10 de Diciembre de 2018

• Autor: Fernández Cerero, Damian.
• Titulo: “Energy and Performance-Aware Scheduling and Shut-Down Models for Efficient Cloud-Computing Data Centers”
• Departamento: Lenguajes y Sistemas Informáticos.
• Teseo: https://www.educacion.gob.es/teseo/mostrarRef.do?ref=1721142
• Directores: Juan Antonio Ortega Ramírez, Alejandro Fernández-Montes González (Codirector) y Juan Antonio Ortega Ramírez (Tutor/Ponente).
• Sinopsis:

  This Doctoral Dissertation, presented as a set of research contributions, focuses on resource efficiency in data centers. This topic has been faced mainly through the development of several energy-efficiency, resource-managing and scheduling policies, as well as the simulation tools required to test them in realistic cloud-computing environments.

  Several models have been implemented in order to minimize energy consumption in Cloud-Computing environments. Among them: a) Fifteen probabilistic and deterministic energy policies which shut-down idle machines; b) Five energy-aware scheduling algorithms, including several genetic algorithm models; c) A Stackelberg game-based strategy which models the concurrency between opposite requirements of Cloud-Computing systems in order to dynamically apply the optimal scheduling algorithms and energy-efficiency policies depending on the environment; and d) A productive analysis on the resource efficiency of several realistic cloud-computing environments.

  A novel simulation tool called SCORE, able to simulate several data-center sizes, machine heterogeneity, security levels, workload composition and patterns, scheduling strategies and energy-efficiency strategies, was developed in order to test such strategies in large-scale cloud-computing clusters. As results, more than fifty Key Performance Indicators (KPI) show that more than 20% of energy consumption can be reduced in realistic high-utilization environments when proper policies are employed.