Self-Adaptive Decentralized Monitoring in Software-Defined Networks

• Published in: IEEE eTransactions on network and service management Vol. 15; no. 4; pp. 1277 - 1291
• Main Authors: Tangari, Gioacchino, Tuncer, Daphne, Charalambides, Marinos, Qi, Yuanshunle, Pavlou, George
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control; Frequency measurement; Monitoring; Monitoring systems; Network monitoring; Reconfiguration; Resource management; Self adaptive control systems; self-adaptation; Software; Software defined networking; software-defined networks; Switches; Synchronization; Task analysis
• ISSN: 1932-4537; 1932-4537
• DOI: 10.1109/TNSM.2018.2874813

The software-defined networking (SDN) paradigm can allow network management solutions to automatically and frequently reconfigure network resources. When developing SDN-based management architectures, it is of paramount importance to design a monitoring system that can provide timely and consistent updates to heterogeneous management applications. To support such applications operating with low latency requirements, the monitoring system should scale with increasing network size and provide precise network views with minimum overhead on the available resources. In this paper, we present a novel, self-adaptive, decentralized framework for resource monitoring in SDN. Our framework enables accurate statistics to be collected with limited burden on the network resources. This is realized through a self-tuning, adaptive monitoring mechanism that automatically adjusts its settings based on the traffic dynamics. We evaluate our proposal based on a realistic use case scenario, where a content distribution service and an on-demand gaming platform are deployed within an ISP network. The results show that reduced monitoring latencies are obtained with the proposed framework, thus enabling shorter reconfiguration control loops. In addition, the proposed adaptive monitoring method achieves significant gain in terms of monitoring overhead, while preserving the performance of the services considered.