AMPS: Application aware multipath flow routing using machine learning in SDN

Pasca, S Thomas Valerrian; Kodali, Siva Sairam Prasad; Kataoka, Kotaro

doi:10.1109/ncc.2017.8077095

Cited by 50 publications

(42 citation statements)

References 8 publications

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“…Some researchers in other works have considered utilizing the SDN architecture for network application classification. Prasad and Kataoka designed an application‐aware multipath packet forwarding mechanism by combining machine learning and SDN. To achieve the application awareness, the C4.5 decision tree algorithm was used to build the application classifier, and the machine learning–based trainer and classifier were integrated in the controller because of a global view and the logically centralized control capability.…”

Section: Background and Related Workmentioning

confidence: 99%

“…In recent years, many research works have already applied machine learning methods in network application classification. [2][3][4][5][6][7][8][9][10][11]23,24 Most of them are focused on improving the machine learning algorithm and feature selection since the selected features and machine learning algorithm have a great effect on the classifier's performance.…”

Section: Related Workmentioning

confidence: 99%

“…To achieve accurate network applications, a large number of different application classification methods have been proposed in the past few decades, mainly including port‐based approach, payload‐based approach, and machine learning–based approach . In current years, the most widely used technology for application classification is the machine learning–based approach, in which backpropagation (BP) neural network, Bayesian network, support vector machine (SVM), and C4.5 decision tree are usually applied to the classification …”

Section: Introductionmentioning

confidence: 99%

“…2 In current years, the most widely used technology for application classification is the machine learning-based approach, in which backpropagation (BP) neural network, Bayesian network, support vector machine (SVM), and C4.5 decision tree are usually applied to the classification. [3][4][5][6][7][8][9][10][11] Different from identifying port number in the port-based approach as well as inspecting payload content in the payload-based approach, in machine learning-based classification methods, the flow features statistics, including the size of packet, interpacket time, and flow duration time, are used to automatically identify and classify network application by using machine learning. The shallow neural network is generally used to build the application classifier in lots of machine learning-based classification methods.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Deep learning–based network application classification for SDN

Zhang

Wang

et al. 2018

Trans Emerging Tel Tech

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Accurate application classification is important and useful to improve network performance. However, with the continuous expansion of network scale and the rapid increase of network users, it is very difficult for the existing application classification methods to accurately identify and classify network applications. Currently, most classification methods are suitable for small‐scale data sets and cannot achieve high classification accuracy because of the shallow learning structure and the limited learning ability. The emergence of deep learning technology and software‐defined networking (SDN) enables the application classification method to process large‐scale data. In this paper, by leveraging the SDN architecture, we present a novel hybrid deep neural network–based application classification method, which achieves high classification accuracy without the manual feature selection and extraction. In the proposed application classification framework, by taking the advantage of the logical centralized control and powerful computing capability, the massive network traffic is easily collected and processed by the SDN controller. The processed data is used to train the hybrid deep neural network, which is composed of the stacked autoencoder and softmax regression layer. The deep flow features can be obtained from the stacked autoencoder automatically instead of the manual feature selection and extraction. The softmax regression layer is used as the classifier to realize the application classification. Finally, simulation results demonstrate that our proposed classification method is effective and gets higher classification accuracy than the support vector machine–based classification method.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Deep learning–based network application classification for SDN

Zhang

Wang

et al. 2018

Trans Emerging Tel Tech

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“…As for the application of route choosing and traffic flow identification in regard of mobile edge network, [39] uses the characters of SDN and combines them with decision tree algorithm to categorize traffic flow, allowing the controller to choose an access point from the local network that is suitable to be connected to a mobile device according to the level of congestion of the backhaul route. Reference [40] proposes a model called AMPS that combines SDN and machine learning. The model is capable of categorizing the differences of each traffic flow by means of learning packet identification and then chooses the best route of transmission for the traffic flow to achieve the automation of the optimal selection of routing path.…”

Section: Background and Related Workmentioning

confidence: 99%

Micro Operator Design Pattern in 5G SDN/NFV Network

Tseng

Huang

Tseng

et al. 2018

Wireless Communications and Mobile Computing

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The trend of 5G mobile networks is increasing with the number of users and the transmission rate. Many operators are turning to small cell and indoor coverage of telecom network service. With the emerging Software Defined Networking and Network Function Virtualization technologies, Internet Service Provider is able to deploy their networks more flexibly and dynamically. In addition to the change of the wireless mobile network deployment model, it also drives the development trend of the Micro Operator ( O). Telecom operators can provide regional network services through public buildings, shopping malls, or industrial sites. In addition, localized network services are provided and bandwidth consumption is reduced. The distributed architecture of O tackles computing requirements for applications, data, and services from cloud data center to edge network devices or to the micro data center of O. The service model of O is capable of reducing network latency in response to the low-latency applications for future 5G edge computing environment. This paper addresses the design pattern of 5G micro operator and proposes a Decision Tree Based Flow Redirection (DTBFR) mechanism to redirect the traffic flows to neighbor service nodes. The DTBFR mechanism allows different Os to share network resources and speed up the development of edge computing in the future.

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Fault tolerant resource allocation in fog environment using game theory‐based reinforcement learning

Divya

2021

Concurrency and Computation

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Realtime decision making is associated with an on-demand, latency aware resource allocation. Fog nodes along with cloud infrastructure, when used effectively can ensure real-time decision making. In this article, we propose an efficient resource allocation and fault tolerance mechanism for the fog layer. Our work takes the advantage of game theory, where Nash equilibrium is the initial allocation strategy, which is then passed on to the reinforcement learner. The allocation is done proactively based on the network status and traffic history. The performance of our system is compared with the existing open shortest path first and neural network algorithms. Besides, fault tolerance mechanism has also been proposed which takes the advantage of the fail-over cluster formation to find the link failure and provide an alternate path in the smart switch, which is the networking component of the fog network. The proposed work gives an improved recovery time and average service time in case of failure with a recovery time of 32 ms. The experimental results are justified in terms of improved service time, lower delay, and optimal energy utilization. K E Y W O R D Sdeep reinforcement learning, failover cluster, fog computing, game theory, resource allocation INTRODUCTIONA surge in the importance of sensors and real-time monitoring has brought about the convergence of two major technologies, the cloud and Internet of Things (IoT), which lead to the emergence of cloud of Things. 1 Processing of large streaming data and providing low latent, power-efficient, high accurate decision making has yet again emerged a new technology-Fog Computing, proposed by CISCO. 2,3 This computing layer helps to alleviate congestion in the cloud by the execution of time-sensitive tasks and data filtering near the edge. 4 Researchers use the terms Edge/fog/mist computing based on the distance of the computing layer from the source of data. 5 Our work takes the advantage of the fog layer to execute the latency aware tasks and effectively distribute the load in the fog layer.One of the most commonly used techniques for load distribution is task or code offloading 6 which helps to distribute the load from the cloud to the lower layer. Latency sensitive tasks are offloaded to the intermediate layer near the edge for better performance. 7,8 This helps in a significant reduction of data being transferred to a remote location, thereby saving bandwidth utility.Software-defined network (SDN) plays a major role as the networking component in a fog network creation. The SDN is an intelligent paradigm of networking, which consists of two planes, the data and the control plane to take care of delivery and networking of the application. 9,10 The Open-Flow protocol ensures the communication between the control plane and the data plane, 11 which instructs the data plane reactively or proactively on packet handling.Traffic engineering plays a key role in path-finding and route establishment in a network. A good algorithm enhances the throughput and minimizes the d...

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AMPS: Application aware multipath flow routing using machine learning in SDN

Cited by 50 publications

References 8 publications

Deep learning–based network application classification for SDN

Deep learning–based network application classification for SDN

Micro Operator Design Pattern in 5G SDN/NFV Network

Fault tolerant resource allocation in fog environment using game theory‐based reinforcement learning

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