Efficient memory management with co-timeout policy eviction and history-enlightened selective strategy installation

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-03-12 DOI:10.1016/j.future.2025.107799

Xianfeng Li , Haisheng Yu , Xue Yang , Haoran Sun , Yan Huang

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引用次数: 0

Abstract

In Software-Defined Network (SDN), the controller plays a crucial role in implementing fine-grained network policies by installing flow rules in the switch’s flow table. However, the limited capacity of the flow table, often implemented using TCAM, poses scalability challenges due to its low density and high energy consumption. To address this issue, this paper focuses on two key aspects: (1) early eviction of installed flow rules and (2) selective installation of flow rules.

To tackle the first aspect, we propose an adaptive timeout mechanism called Two-Stage Timeout (TST) that enhances the flow table architecture. TST enables the efficient eviction of short-lived flows, creating space for more valuable flow rules. In addition, the introduction of the Inactive Flow Queue (IFQ) improves the retention of active flows, enhancing overall table management.

For the second aspect, we introduce RICHRIN, a mechanism that combines historical and real-time information to prevent the installation of unnecessary flow rules that contribute little to cache hit rates. RICHRIN effectively filters out a significant portion of these unproductive flows, reducing pollution in the flow table.

To evaluate the performance of TST and RICHRIN, we conduct experiments using real network packet traces from CAIDA. The results demonstrate that these mechanisms significantly improve the rule cache hit ratio and substantially reduce the number of rule installations. This research effectively addresses the scalability problem of the flow table and provides valuable insights for optimizing flow table management in SDN environments.

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来源期刊

Future Generation Computer Systems-The International Journal of Escience 工程技术-计算机：理论方法

CiteScore

19.90

自引率

2.70%

发文量

376

审稿时长

10.6 months

期刊介绍： Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications. Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration. Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.