Paper Info
Title
Self-organized Parallel Cooperation for Solving Optimization Problems
Abstract
This paper is about using a set of self-organized computing resources to perform multi-objective optimization. In the proposed approach, the computing resources are presented as a unified resource to the user where in traditional parallel optimization paradigms the user has to assign tasks to the resources, collect the best available solutions and deal with failing resources. In this approach called self-organized parallel cooperation model, the user has to specify the preferences and only give the objective functions to the system. The self-organized computing resources deliver the obtained solutions after a certain time to the user. In such a system, fast resources must continue the optimization as long as the overall computing time is not over. However as the solutions of a multi-objective problem depend on each other (via the domination relation) adding a waiting time to the fast processors would affect the quality of the solutions. This has been studied on a scenario of 100 heterogeneous computing resources in the presence of failures in the system.
Year
DOI
Venue
2009
10.1007/978-3-642-00454-4_15
ARCS
Keywords
Field
DocType
fast processor,self-organized computing resource,multi-objective optimization,fast resource,traditional parallel optimization,certain time,optimization problems,overall computing time,multi-objective problem,self-organized parallel cooperation,heterogeneous computing resource,computing resource,objective function,multi objective optimization,heterogeneous computing,self organization,optimization problem
Cooperation model,Mathematical optimization,Parallel optimization,Computer science,Symmetric multiprocessor system,Multi-objective optimization,Utility computing,Optimization problem,Distributed computing
Conference
Volume
ISSN
Citations 
5455
0302-9743
1
PageRank 
References 
Authors
0.36
9
2
Name
Order
Citations
PageRank
Sanaz Mostaghim143241.17
Hartmut Schmeck21034120.58