ACM: Accuracy-Aware Collaborative Monitoring for Software-Defined Network-Wide Measurement

• Published in: Sensors (Basel, Switzerland) Switzerland), 2022-10, Vol.22 (20), p.7932
• Main Authors: Gu, Jiqing, Song, Chao, Dai, Haipeng, Shi, Lei, Wu, Jinqiu, Lu, Li
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Approximation; Collaboration; collaborative monitoring; Count–Min sketch; large flows; Load balancing; Measurement; Monitoring; Monitors; Operators (mathematics); Sketches; Software; software-defined network-wide measurement; Software-defined networking; Technology application; Traffic congestion; Virtual private networks
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22207932

Software-defined measurement (SDM) is a simple and efficient way to deploy measurement tasks and collect measurement data. With SDM, it is convenient for operators to implement fine-grained network-wide measurements at the flow level, from which many important functions can benefit. The prior work provides mechanisms to distribute flows to monitors, such that each monitor can identify its non-overlapped subset of flows to measure, and a certain global performance criterion is optimized, such as load balance or flow coverage. Many applications of network management can benefit from a function that can find large flows efficiently, such as congestion control by dynamically scheduling large flows, caching of forwarding table entries, and network capacity planning. However, the current network-wide measurements neglect the diversity of different flows as they treat large flows and small flows equally. In this paper, we present a mechanism of accuracy-aware collaborative monitoring (ACM) to improve the measurement accuracies of large flows in network-wide measurements at the flow level. The structure of the sketch is an approximate counting algorithm, and a high-measurement accuracy can be achieved by merging the results from multiple monitors with sketches, which is termed as collaborative monitoring. The core idea of our method is to allocate more monitors to large flows and achieve the load balance to provide accuracy-aware monitoring. We modeled our problem as an integer–linear programming problem, which is NP-hard. Thus, we propose an approximation algorithm, named the improved longest processing time algorithm (iLPTA); we proved that its approximation ratio is (12+nl). We propose a two-stage online distribution algorithm (TODA). Moreover, we proved that its approximation ratio is (1+nl−1). The iLPTA is an offline approximation algorithm used to assign monitors for each flow, which prove the validity and feasibility of the core idea. The TODA is an online algorithm that attempts to achieve the load balance by selecting the monitor with the smallest load to a large flow. Our extensional experiment results verify the effectiveness of our proposed algorithms.