Scalability-aware mechanism based on workload prediction in ultra-peer networks

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Ultra-peers networks are emergent architectures in large-scale distributed computing environments. Controlling the workload is essential since the system scales up rapidly and accommodates a dynamic change in the number of users, resources, etc. Thus, developing an advanced model based on scalability aspects is a necessity, since it is an important and critical issue when designing such systems. In this paper, we present a scalability-aware approach for ultra-peers networks where each ultra-peer behaves like an ecosystem, in which, we prevent the presence of bottleneck in these structured information systems, and help each ultra-peer to find and stay in a steady state. We make use of neural networks in conjunction with the queueing theory to understand the behaviour of each ultra-peer, by first estimating its future workload, then to take decision on whether the next period will cause a bottleneck situation or not. After that we propose solutions to allow each ultra-peer to scale with the growth of the network size. The effectiveness of the design on scalability is evaluated using synthetic as well as realistic workloads for a number of different scenarios. Results show that the ultra-peer has successfully supervised its state to scale with the growth of the system size.

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Correspondence to Nabila Chergui.

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Chergui, N., Kechadi, T. & Chikhi, S. Scalability-aware mechanism based on workload prediction in ultra-peer networks. Peer-to-Peer Netw. Appl. 11, 431–449 (2018) doi:10.1007/s12083-017-0542-z

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  • Workload prediction
  • Distributed systems
  • Ultra-peers architectures
  • Scalability
  • Queueing theory
  • Neural network