An approach for SDN traffic monitoring based on big data techniques

Queiroz, Wander; Capretz, Miriam A. M.; Dantas, Mário A. R.

doi:10.1016/j.jnca.2019.01.016

Cited by 65 publications

(42 citation statements)

References 12 publications

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“…Among all these features, the deadline hit ratio and end-to-end delay are the important issues that this paper addresses. In [29], providing real time requirements in SDN networks (especially hitting the deadlines) is not addressed at all, while the main challenge of our research is to present a solution to provide real time SDN-based networks for the use of telesurgery application. In [30], the authors propose a method based on spectral clustering for SDN traffic classification.…”

Section: ) Network Componentmentioning

confidence: 99%

RT-TelSurg: Real Time Telesurgery Using SDN, Fog, and Cloud as Infrastructures

Sedaghat

Jahangir

2021

IEEE Access

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This paper proposes a novel and efficient real time network architecture, named RT-TelSurg, for one of the most appealing tactile Internet applications, i.e., Telesurgery. In telesurgery, the patient's vital signs and status and the required robotic commands during the surgery should be received on time. Otherwise, the life of the patient or the safety of the operation is endangered. Hence, transmitted packets should meet their respective relative deadlines. Software-defined networking is a relatively new architecture for computer and telecommunications networks in which the network control plane is separated from the data plane. One way to achieve real time telesurgery is to employ cloud and fog networks using SDN as infrastructure. By using a real time cloud controller in the SDN as the core and a fog controller on the edge of the network on the master (physician) side, one may satisfy the acceptable level of the timing constraints of the telesurgery. Accordingly, based on the presented architecture, we develop the statistical performance model of our proposed approach, RT-TelSurg. This statistical queuing theory model is designed for critical and non-critical states of the network. Based on these states, network resources are allocated to telesurgery data in both modes, and a computational bottleneck is detected. RT-TelSurg is evaluated according to real time and surgery efficiency parameters. The results show that the average deadline hit ratio is 98.2% in different conditions, which is quite acceptable for telesurgery applications.

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Section: ) Network Componentmentioning

confidence: 99%

RT-TelSurg: Real Time Telesurgery Using SDN, Fog, and Cloud as Infrastructures

Sedaghat

Jahangir

2021

IEEE Access

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“…This led to a reduction in the network overheads caused by the conventional periodic polling method of collecting flow statistics and performs real-time information collection based on a representational state transfer (REST) interface. In [12], Queiroz et al proposed a granular traffic monitoring method that collects and generates traffic statistics in real time using counter values defined by OpenFlow protocol. Yang and Yeung designed a periodic flow statistics collection scheme in SDN-capable switches to reduce the bandwidth cost of the SDN control plane and the number of flow rules on the switch for collecting flow statistics [13].…”

Section: A Traffic Samplingmentioning

confidence: 99%

Deep Reinforcement Learning-Based Traffic Sampling for Multiple Traffic Analyzers on Software-Defined Networks

2021

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Intrusion detection system (IDS) and deep packet inspection (DPI) are widely used to detect network attacks and anomalies, thereby enhancing cyber-security. Conventional traffic analyzers such as IDS have fixed locations and a limited capacity to perform DPI on large volumes of network traffic. Nowadays, software-defined networking (SDN) technology, which provides flexibility, elasticity, and programmability by decoupling the network control and data planes, makes it possible to capture entire or a certain portion of data traffic flows on SDN-capable switches and steer the captured network traffic to one of the traffic analyzers on the network. Therefore, how to sample network traffic and where to steer the sampled traffic among multiple traffic analyzers are critical problems facing cyber-security. Since there is a possibility that potentially useful information will be lost in not-captured traffic, deciding the sampling points and sampling rates of network traffic remains important. Additionally, after determining the sampling points and rates, sampled traffic must be sent to one of the multiple traffic analyzers for traffic inspection, which may incur additional network delivery overheads. We propose a less-intrusive traffic sampling mechanism for multiple traffic analyzers on an SDN-capable network using a deep deterministic policy gradient (DDPG), which is a representative deep reinforcement learning (DRL) algorithm for continuous action control. The proposed system learns sampling resource allocation policy under the uncertainty of flow distribution according to sampled traffic inspection results obtained from multiple traffic analyzers. Through extensive simulations and the SDN-based testbed experiments, we demonstrate that the proposed approach has a high probability of capturing malicious flows while maintaining a balanced load of multiple traffic analyzers and reducing flow monitoring overheads.

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“…The proposed MapReduce approach is part of a TE method based on Big Data techniques shown in Fig. 1 [41,42]. This TE method delivers fine-grained statistical analysis of network resources, such as the ratio of each port in the switch payload, throughput capacity used on ports, and the TM estimation.…”

Section: Mapreduce Approach For Tm Estimationmentioning

confidence: 99%

“…As shown in Fig. 1, the collection process is carried out by the Big Data Streaming module, and this process is detailed by Queiroz et al [41]. For the context of this paper, it is considered that the Big Data Streaming module provides the traffic data statistics as messages, which are used as input for the proposed MapReduce Approach.…”

Section: A Mapreduce Designmentioning

confidence: 99%

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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An approach for SDN traffic monitoring based on big data techniques

Cited by 65 publications

References 12 publications

RT-TelSurg: Real Time Telesurgery Using SDN, Fog, and Cloud as Infrastructures

RT-TelSurg: Real Time Telesurgery Using SDN, Fog, and Cloud as Infrastructures

Deep Reinforcement Learning-Based Traffic Sampling for Multiple Traffic Analyzers on Software-Defined Networks

A MapReduce Approach for Traffic Matrix Estimation in SDN

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