Static and adaptive distributed data replication using genetic algorithms
Fast dissemination and access of information in large distributed systems, such as the Internet, has become a norm of our daily life. However, undesired long delays experienced by end-users, especially during the peak hours, continue to be a common problem. Replicating some of the objects at multipl...
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| Published in: | Journal of parallel and distributed computing Vol. 64; no. 11; pp. 1270 - 1285 |
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| Abstract | Fast dissemination and access of information in large distributed systems, such as the Internet, has become a norm of our daily life. However, undesired long delays experienced by end-users, especially during the peak hours, continue to be a common problem. Replicating some of the objects at multiple sites is one possible solution in decreasing network traffic. The decision of what to replicate where, requires solving a constraint optimization problem which is NP-complete in general. Such problems are known to stretch the capacity of a Genetic Algorithm (GA) to its limits. Nevertheless, we propose a GA to solve the problem when the read/write demands remain static and experimentally prove the superior solution quality obtained compared to an intuitive greedy method. Unfortunately, the static GA approach involves high running time and may not be useful when read/write demands continuously change, as is the case with breaking news. To tackle such case we propose a hybrid GA that takes as input the current replica distribution and computes a new one using knowledge about the network attributes and the changes occurred. Keeping in view more pragmatic scenarios in today's distributed information environments, we evaluate these algorithms with respect to the storage capacity constraint of each site as well as variations in the popularity of objects, and also examine the trade-off between running time and solution quality. |
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| AbstractList | Fast dissemination and access of information in large distributed systems, such as the Internet, has become a norm of our daily life. However, undesired long delays experienced by end-users, especially during the peak hours, continue to be a common problem. Replicating some of the objects at multiple sites is one possible solution in decreasing network traffic. The decision of what to replicate where, requires solving a constraint optimization problem which is NP-complete in general. Such problems are known to stretch the capacity of a Genetic Algorithm (GA) to its limits. Nevertheless, we propose a GA to solve the problem when the read/write demands remain static and experimentally prove the superior solution quality obtained compared to an intuitive greedy method. Unfortunately, the static GA approach involves high running time and may not be useful when read/write demands continuously change, as is the case with breaking news. To tackle such case we propose a hybrid GA that takes as input the current replica distribution and computes a new one using knowledge about the network attributes and the changes occurred. Keeping in view more pragmatic scenarios in today's distributed information environments, we evaluate these algorithms with respect to the storage capacity constraint of each site as well as variations in the popularity of objects, and also examine the trade-off between running time and solution quality. |
| Author | Ahmad, Ishfaq Loukopoulos, Thanasis |
| Author_xml | – sequence: 1 givenname: Thanasis surname: Loukopoulos fullname: Loukopoulos, Thanasis organization: Department of Computer Science, The Hong Kong University of Science and Technology, Kowloon, Hong Kong – sequence: 2 givenname: Ishfaq surname: Ahmad fullname: Ahmad, Ishfaq email: iahmad@cse.uta.edu organization: Department of Computer Science and Engineering, The University of Texas at Arlintgon, P.O. Box 19015, 248 D/E Nedderman Hall, 416 Yates St., Arlington, TX 16019-0015, USA |
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| Keywords | Static and dynamic allocation Data replication Internet Greedy method World wide web Genetic algorithms Worldwide web Static allocation Distributed database Constraint satisfaction Information access Distributed system Information dissemination Dynamic allocation Genetic algorithm Problem solving |
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| SubjectTerms | Applied sciences Artificial intelligence Computer science; control theory; systems Computer systems and distributed systems. User interface Data replication Exact sciences and technology Genetic algorithms Greedy method Internet Problem solving, game playing Software Static and dynamic allocation Telecommunications Telecommunications and information theory Teleprocessing networks. Isdn World wide web |
| Title | Static and adaptive distributed data replication using genetic algorithms |
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