An Objective-Driven On-Demand Network Abstraction for Adaptive Applications

Abstract Revealing an abstract view of the network is essential for the new paradigm of developing network-aware adaptive applications that can fully leverage the available computation and storage resources and achieve better business values. In this paper, we introduce ONV, a novel abstraction of flow-based on-demand network view. The ONV models network views as linear constraints on network-related variables in application-layer objective functions, and provides “equivalent” network views that allow applications to achieve the same optimal objectives as if they have the global information. We prove the lower bound for the number of links contained in an equivalent network view, and propose two algorithms to effectively calculate on-demand equivalent network views. We evaluate the efficacy and the efficiency of our algorithms extensively with real-world topologies. Evaluations demonstrate that the ONV can simplify the network up to 80% while maintaining an equivalent view of the network. Even for a large network with more than 25 000 links and a request containing 3000 flows, the result can be effectively computed in less than 1 min on a commodity server.
  • Kai Gao
  • Qiao Xiang (Yale)
  • Xin Wang
  • Richard Yang (Yale)
  • Jun Bi
Date Apr-2019
Venue IEEE/ACM Transactions on Networking 27, no. 2 (2019): 805-818. [link]