QOGMP: QoS-oriented global multi-path traffic scheduling algorithm in software defined network

Guo, Yiping; Hu, Guyu; Shao, Dongsheng

doi:10.1038/s41598-022-18919-w

Cited by 8 publications

(3 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Dynamic routing of flows is achievable due to the decoupling of control and forwarding functions in a KDN architecture. QOGMP is a KDN control architecture that has a distributed control architecture, where there is a broker on the top level for traffic scheduling regarding flow coordination over multiple domains based on QoS requirements, which can be used to disseminate knowledge to end devices [227]. Some have attempted to give a high preference to high-priority traffic by allocating more resources while at the same time considering the end-to-end latency requirements by using weighting parameters, which is a joint resource allocation scheme for multiple traffic classes [228].…”

Section: Review On Existing Work On Flow Scheduling and Prioritizatio...mentioning

confidence: 99%

A Comprehensive Survey on Knowledge-Defined Networking

Wijesekara

Gunawardena

2023

Telecom

View full text Add to dashboard Cite

Traditional networking is hardware-based, having the control plane coupled with the data plane. Software-Defined Networking (SDN), which has a logically centralized control plane, has been introduced to increase the programmability and flexibility of networks. Knowledge-Defined Networking (KDN) is an advanced version of SDN that takes one step forward by decoupling the management plane from control logic and introducing a new plane, called a knowledge plane, decoupled from control logic for generating knowledge based on data collected from the network. KDN is the next-generation architecture for self-learning, self-organizing, and self-evolving networks with high automation and intelligence. Even though KDN was introduced about two decades ago, it had not gained much attention among researchers until recently. The reasons for delayed recognition could be due to the technology gap and difficulty in direct transformation from traditional networks to KDN. Communication networks around the globe have already begun to transform from SDNs into KDNs. Machine learning models are typically used to generate knowledge using the data collected from network devices and sensors, where the generated knowledge may be further composed to create knowledge ontologies that can be used in generating rules, where rules and/or knowledge can be provided to the control, management, and application planes for use in decision-making processes, for network monitoring and configuration, and for dynamic adjustment of network policies, respectively. Among the numerous advantages that KDN brings compared to SDN, enhanced automation and intelligence, higher flexibility, and improved security stand tall. However, KDN also has a set of challenges, such as reliance on large quantities of high-quality data, difficulty in integration with legacy networks, the high cost of upgrading to KDN, etc. In this survey, we first present an overview of the KDN architecture and then discuss each plane of the KDN in detail, such as sub-planes and interfaces, functions of each plane, existing standards and protocols, different models of the planes, etc., with respect to examples from the existing literature. Existing works are qualitatively reviewed and assessed by grouping them into categories and assessing the individual performance of the literature where possible. We further compare and contrast traditional networks and SDN against KDN. Finally, we discuss the benefits, challenges, design guidelines, and ongoing research of KDNs. Design guidelines and recommendations are provided so that identified challenges can be mitigated. Therefore, this survey is a comprehensive review of architecture, operation, applications, and existing works of knowledge-defined networks.

show abstract

Section: Review On Existing Work On Flow Scheduling and Prioritizatio...mentioning

confidence: 99%

A Comprehensive Survey on Knowledge-Defined Networking

Wijesekara

Gunawardena

2023

Telecom

View full text Add to dashboard Cite

show abstract

“…Guo et al [17] designed a quality-of-service oriented SDN global multipath traffic scheduling algorithm (QOGMP). The forwarding rules of flows are specified by packet loss, delay, and link capacity, and deep reinforcement learning is used to compute the forwarding paths.…”

Section: Traffic Scheduling Considering Quailty Of Servicementioning

confidence: 99%

Data Center Traffic Scheduling Strategy for Minimization Congestion and Quality of Service Guaranteeing

Wang¹,

Cao²,

Hu³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

According to Cisco's Internet Report 2020 white paper, there will be 29.3 billion connected devices worldwide by 2023, up from 18.4 billion in 2018. 5G connections will generate nearly three times more traffic than 4G connections. While bringing a boom to the network, it also presents unprecedented challenges in terms of flow forwarding decisions. The path assignment mechanism used in traditional traffic scheduling methods tends to cause local network congestion caused by the concentration of elephant flows, resulting in unbalanced network load and degraded quality of service. Using the centralized control of software-defined networks, this study proposes a data center traffic scheduling strategy for minimization congestion and quality of service guaranteeing (MCQG). The ideal transmission path is selected for data flows while considering the network congestion rate and quality of service. Different traffic scheduling strategies are used according to the characteristics of different service types in data centers. Reroute scheduling for elephant flows that tend to cause local congestion. The path evaluation function is formed by the maximum link utilization on the path, the number of elephant flows and the time delay, and the fast merit-seeking capability of the sparrow search algorithm is used to find the path with the lowest actual link overhead as the rerouting path for the elephant flows. It is used to reduce the possibility of local network congestion occurrence. Equal cost multi-path (ECMP) protocols with faster response time are used to schedule mouse flows with shorter duration. Used to guarantee the quality of service of the network. To achieve isolated transmission of various types of data streams. The experimental results show that the proposed strategy has higher throughput, better network load balancing, and better robustness compared to ECMP under different traffic models. In addition, because it can fully utilize the resources in the network, MCQG also outperforms another traffic scheduling strategy that does rerouting for elephant flows (namely Hedera). Compared with ECMP and Hedera, MCQG improves average throughput by 11.73% and 4.29%, and normalized total throughput by 6.74% and 2.64%, respectively; MCQG improves link utilization by 23.25% and 15.07%; in addition, the average round-trip delay and packet loss rate fluctuate significantly less than the two compared strategies.

show abstract

“…This algorithm aims to minimize the routing computation in the SDN controller and decrease the flow table rules in the SDN forwarding elements, solving the overflow problem in SDN switches. Guo et al 33 attempted to solve the problem of high computation time in determining the best route of an SDN flow by proposing a lightweight routing algorithm that depends on deep reinforcement learning and traction link. This approach guarantees QoS and resolves the issues of excessive delay.…”

Section: Related Workmentioning

confidence: 99%

Design of an optimized traffic‐aware routing algorithm using integer linear programming for software‐defined networking

Eissa

Azim

Ata

2023

Int J Communication

View full text Add to dashboard Cite

Summary The number of internet users and connected devices has dramatically expanded due to the recent technological boom and the benefits that the internet of things offers to ease our lives. Network scheduling, quality of service, resource allocation, and security issues are now being addressed via software‐defined networking (SDN). SDN has several benefits over traditional networks, including global centralized control, managing network traffic, and separating the forwarding and control plane. The work done in this paper aims to design and implement a traffic‐aware routing framework based on routing optimization presented as an integer linear programming (ILP) to improve heterogeneous traffic flows' quality of service (QoS) in a simulated SDN environment. With the knowledge that the routing problem is a nondeterministic polynomial‐time‐hard problem, the proposed scheme aims to decrease the computational routing time to make the ILP‐based routing system more suitable for real‐time processing. The simulation results illustrate that the proposed framework reduces the computational time by 23% and 49% for Abilene and Goodnet topology, respectively. Additionally, with 1000 flows in the network, the suggested scheme reduces the number of network flows that violate the QoS by 9% and 22% (with Abilene topology) and 16% and 51% (with Goodnet topology) as compared to the existing shortest path delay and sway methods, respectively.

show abstract

QOGMP: QoS-oriented global multi-path traffic scheduling algorithm in software defined network

Cited by 8 publications

References 28 publications

A Comprehensive Survey on Knowledge-Defined Networking

A Comprehensive Survey on Knowledge-Defined Networking

Data Center Traffic Scheduling Strategy for Minimization Congestion and Quality of Service Guaranteeing

Design of an optimized traffic‐aware routing algorithm using integer linear programming for software‐defined networking

Contact Info

Product

Resources

About