Route Path Selection Optimization Scheme Based Link Quality Estimation and Critical Switch Awareness for Software Defined Networks

Abstract

Software-defined network (SDN) is a new paradigm that decouples the control plane and data plane. This offered a more flexible way to efficiently manage the network. However, the increasing number of traffics due to the proliferation of the Internet of Things (IoT) devices also increase the number of flow arrival which in turn causes flow rules to change more often, and similarly, path setup requests increased. These events required route path computation activities to take place immediately to cope with the new network changes. Searching for an optimal route might be costly in terms of the time required to calculate a new path and update the corresponding switches. However, the current path selection schemes considered only single routing metrics either link or switch operation. Incorporating link quality and switch’s role during path selection decisions have not been considered. This paper proposed Route Path Selection Optimization (RPSO) with multi-constraint. RPSO introduced joint parameters based on link and switches such as Link Latency (LL), Link Delivery Ratio (LDR), and Critical Switch Frequency Score (CWFscore). These metrics encourage path selection with better link quality and a minimal number of critical switches. The experimental results show that the proposed scheme reduced path stretch by 37%, path setup latency by 73% thereby improving throughput by 55.73%, and packet delivery ratio by 12.5% compared to the baseline work.

Dynamic Routing and Failure Recovery Approaches for Efficient Resource Utilization in OpenFlow-SDN: A Survey

“…Other solutions [61]compute paths based on link load to avoid congestion. A critical switch and link routing scheme was presented in [62], [63] to compute the path based on link and switch features. This way, flows are routed through a path with the minimum number of critical switches and links to improve the packet delivery ratio and throughput.…”

Review of Path Selection Algorithms with Link Quality and Critical Switch Aware for Heterogeneous Traffic in SDN

“…The proliferation of the Internet of Things (IoT) applications has significantly increased the number and heterogeneity of traffic in modern networks [1]. A prior study reveals that active internet devices will rise from 26.66 billion in 2019 to 41 billion in 2027 [3].…”

“…The controller extracts the network statistics from the DP in real-time to feed various network applications. Examples of these applications are routing [1], security [17], congestion [18], prioritized services [19], QoS [20], load balancing [21], energy [22], and many others. The applications run their algorithms to implement new rules simultaneously throughout the network seamlessly.…”

“…The frequency of rules update is 1.5 to 5s in the restoration approach [1]. The time spent to complete one cycle of update operation consists of (1) the rule com-putation delay, (2) the transmission and propagation delay in distributing the rule to all the switches, and (3) the delay in installing the rule.…”

Comparative Analysis of Routing Schemes Based on Machine Learning

“…Although there have been some routing and traffic engineering methods to try to solve the network optimization problem in various data center scenarios, in the data center network scenario, the existing routing and traffic scheduling optimization methods are still difficult to meet the requirements of efficient utilization of links and loads [2]. In order to meet complex network application scenarios and diverse service quality requirements, many network layer optimization schemes based on mathematical models have been proposed [3][4][5][6].…”