Self-Adaptive Decentralized Monitoring in Software-Defined Networks

Abstract

The Software-Defined Networking (SDN) paradigm can allow network management solutions to automatically and frequently reconfigure network resources. When developing SDNbased 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.

Novel Adaptive Data Collection based on a Confidence Index in SDN

“…Lots of adaptive algorithms are provided in the literature from wireless sensor networks data collection [21] and Internet of Things (IoT) [17] to monitoring in networking and especially in SDN for monitoring overhead optimization. As pointed out in [6], we can distinguish two main techniques:…”

“…This approach is used in [23] which uses a linear prediction based dynamic adjustment scheme to provide dynamic zooming into the flow space (temporal and spatial dimensions). Authors in [6] propose SAM (Self-tuning Adaptive Monitoring) which uses a prediction-based approach to dynamically adjust polling rate but with minimal parameter tuning effort, not like [23] and [22].…”

“…To handle this, adaptive data collection techniques are developed. Based on [5], an overview of network monitoring in SDN, [6] an adaptive approach for monitoring in SDN and [7], presenting a state-of-the-art review of monitoring in edge computing and its requirement, the literature adaptive techniques handle efficiently overhead reduction problem, but they still present these 3 aspects:…”

A Lightweight Scheme of Active-Port-Aware Monitoring in Software-Defined Networks

“…Although various monitoring methods [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21] have been proposed to improve monitoring performance, existing monitoring mechanisms in SDN still have potential risks concerning monitoring overheads. Most of the previous monitoring methods are based on flow monitoring which introduces a heavy monitoring overhead in accordance with the increasing of the number of flow rules in SDN.…”

Low-Overhead Near-Real-Time Flow Statistics Collection in SDN

“…provide timely accurate statistics to the control plane but with low overheads, pullbased adaptive approaches consisting of using adaptive polling rates, instead of a fixed one were proposed. The collection granularity adjustment is done either according to the stability of the statistic being collected, by comparing the difference between two consecutive data-points to one or several thresholds [4], or by using a prediction-based approach which adjusts the reporting intervals according to the ability to provide good estimations of future data-points based on the history composed of the previously collected data-points [3] [5].…”