Efficient bandwidth allocation in SDN-based peer-to-peer data streaming using machine learning algorithm

Aldabbas, Hamza

doi:10.1007/s11227-022-04929-y

Cited by 6 publications

(3 citation statements)

References 34 publications

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“…Post-classification, the network engages in nuanced traffic engineering procedures. This phase is critical as packets are judiciously directed to the appropriate controllers; each specifically assigned to manage certain traffic types [48]. Consequent to this stratified processing, data transmission to the intended destination addresses ensues, adhering strictly to a hierarchy of priority and queuing policies pre-configured within the controllers and network switches [49].…”

Section: Methodsmentioning

confidence: 99%

Load Balancing in DCN Servers Through Software Defined Network Machine Learning

Beissenova,

Zhidebayeva,

Kopzhassarova

et al. 2024

IJACSA

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In this research paper, we delve into the innovative realm of optimizing load balancing in Data Center Networks (DCNs) by leveraging the capabilities of Software-Defined Networking (SDN) and machine learning algorithms. Traditional DCN architectures face significant challenges in handling unpredictable traffic patterns, leading to bottlenecks, network congestion, and suboptimal utilization of resources. Our study proposes a novel framework that integrates the flexibility and programmability of SDN with the predictive and analytical prowess of machine learning. We employed a multi-layered methodology, initially constructing a virtualized environment to simulate real-world DCN traffic scenarios, followed by the implementation of SDN controllers to instill adaptiveness and programmability. Subsequently, we integrated machine learning models, training them on a substantial dataset encompassing diverse traffic patterns and network conditions. The crux of our approach was the application of these trained models to anticipate network congestion and dynamically adjust traffic flows, ensuring efficient load distribution among servers. A comparative analysis was conducted against prevailing load balancing methods, revealing our model's superiority in terms of latency reduction, enhanced throughput, and improved resource allocation. Furthermore, our research illuminates the potential for machine learning's self-learning mechanism to foresee and adapt to future network states or exigencies, marking a significant advancement from reactive to proactive network management. This convergence of SDN and machine learning, as demonstrated, ushers in a new era of intelligent, scalable, and highly reliable DCNs, demanding further exploration and investment for future-ready data centers.

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Section: Methodsmentioning

confidence: 99%

Load Balancing in DCN Servers Through Software Defined Network Machine Learning

Beissenova,

Zhidebayeva,

Kopzhassarova

et al. 2024

IJACSA

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“…RSCR, leveraging the global view of SDN, can dynamically adjust routes based on changing traffic patterns. Furthermore, unlike traditional routing that relies on protocols such as RIP, OSPF, and BGP [ 35 ], RSCR, as described in paper [ 36 , 37 , 38 ], integrates with SDN using new routing protocols. In SD-CFN, timely and reliable communication is also crucial.…”

Section: Rscrmentioning

confidence: 99%

Research on Computing Resource Measurement and Routing Methods in Software Defined Computing First Network

Gong,

Ren,

Wang

et al. 2024

Sensors

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Computing resource measurement and computing routing are essential technologies in the computing first network (CFN), serving as its foundational elements. This paper introduces a Software Defined Computing First Network (SD-CFN) architecture. Building upon this framework, a Dynamic-Static Integrated Computing Resource Measurement Mechanism (DCRMM) is proposed, incorporating methods such as the entropy weight method and K-Means clustering. The DCRMM algorithm outperforms the Maximum-closest Static Algorithm (MSA) and Maximum Closest Dynamic Algorithm (MDA) in terms of node stability, node utilization, and node matching accuracy. Additionally, a Reinforcement Learning and Software Defined Computing First Networking Routing (RSCR) algorithm is presented as a software-defined computing routing solution within the SD-CFN. RSCR introduces a knowledge plane responsible for computing routing calculations. It comprehensively considers factors such as link latency, available bandwidth, and packet loss rate. Simulation experiments conducted on the GÉANT topology demonstrate that RSCR outperforms the OSPF algorithm in terms of link latency, packet loss rate, and throughput. DCRMM and RSCR offer innovative solutions for computing resource measurement and computing routing in computing first networks.

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“…However, this approach was limited to addressing only a subset of shorter MPTCP subflows. Hamza (2022) formulated the guaranteed conditions to cover better transmission quality and reliability, and the autonomous transmission path management and dispatching mechanism were introduced. Amaldi et al (2016) introduced machine learning models incorporating parameters such as the MPTCP subflow transmission rate, network swallowing volume, and network latency.…”

Section: Introductionmentioning

confidence: 99%

Research on the control strategies of data flow transmission paths for MPTCP-based communication networks

Shu,

Du,

Zhu

et al. 2023

PeerJ Computer Science

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The performance of multipath transmission control protocol (MPTCP) subflow through the enhancement mechanism of the MPTCP communication is improved. When dealing with multiple MPTCP subflows occupying the same transmission path, critical issues such as selection and optimization of multipath, and efficient scheduling of available multiple tracks are effectively addressed by incorporating the technology called software defined network (SDN) that is constructed based on four key parameters, namely, network transmission bandwidth, transmission paths, path capacity, and network latency. Besides, critical equipment such as the network physical device layer and SDN controller are integrated with the four parameters. So, the network model defines the transmission control process and data information. Considering the predetermined total network bandwidth capacity to select multiple paths, the adequate bandwidth capacity is determined by defining the data transfer rate between MPTCP terminals and MPTCP servers. However, the processing latency of the OpenFlow switch and the SDN controller is excluded. The effective network transmission paths are calculated through two rounds of path selection algorithms. Moreover, according to the demand capacity of the data transmission and the supply capacity of the required occupied network resource, a supply and demand strategy is formulated by considering the bandwidth capacity of the total network and invalid network latency factors. Then, the available network transmission path from the valid network transmission path is calculated. The shortest path calculation problem, which is the calculation and sorting of the shortest path, is transformed into a clustering, Inter-Cluster Average Classification (ICA), problem. The instruction of the OpenFlow communication flow is designed to schedule MPTCP subflows. Thus, various validation objectives, including the network model, effective network latency, effective transmission paths, supply-demand strategies, ineffective transmission paths, shortest feasible paths, and communication rules are addressed by the proposed method whose reliability, stability, and data transmission performance are validated through comparative analysis with other conventional algorithms. Found that the network latency is around 20 s, the network transmission rate is approximately 10 Mbps, the network bandwidth capacity reaches around 25Mbps, the network resource utilization rate is about 75%, and the network swallowing volume is approximately 3 M/s.

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Efficient bandwidth allocation in SDN-based peer-to-peer data streaming using machine learning algorithm

Cited by 6 publications

References 34 publications

Load Balancing in DCN Servers Through Software Defined Network Machine Learning

Load Balancing in DCN Servers Through Software Defined Network Machine Learning

Research on Computing Resource Measurement and Routing Methods in Software Defined Computing First Network

Research on the control strategies of data flow transmission paths for MPTCP-based communication networks

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