Reference : ENERGY-EFFICIENT SCHEDULING IN GRID COMPUTING AND RESOURCE ALLOCATION IN OPPORTUNISTI...
Dissertations and theses : Doctoral thesis
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/18833
ENERGY-EFFICIENT SCHEDULING IN GRID COMPUTING AND RESOURCE ALLOCATION IN OPPORTUNISTIC CLOUD COMPUTING: MODELS AND ALGORITHMS
English
Diaz, Cesar Orlando mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
2-Sep-2014
University of Luxembourg, ​Luxembourg, ​​Luxembourg
Docteur en Informatique
131
Bouvry, Pascal mailto
Guinand, Frederic mailto
Pecero, Johnatan mailto
Kacem, Imed mailto
[en] Scheduling heuristics ; Resource Allocation Algorithms ; energy-efficiency ; Grid Computing ; Cloud computing ; Opportunistic Cloud Infrastructure
[en] Resource allocation among Heterogenous Computing Systems (HCS) components, such as cluster, grid, or cloud computing can be considered as a service. These systems manage millions of computational resources to solve several difficult com- putational problems. Resource allocation and scheduling among these systems are still a hot topic for research purposes. A goal of this research is to find an effi- cient use of these resources proposing a resource allocation and efficient scheduling techniques. Firstly, the relevance of energy consumption in processing elements as well as techniques and policies to support it are presented. It emphasizes in resource allocation algorithms in opportunistic environments and low complexity scheduling heuristics in grid computing environment. In particular, a series of low complexity, scalable, and energy-efficient algorithms for scheduling in grid computing and a resource allocation technique for opportunistic environment are presented. The latest aforementioned technique was evaluated in an opportunistic cloud environment. Three fast and energy-efficient batch mode scheduling novel heuristics were designed, developed, and evaluated to produce fast tasks mapping in HCS. To fully understand their capabilities and limitations, these aforemen- tioned heuristics were studied and compared with a variety of system parameters for their performance and scalability.
Researchers ; Professionals ; Students
http://hdl.handle.net/10993/18833

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