Software defined networking (SDN) is an emerging network paradigm that decouples the control plane from the data plane. The data plane is composed of forwarding elements called switches and the control plane is composed of controllers. SDN is gaining popularity from industry and academics due to its advantages such as centralized, flexible, and programmable network management. The increasing number of traffics due to the proliferation of the Internet of Thing (IoT) devices may result in two problems: (1) increased processing load of the controller, and (2) insufficient space in the switches’ flow table to accommodate the flow entries. These problems may cause undesired network behavior and unstable network performance, especially in large-scale networks. Many solutions have been proposed to improve the management of the flow table, reducing controller processing load, and mitigating security threats and vulnerabilities on the controllers and switches. This paper provides comprehensive surveys of existing schemes to ensure SDN meets the quality of service (QoS) demands of various applications and cloud services. Finally, potential future research directions are identified and discussed such as management of flow table using machine learning.

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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.

Software-defined networking (SDN) enables flexible fine-grained networking policies by allowing the SDN controller to install packet handling rules on distributed switches. The behaviour of SDN depends on the set of forwarding entries installed at the switch flow table. The increasing number of traffics from the proliferation of the Internet of Thing (IoT) devices increase the processing load on the controller and generates an additional number of entries stored in the flow table. However, the switch flow table memory (TCAM) cannot accommodate many entries. Packets from multimedia flows are usually large in size and thus suffer processing delay and require more flow set up requests. The SDN controller may be overloaded and face some scalability problems because it supports a limited number of requests from switches. OpenFlow uses timeout configuration to manage flow setup request. The conventional fixed timeout cannot cope up with the dynamic nature of traffic flows. This paper controls the frequent flow setup requests by proposing an adaptive and hybrid idle–hard timeout allocation (AH-IHTA). The algorithm considers traffic patterns, flow table usage ratio, and returns appropriate the timeout to different flows. The performance evaluations conducted have shown a 28% and 39% reduction in the flow setup request and flow eviction, respectively.

The increase in size and complexity of the Internet has led to the introduction of Software Defined Networking (SDN). SDN is a new networking paradigm that breaks the limitations of traditional IP networks and upgrades the current network infrastructures. However, like traditional IP networks, network failures may also occur in SDN. Multiple research studies have discussed this problem by using a variety of techniques. Among them is the use of the community detection method is one of the failure recovery technique for SDN. However, this technique have not considered the specific problem of multiple link multi-community failure and inter-community link failure scenarios. This paper presents a community detection-based routing algorithm (CDRA) for link failure recovery in SDN. The proposed CDRA scheme is efficient to deal with single link intra-community failure scenarios and multiple link multi-community failure scenarios and is also able to handle the inter-community link failure scenarios in SDN. Extensive simulations are performed to evaluate the performance of the proposed CDRA scheme. The simulation results depicts that the proposed CDRA scheme have better simulations results and reduce average round trip time by 35.73%, avg data packet loss by 1.26% and average end to end delay 49.3% than the Dijkstra based general recovery algorithm and also can be used on a large scale network platform.

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