Improving learning-based request forwarding in resource discovery through load-awareness

Authors:
Mohammad Norouzi Arab;Seyedeh Leili Mirtaheri;Ehsan Mousavi Khaneghah;Mohsen Sharifi;Meisam Mohammadkhani
Affiliations:
School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran;School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran;School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran;School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran;School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran
Venue:
Globe'11 Proceedings of the 4th international conference on Data management in grid and peer-to-peer systems
Year:
2011

Citing 11
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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.