Abstract
Request forwarding is an efficient approach in discovering resources in distributed systems because it achieves one of the main goals of distributed systems namely the scalability goal. Despite achieving reasonable scalability, this approach suffers from long response times to resource requests. Several solutions such as learning-based request forwarding have tried to improve the response time but not quite. This is because target nodes in learning-based request forwarding are selected based on their responses to previous similar requests. This method of selection overloads the nodes and prolongs the response times to resource requests. This paper introduces a new strategy for selection of target nodes to ameliorate this flaw by taking into account the loads on target nodes as well as their abilities in responding to requests based on their previous behaviors. Evaluations show that as the number of requests increases, the proposed strategy reduces the response times to resource requests much more significantly compared with pure learning-based request forwarding strategy.
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Arab, M.N., Mirtaheri, S.L., Khaneghah, E.M., Sharifi, M., Mohammadkhani, M. (2011). Improving Learning-Based Request Forwarding in Resource Discovery through Load-Awareness. In: Hameurlain, A., Tjoa, A.M. (eds) Data Management in Grid and Peer-to-Peer Systems. Globe 2011. Lecture Notes in Computer Science, vol 6864. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22947-3_7
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DOI: https://doi.org/10.1007/978-3-642-22947-3_7
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