Energy-efficient Resource Allocation in Automatic cloud computing


CALL: 2009

DOMAIN: IS - Telecommunication and Multimedia


LAST NAME: Blazewicz



HOST INSTITUTION: University of Luxembourg

KEYWORDS: Energy-efficiency; resource management; heterogeneous computing; multi-objective optimization; multi-agent systems; parallel and distributed computing; telecommunication networks; cloud computing

START: 2010-01-01

END: 2012-12-31


Submitted Abstract

A simple concept that has emerged out of the conceptions of heterogeneous distributedcomputing, grid computing, utility computing, and autonomic computing is that of cloudcomputing (CC). In CC, end-users do not own or rent any part of the infrastructure. They (theend-users) simply use the services available through the CC paradigm and pay for the servicesused. The CC paradigm can offer any conceivable form of services, such as databases,computational resources, social networking, and telephonic services.As such, CC can be perceived as an immediate extension of data centers that are hugefacilities housing IT services on hundreds of high-end computing servers. One of their majorconcern is the huge amounts of electrical power consumption, and most of the energy wasspent to cool the underutilized high-end computing servers. There is no general shift by the CCservice providers on the issue of energy consumption; therefore, CC may reach a similar faithas data centers, if not made energy-efficient.Green-IT aims to provide a holistic autonomic energy-efficient solution to manage, provision,and administer the various resources within the CC paradigm.The main research challenges that will be tackled to achieve the holistic approach are notedbelow.• Development of meta-models: CC is a complex system of numerous pervasive devices thatrequest services over heterogeneous network infrastructures from a data center that isenergy gobbler. Because each computing entity’s performance is defined uniquely, we mustdevelop meta-models that can adequately define a unified performance metric of thesystem, and the system’s properties, constraints, and optimization criteria.• Develop resource management methodologies: With several possible objectives andconstraints, the meta-models must result in multi-objective multi-constraint problems (MOC).Green-IT will develop, refine, and evolve solutions for MOC that will primarily be motivatedbased on, goal programming, adaptive weighted sums approach, homotopic functions,boundary intersections, multi-level programming, Stackelberg games, and multi-objectivegenetic algorithm solvers.• Develop autonomic resource management: The anytime anywhere slogan only will beeffective when an autonomic management of resources can be achieved. The resourceallocation methodologies developed must go further refinement such that the system athand is self- healing, repairing, and optimizing. In particular, it is our intention to utilize multiagentsystems (MAS) that can learn to adapt (machine learning methodologies) andgracefully evolve to adapt (evolutionary game theoretical methodologies).Because the CC paradigm is in its infancy, definitions, protocols, policies, implementations, areall undergoing scrutiny. Besides major IT companies, such as Google, IBM, Microsoft, andAmazon, there is no sole university-based research group that is conducting cutting-edgeresearch in autonomic energy-efficient management of resources in CC. This will ensure UL’slead in research on CC that will significantly impact our daily lives. The large set of datacenters based in Luxembourg (public and private) are the obviously targets for Green-IT.Luxconnect, as confirmed by Prof. Engel, president of Luxconnect data center, will take anactive part in this project and Dr. Koenig (advisor to UL rector) confirmed that Green-IT fits intoUL sustainability plan for Esch-Belval.

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