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Effective switch memory management in OpenFlow networks

Published: 26 May 2014 Publication History

Abstract

OpenFlow networks require installation of flow rules in a limited capacity switch memory (Ternary Content Addressable Memory or TCAMs, in particular) from a logically centralized controller. A controller can manage the switch memory in an OpenFlow network through events that are generated by the switch at discrete time intervals. Recent studies have shown that data centers can have up to 10,000 network flows per second per server rack today. Increasing the TCAM size to accommodate these large number of flow rules is not a viable solution since TCAM is costly and power hungry. Current OpenFlow controllers handle this issue by installing flow rules with a default idle timeout after which the switch automatically evicts the rule from its TCAM. This results in inefficient usage of switch memory for short lived flows when the timeout is too high and in increased controller workload for frequent flows when the timeout is too low.
In this context, we present SmartTime - an OpenFlow controller system that combines an adaptive timeout heuristic to compute efficient idle timeouts with proactive eviction of flow rules, which results in effective utilization of TCAM space while ensuring that TCAM misses (or controller load) does not increase. To the best of our knowledge, SmartTime is the first real implementation of an intelligent flow management strategy in an OpenFlow controller that can be deployed in current OpenFlow networks. In our experiments using multiple real data center packet traces and cache sizes, SmartTime adaptive policy consistently outperformed the best performing static idle timeout policy or random eviction policy by up to 58% in terms of total cost.

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  • (2024)Adaptive Flow Timeout Management in Software-Defined Optical NetworksPhotonics10.3390/photonics1107059511:7(595)Online publication date: 26-Jun-2024
  • (2024)Predictive mobility and cost-aware flow placement in SDN-based IoT networks: a Q-learning approachJournal of Cloud Computing10.1186/s13677-024-00589-w13:1Online publication date: 25-Jan-2024
  • (2024)Maximizing SDN Flow Table Efficiency with Dynamic Timeout Allocation and Proactive Eviction2024 15th International Conference on Network of the Future (NoF)10.1109/NoF62948.2024.10741357(10-18)Online publication date: 2-Oct-2024
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cover image ACM Conferences
DEBS '14: Proceedings of the 8th ACM International Conference on Distributed Event-Based Systems
May 2014
371 pages
ISBN:9781450327374
DOI:10.1145/2611286
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 26 May 2014

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Author Tags

  1. OpenFlow
  2. idle timeout
  3. software defined networking

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DEBS '14

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DEBS '14 Paper Acceptance Rate 16 of 174 submissions, 9%;
Overall Acceptance Rate 145 of 583 submissions, 25%

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Cited By

View all
  • (2024)Adaptive Flow Timeout Management in Software-Defined Optical NetworksPhotonics10.3390/photonics1107059511:7(595)Online publication date: 26-Jun-2024
  • (2024)Predictive mobility and cost-aware flow placement in SDN-based IoT networks: a Q-learning approachJournal of Cloud Computing10.1186/s13677-024-00589-w13:1Online publication date: 25-Jan-2024
  • (2024)Maximizing SDN Flow Table Efficiency with Dynamic Timeout Allocation and Proactive Eviction2024 15th International Conference on Network of the Future (NoF)10.1109/NoF62948.2024.10741357(10-18)Online publication date: 2-Oct-2024
  • (2024)Flow-based Service Time optimization in software-defined networks using Deep Reinforcement LearningComputer Communications10.1016/j.comcom.2023.12.038216(54-67)Online publication date: Feb-2024
  • (2024)Unmasking SDN flow table saturation: fingerprinting, attacks and defensesInternational Journal of Information Security10.1007/s10207-024-00897-x23:6(3465-3479)Online publication date: 4-Aug-2024
  • (2023)Flow Table Saturation Attack against Dynamic Timeout Mechanisms in SDNApplied Sciences10.3390/app1312721013:12(7210)Online publication date: 16-Jun-2023
  • (2023)SFTO-Guard: Real-time detection and mitigation system for slow-rate flow table overflow attacksJournal of Network and Computer Applications10.1016/j.jnca.2023.103597213(103597)Online publication date: Apr-2023
  • (2022)Dynamic Cluster-Based Flow Management for Software Defined NetworksIEEE Transactions on Services Computing10.1109/TSC.2019.294385215:1(361-371)Online publication date: 1-Jan-2022
  • (2022)Attack Resilience of Statistics-based Recently Used Cache Replacement Policy in Software-Defined Networks2022 International Conference on Intelligent Technology, System and Service for Internet of Everything (ITSS-IoE)10.1109/ITSS-IoE56359.2022.9990963(1-6)Online publication date: 3-Dec-2022
  • (2022)Performance Evaluation of Flowtable Eviction Mechanisms for Software Defined Networks considering Traffic Flows variabilities2022 IEEE 12th Symposium on Computer Applications & Industrial Electronics (ISCAIE)10.1109/ISCAIE54458.2022.9794547(71-75)Online publication date: 21-May-2022
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