Decision Tree‐Based Entries Reduction scheme using multi‐match attributes to prevent flow table overflow in SDN environment

NallusamyPriyanka,; SaravanenSapna,; Murugan, Krishnan

doi:10.1002/nem.2141

Cited by 11 publications

(6 citation statements)

References 45 publications

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“…The controller overhead has been significantly reduced in this work. 28 In summary, it can be said that history-based techniques need to store large volumes of data for preprocessing although the eviction decision is online. Instantaneous decisions do not require any additional data for decision making.…”

Section: History-based Eviction Techniquesmentioning

confidence: 99%

“…Nallusamy et al 28 Decision tree-based eviction scheme is used. A secondary cache is used to temporarily store the evicted flow rule until its reinstallation History-based The controller's overhead is reduced substantially.…”

Section: Huang and Youn 26mentioning

confidence: 99%

“…Another work introduces a secondary cache to hold the evicted flow rules such that the same rules could be reinstalled without experiencing controller delay. The controller overhead has been significantly reduced in this work 28 …”

Section: Background and Motivationmentioning

confidence: 99%

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A novel eviction policy based on shortest remaining time for software defined networking flow tables

Dhandapani,

Thanganadar Thangathai,

Hamead Haja Moinudeen

2023

Int J Network Mgmt

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SummarySoftware defined networking is a modern paradigm that divides the control plane from the data plane for improved network manageability. A flow table in the data plane has limited and expensive memory called TCAM. The presence of unwanted flow rules would lead to flow bloat conditions and make the lookup operation inefficient. Eviction schemes based on LRU policy have been widely studied in the literature which preempts the life of the least recently used flow rule and reduces the occupancy of the flow table. LRU considers the past behavior for the eviction of a flow rule. This paper proposes a novel policy that preempts a flow rule by considering its future characteristic, the shortest remaining time (SRT). A rule with a higher probability of being used is avoided from eviction to mitigate the degradation of performance. The modeling of the SRT technique exhibits better utilization of a flow rule that has higher probability of being used. On the other hand, LRU does not guarantee that the evicted flow rule will not be used frequently in the future and it has been shown that an incorrectly evicted flow rule incurs controller delay. The experimental results show that for different traffic rates, SRT has reduced the delay by 15%, reinstallation count by 25%, and jitter by 40%. SRT has increased utilization by 22% compared to LRU.

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Section: History-based Eviction Techniquesmentioning

confidence: 99%

Section: Huang and Youn 26mentioning

confidence: 99%

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A novel eviction policy based on shortest remaining time for software defined networking flow tables

Dhandapani,

Thanganadar Thangathai,

Hamead Haja Moinudeen

2023

Int J Network Mgmt

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“…In order to prevent flow table overflow numerous techniques were proposed considering the following factors. In the schemes, [9][10][11] the flow table overflow is prevented by entries aggregation. These approaches prevent flow table overflow by counting all the entries periodically.…”

Section: Introductionmentioning

confidence: 99%

A novel machine learning‐based classification approach to prevent flow table overflow attack in Software‐Defined Networking

Karthikeyan¹,

Murugan²

2023

Concurrency and Computation

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SummarySoftware‐defined networking (SDN) is an emerging networking architecture where the network control is physically separated from the forwarding plane. It is dynamic, manageable, cost‐effective, and flexible, which is ideal for today's high‐bandwidth applications. The flow table is the fundamental data structure residing in ternary content addressable memory (TCAM) that provides flow rules for incoming flows. The TCAM is the costliest part of an SDN switch that can store limited flow rules. Hence, it is susceptible to distributed denial of service (DDoS) and more specifically to Transformed DDoS (TDDoS). It targets the flow tables, exhausting its limited resource, and resulting in flow table overflow. During overflow, the controller is incapable of installing new rules to the switch, and the switch function is disabled from the network. In this paper, a novel framework named machine learning‐based overflow prevention (MLOP) was proposed which includes a victim switch was identified using association rule mining algorithm concepts. Then, the fuzzy C‐means (FCM) was applied to classify the attack in the victim switch. In addition, FCM ensemble with an optimizing method called dynamic time warping (DTW) which detects similar patterns in the time window during classification. Finally, an elbow method is used that determines the cluster count for optimizing clusters which increases the attack detection and also increases the holding time with reduced packet loss. The real‐time network traffic datasets were used for simulations, and the results were compared with other state‐of‐the‐art approaches. The experimental results show that MLOP increases the holding time by 15% on average and reduces the packet loss due to flow table Overflow by 22.81% than the other existing approaches. In addition, the throughput is increased in the proposed MLOP from 10% to 40%, and end‐to‐end delay is reduced between the variations of 10% to 70%.

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“…Even though it provides a significant advantage of entry reduction, it consumes the confined TCAM memory. A similar method called Fast Flow Table Aggregation 15 uses binary search tree construction along with Optimal Routing 16 was proposed that enhances the flow rule management with a lower computational cost. The proposed technique uses the Machine Learning (ML) algorithm called Decision Tree.…”

Section: Introductionmentioning

confidence: 99%

AGFT: Adaptive entries aggregation scheme to prevent overflow in multiple flow table environment

Nallusamy

Reshmi²,

Murugan³

2021

Concurrency and Computation

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The revolutionary architecture termed Software-Defined Network provides flexible network management by detaching the control logic from the underlying data plane.The flow table resides in Ternary Content Addressable Memory imparts rules for the incoming flows with a limitation of high cost, limited storage, and consumes high power.In data center networks, when the traffic rate is high the overflow occurs due to storage limitation with high packet drop, frequent rule miss, and severe controller overhead. To overcome these challenges and to provide Quality of Service to the current network design sketch-based entry reduction scheme is proposed. It incorporates three concurrent modules integrated to function sequentially where (1) Periodical analysis in Multiple Flow Tables is performed to ensure the availability of redundant entries using the robust data mining algorithm called Term Frequency. (2) Recurrent entries are further classified and clustered using the Boyer-Moore pattern matching algorithm to facilitate the forthcoming aggregation process. (3) A compact flow table is achieved with a customized multibit trie using Huffman coding compression technique. The experimental outcomes prove that this work prevents overflow by 99.98% with 98.99% enhanced flow table space and provides a significant reduction of controller overhead than the existing schemes.

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Decision Tree‐Based Entries Reduction scheme using multi‐match attributes to prevent flow table overflow in SDN environment

Cited by 11 publications

References 45 publications

A novel eviction policy based on shortest remaining time for software defined networking flow tables

A novel eviction policy based on shortest remaining time for software defined networking flow tables

A novel machine learning‐based classification approach to prevent flow table overflow attack in Software‐Defined Networking

AGFT: Adaptive entries aggregation scheme to prevent overflow in multiple flow table environment

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