant value in the area of Cloud energy-efficiency, compared to traditional approaches, we have: ? Defined a taxonomy on energy efficiency that compiles the different levels of abstraction that can be found in data centers area. ? Classified state-of-the-art approaches according to the proposed taxonomy, identifying new open challenges from a holistic perspective. ? Identified the trade-offs between leakage and cooling consumption based on empirical research. ? Proposednovelmodelingtechniquesfortheautomaticidentificationoffastandaccurate models, providing testing in a real environment. ? Analyzed DVFS, performance and power trade-offs in the Cloud environment. ? Designed and implemented a novel proactive optimization policy for dynamic consolidation of virtual machines that combine DVFS and power-aware strategies while ensuring QoS. ? Derived thermal models for CPU and memory devices validated in real environment. ? Designed and implemented new proactive approaches that include DVFS, thermal and power considerations in both cooling and IT consumption from a novel holistic perspective. ? Validated our optimization strategies in simulation environment.
Internacional	Si
ISBN
Tipo de Tesis	Doctoral
Calificación	Sobresaliente cum laude
Fecha	29/06/2017

Esta actividad pertenece a memorias de investigación

• Autor: Patricia Arroba Garcia (UPM)
• Director: Jose Manuel Moya Fernandez (UPM)
• Director: José Luis Ayala (Universidad Complutense de Madrid)

• Creador: Grupo de Investigación: Laboratorio de Sistemas Integrados (LSI)
• Centro o Instituto I+D+i: Centro de Investigación en Simulación Computacional
• Departamento: Ingeniería Electrónica