An Approach Based on Knowledge-Defined Networking for Identifying Heavy-Hitter Flows in Data Center Networks

Duque-Torres, Alejandra; Amezquita-Suarez, Felipe; Rendón, Óscar Mauricio Caicedo; Ordóñez, Armando; Campo, Wilmar Yesid

doi:10.3390/app9224808

Cited by 12 publications

(9 citation statements)

References 63 publications

(91 reference statements)

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“…Software-defined networking is able to organize and manage up to thousands of network devices through a point of management, network monitoring both in terms of resources and connectivity, change the behavior of the networks automatically, maximizing the use of devices such as network bandwidth optimization, load balancing, traffic engineering and others associated with the programmability and scalability [5], [6]. Previously published studies have explored how SDN can provide better mechanisms for common network management and configuration tasks across a variety of problems including Internet of Things [7], cellular SDN [8], and data centre networks [9].…”

Section: Introductionmentioning

confidence: 99%

Software Defined Networks: a Comparative Study and Quality of Services Evaluation

Nugroho

Irfan

Faruq

2019

SJI

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Software-Defined Network (SDN) as architecture network that separates the control and forwarding functions, so that network operators and administrators can configure the networks in a simple and centrally between thousands of devices. This study is designed and evaluate the Quality of Services (QoS) performances between the two networks employed SDN-based architecture and without SDN-based. MinNet as a software emulator used as a data plane in the network Software Define Network. In this study, comparison of the value of the QoS on the network based on Software Defined Network and traditional network during the test run from the source node is investigated. Network testing by using traffic loads. Traffic loads are used starting from 20Mbps-100Mbps. The result is verified that the QoS analysis of the Software-Defined Network architecture performed better than conventional network architectures. The value of the latency delay on the Software Define Network range between 0,019-0,084ms, and with 0% packet loss when addressed the network traffics of 10-100Mbps.

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Section: Introductionmentioning

confidence: 99%

Software Defined Networks: a Comparative Study and Quality of Services Evaluation

Nugroho

Irfan

Faruq

2019

SJI

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“…Saber et al [16] had a similar research concern and proposed a cost-sensitive classification method that can effectively reduce classification latency. Deque-torres et al [126] proposed a knowledge-defined networking (KDN) based approach for identifying heavy hitters in data center networks, where the efficient threshold for the heavy hitter detection was determined through clustering analysis. Unfortunately, the scheme was not compared with other intelligent methods, thus failing in proving its superiority.…”

Section: B Flow Classificationmentioning

confidence: 99%

Machine Learning Empowered Intelligent Data Center Networking: A Survey

Li¹,

Wang²,

Yang³

et al. 2022

Preprint

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“…A. Duque-Torres, F. Amezquita-Su´arez, O. M. Caicedo Rendon, A. Ord´o˜nez, and W. Y. Campo provided a solution to tackle the issue of big-size traffic flows, which consume the network resources more than different flows consolidated. They proposed a method for investigating the usefulness of applying Knowledge Defined Networking (KDN) in big-size traffic flows classification by using ML [16].…”

Section: A Supervised Machine Learningmentioning

confidence: 99%

FDPHI: Fast Deep Packet Header Inspection for Data Traffic Classification and Management

Al-Khalidi¹,

Yaseen²

2021

IJIES

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Traffic classification is referred to as the task of categorizing traffic flows into application-aware classes such as chats, streaming, VoIP, etc. Most systems of network traffic identification are based on features. These features may be static signatures, port numbers, statistical characteristics, and so on. Current methods of data flow classification are effective, they still lack new inventive approaches to meet the needs of vital points such as real-time traffic classification, low power consumption, ), Central Processing Unit (CPU) utilization, etc. Our novel Fast Deep Packet Header Inspection (FDPHI) traffic classification proposal employs 1 Dimension Convolution Neural Network (1D-CNN) to automatically learn more representational characteristics of traffic flow types; by considering only the position of the selected bits from the packet header. The proposal a learning approach based on deep packet inspection which integrates both feature extraction and classification phases into one system. The results show that the FDPHI works very well on the applications of feature learning. Also, it presents powerful adequate traffic classification results in terms of energy consumption (70% less power CPU utilization around 48% less), and processing time (310% for IPv4 and 595% for IPv6).

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An Approach Based on Knowledge-Defined Networking for Identifying Heavy-Hitter Flows in Data Center Networks

Cited by 12 publications

References 63 publications

Software Defined Networks: a Comparative Study and Quality of Services Evaluation

Software Defined Networks: a Comparative Study and Quality of Services Evaluation

Machine Learning Empowered Intelligent Data Center Networking: A Survey

FDPHI: Fast Deep Packet Header Inspection for Data Traffic Classification and Management

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