Sample and Fetch-Based Large Flow Detection Mechanism in Software Defined Networks

Xing, Changyou; Ding, Ke; Hu, Chao; Chen, Ming

doi:10.1109/lcomm.2016.2585480

Cited by 16 publications

(10 citation statements)

References 8 publications

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“…Li et al [29] developed a method to determine which flows are most informative to construct a measurement flow set iteratively until an accuracy requirement is satisfied or a measurement resource constraint is reached. FlowMon [30] provided a sample and fetch based mechanism to detect large flows. The sample mechanism detects suspicions large flows and the fetch mechanism install measurements rules in the OF switches.…”

Section: Related Workmentioning

confidence: 99%

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A MapReduce Approach for Traffic Matrix Estimation in SDN

2020

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A traffic matrix (TM) is a source of critical traffic throughput information for traffic engineering activities and network management tasks such as traffic prediction, capacity planning, network provisioning, and anomaly detection. However, estimating TM poses several challenges for network engineers. One of the challenges is that traffic data statistics are constantly changing, and their aggregation for real-time monitoring becomes a difficult task. This paper presents a near real-time TM estimation approach for OpenFlow (OF) networks. It makes use of Big Data techniques based on MapReduce operations to tackle the aggregation problem. The proposed method uses traffic data statistics collected from OF switches through an SDN controller as input and aggregates these data in a Big Data streaming processing environment. This paper explores the benefits of the distributed MapReduce computing model to provide an estimate of the TM for all origin-destination (OD) pairs of hosts in the network in two ways: 1) the accumulated throughput and 2) the throughput between two sequential TM estimates. This procedure enables network engineers to monitor the behavior and evolution of the throughput on each OD pair in the network and on each link in the path between each OD pair. The generated TM is persisted in a NoSQL database and can be made available for a variety of network traffic monitoring applications. The results of the simulations show the potential of the proposed MapReduce approach for TM estimation.

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Section: Related Workmentioning

confidence: 99%

“…This function searches the set L to provide these data. Using them, the elements of LP can be generated (lines [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34]. For each value returned from the getPorts() function, an entry in LP is added.…”

Section: ) Generate Lp Set Algorithmmentioning

confidence: 99%

A MapReduce Approach for Traffic Matrix Estimation in SDN

2020

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“…However, for long flow detection, the existing solution often does not meet the requirements of long flow detection accuracy and suffers from feasibility issues [25]. FlowMon fisrt captured the suspicious long flow through coarse-grained sampling method optimized the TCAM resource allocation [26].…”

Section: Samplingmentioning

confidence: 99%

“…Sonum reduces the consumption of hardware resources. We compare the missed detection ratio of Sonum with FlowMon [26] in Figure 7. FlowMon is a sampling method based on SDN.…”

mentioning

confidence: 99%

Sonum: Software-Defined Synergetic Sampling Approach and Optimal Network Utilization Mechanism for Long Flow in a Data Center Network

Tan

Cheng

et al. 2019

Applied Sciences

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Long flow detection and load balancing are crucial techniques for data center running and management. However, both of them have been independently studied in previous studies. In this paper, we propose a complete solution called Sonum, which can complete long flow detection and scheduling at the same time. Sonum consists of a software-defined synergetic sampling approach and an optimal network utilization mechanism. Sonum detects long flows through consolidating and processing sampling information from multiple switches. Compared with the existing prime solution, the missed detection rate of Sonum is reduced by 2.3%–5.1%. After obtaining the long flow information, Sonum minimizes the potential packet loss rate as the optimization target and then translates load balancing into an optimization problem of arranging a minimum packet loss path for long flows. This paper also introduces a heuristic algorithm for solving this optimization problem. The experimental results show that Sonum outperforms ECMP and Hedera in terms of network throughput and flow completion time.

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“…Another advantage of these TCAM-based methods is that they can be easily deployed in OpenFlow-like devices. Some previous works, such as sampling based methods [12], [17], and sketch based methods [18], are unfriendly to OpenFlow-like devices, since they need to reconstruct the data plane devices to support their features.…”

Section: Introductionmentioning

confidence: 99%

A Distributed Hierarchical Heavy Hitter Detection Method in Software-Defined Networking

2019

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Software-defined networking (SDN) enables fast innovation in networks. The management and measurement features that we require can be easily implemented in SDN. However, when simplicity is introduced, SDN also faces some problems, of which the most challenging one is the scalability problem. As the foundation of the networking management, scalability of the traffic measurement is also important. Recently, many works have focused on TCAM-based measurement, which is considered to be scalable and efficient enough. In this paper, we propose a distributed hierarchical heavy hitter (HHH) detection method, which is also a TCAM-based method. Unlike previous works, this method focuses on optimizing the detection speed by dynamically controlling the resource allocated to each measurement task. We propose an efficient solution to solve the optimization problem. The simulation with network-wide tasks and diverse traffic has shown that this method can improve the detection speed when compared with other resource allocation methods, and it can work better under strict resource limitation.INDEX TERMS Hierarchical heavy hitter, software-defined networking, dynamic resource allocation.

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Sample and Fetch-Based Large Flow Detection Mechanism in Software Defined Networks

Cited by 16 publications

References 8 publications

A MapReduce Approach for Traffic Matrix Estimation in SDN

A MapReduce Approach for Traffic Matrix Estimation in SDN

Sonum: Software-Defined Synergetic Sampling Approach and Optimal Network Utilization Mechanism for Long Flow in a Data Center Network

A Distributed Hierarchical Heavy Hitter Detection Method in Software-Defined Networking

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