On minimizing flow monitoring costs in large-scale software-defined network networks
Source of Publication
International Journal of Network Management
Recent years have witnessed the rise of novel network applications such as telesurgery, telepresence, and holoportation. As such applications have stringent performance requirements, timely and accurate traffic monitoring becomes of paramount importance to be able to react in a timely and efficient manner, and swiftly adjust the network configuration to achieve the sought-after requirements. However, existing monitoring schemes are either incurring high cost (e.g., high bandwidth consumption) due to the large number of monitoring messages or inefficient when they incur high reporting delay (i.e., the time needed for a monitoring message to reach the controller) making the collected statistics obsolete. In this paper, we address this problem and propose monitoring mechanisms for software defined networks that minimize the monitoring cost while satisfying an upper bound on the reporting delay of the statistics. Our solutions allow to carefully select the switch that should report the statistics about each flow crossing the network taking into consideration the available bandwidth and the capacity of the switch (i.e., the maximum number of flows that it can monitor). In particular, we formulate the switch-to-flow selection problem as an integer linear program and propose two heuristic algorithms to cope with large-scale instances of the problem. We consider the scenario where a single controller is collecting statistics and another where statistics are collected by multiple controllers. Simulation results show that the proposed algorithms provide near-optimal solutions with minimal computation time and outperform existing monitoring strategies in terms of monitoring cost and reporting delay.
flow monitoring, high-precision monitoring, software-defined networking
Yahyaoui, Haythem; Zhani, Mohamed Faten; Bouachir, Ouns; and Aloqaily, Moayad, "On minimizing flow monitoring costs in large-scale software-defined network networks" (2023). All Works. 5562.
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