A classification-based elephant flow detection method using application round on SDN environments

Huang, Yuanhao; Shih, Wen-Yueh; Huang, Jia-Hong

doi:10.1109/apnoms.2017.8094140

Cited by 12 publications

(12 citation statements)

References 3 publications

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“…Figure 7 shows the accuracy of our purposed classification for various training sizes. We observed that our EF detection method on the controller-side achieves a higher accuracy than the existing EEFD method [21] by up to 0.7%, and the classification-based EDMAR [22] by up to 0.5%.…”

Section: ) Classification Accuracymentioning

confidence: 81%

“…Figures 8(a) and 8(b) illustrate the precision and recall of our method compared to EDMAR [22], FlowSeer [23], and the Bayes network (BayesNet) [51]. Our method performs better in terms of accuracy, precision, and recall because the controller-side classifier becomes more accurate with an increase in the number of features used.…”

Section: ) Classification Accuracymentioning

confidence: 99%

“…However, this study has some scalability limitations when connecting to large-scale networks that are more complex and dynamic, such as multi-tenant cloud networks, large virtualized data centers, and Internet-of-Things (IoT). Huang et al [22] proposed the arrangement of a pair of classifiers that respectively run on the controller and switch. Due to a limited switch computing power, only rules and decision trees classifiers can be used on the switch side.…”

Section: ) Switch Based Detectionmentioning

confidence: 99%

“…These features make the VFDT Table 3 shows the overall performance of our proposed method. We compared the performance of our EF detection method with that of the EDMAR method [22], FlowSeer [23], and the EEFD method [21]. The experiments were conducted on the MAWI dataset [52], UNI1 Dataset [49], and UNI2 Dataset [49].…”

Section: ) Vfdt Performance Comparisonmentioning

confidence: 99%

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Flow-Aware Elephant Flow Detection for Software-Defined Networks

et al. 2020

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Software-defined networking (SDN) separates the network control plane from the packet forwarding plane, which provides comprehensive network-state visibility for better network management and resilience. Traffic classification, particularly for elephant flow detection, can lead to improved flow control and resource provisioning in SDN networks. Existing elephant flow detection techniques use pre-set thresholds that cannot scale with the changes in the traffic concept and distribution. This paper proposes a flow-aware elephant flow detection applied to SDN. The proposed technique employs two classifiers, each respectively on SDN switches and controller, to achieve accurate elephant flow detection efficiently. Moreover, this technique allows sharing the elephant flow classification tasks between the controller and switches. Hence, most mice flows can be filtered in the switches, thus avoiding the need to send large numbers of classification requests and signaling messages to the controller. Experimental findings reveal that the proposed technique outperforms contemporary methods in terms of the running time, accuracy, F-measure, and recall. INDEX TERMS Software-defined networking, flow classification, elephant flow detection.

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Section: ) Classification Accuracymentioning

confidence: 81%

Section: ) Classification Accuracymentioning

confidence: 99%

Section: ) Switch Based Detectionmentioning

confidence: 99%

Section: ) Vfdt Performance Comparisonmentioning

confidence: 99%

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Flow-Aware Elephant Flow Detection for Software-Defined Networks

et al. 2020

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“…With the introduction of artificial intelligence technology [16][17][18][19], the traditional strategy, which uses posterior statistics to detect the elephant flow, has changed. The historical traffic data are used for training with machine learning algorithms, and a classifier can be built to mine the mapping relationship between traffic class and early features of traffic flow [20] [21].…”

Section: Introductionmentioning

confidence: 99%

Regression Based Dynamic Elephant Flow Detection in Airborne Network

Liu

Chen³

et al. 2020

IEEE Access

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Towards threshold‐agnostic heavy‐hitter classification

et al. 2021

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A heavy-hitter (HH) network traffic flow consumes considerably more network resources than other flows combined. The classification of HHs is critical to provide, among others, the required level of Quality of Service and reliability in both conventional and data center networks. HH classification is typically threshold-based. However, there is no consistent and accepted threshold or set of thresholds that would reliably classify flows. Furthermore, existing threshold-driven approaches use counters (e.g., duration, packets, and bytes); thus, their accuracy depends on how complete the flow information is. This paper paves the way to threshold-agnostic HH identification by proposing an approach that performs HH classification based on per-flow packet size distribution (PSD) and template matching (TM). PSD allows capturing the behavior and dynamism of network traffic flows (even from their first few packets). TM enables to classify HHs by measuring the similarity between the PSD of observed flows and a set of master templates representing the flow size behavior of HH classes. We evaluated the PSD-and TM-based approach using flows extracted from real traffic traces. Results show that our approach classifies HHs accurately and timely, corroborating that the threshold-less perspective is feasible for HH identification.

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A classification-based elephant flow detection method using application round on SDN environments

Cited by 12 publications

References 3 publications

Flow-Aware Elephant Flow Detection for Software-Defined Networks

Flow-Aware Elephant Flow Detection for Software-Defined Networks

Regression Based Dynamic Elephant Flow Detection in Airborne Network

Towards threshold‐agnostic heavy‐hitter classification

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