Title :
Replica placement using genetic algorithm
Author :
Safaee, Shahab ; Haghighat, Abolfazl Torghi
Author_Institution :
Dept. of Electr., Comput. & IT, Qazvin Islamic Azad Univ., Qazvin, Iran
Abstract :
Replica placement is one of the classic issues, which enjoys applicability in finding the optimal location to deploy servers in different fields, particularly, industry in addition to computer network fields. Several methods have been proposed for selecting such locations optimally. The two noteworthy parameters such methods try to optimize are: selection of the best-fit location and the time of executing the algorithm. Consequently, the efficient method will be the one that selects locations as close to the optimal status as possible and enjoys a rather acceptable speed. This is considered an NP-Complete problem, and thus, heuristic methods will be used in its solution. Among the proposed methods to solve the problem of replica placement of server location, the best algorithm in terms of time complexity is the O (N. max(logN, K)). The method which has been introduced in this study is the designation and implementation of an algorithm using the genetic algorithm. The execution time of such an algorithm is much less than the algorithms whose location is close to optimal.
Keywords :
client-server systems; computational complexity; genetic algorithms; NP-complete problem; algorithm designation; algorithm execution time; algorithm implementation; best-fit location selection; computer network fields; genetic algorithm; heuristic methods; industries; optimal location; optimization; replica placement; time complexity; Algorithm design and analysis; Clustering algorithms; Complexity theory; Genetic algorithms; Heuristic algorithms; Partitioning algorithms; Servers; Genetic Algorithm; Optimal Location; Replica Placement;
Conference_Titel :
Innovation Management and Technology Research (ICIMTR), 2012 International Conference on
Conference_Location :
Malacca
Print_ISBN :
978-1-4673-0655-3
DOI :
10.1109/ICIMTR.2012.6236448