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

Abstract

Software Defined Networks (SDN) is a new network paradigm that emerged to offer better network management through the separation of network control logic and data forwarding element. This separation speed up network innovation without the need to rely on the vendor-proprietary interface for network element configuration to forward packets. However, SDN is flow driven network, for each arrived flow, a feasible path is computed to forward the flow to its destination. Afterward, the SDN control logic process the corresponding routing and instruct the set of data forwarding elements to install them on their Flowtable to guide the routing process. Unfortunately, the network changes more frequently in dynamic large-scale networks and the Flowtable is a constraint with limited space. These challenges require the SDN controller to compute paths more often which may also need a large number of flow routing rules placement. In addition, the frequency of communication link failures increases lately. The successful deployment of SDN heavily depends on how it satisfies the reliability requirement with uninterrupted services. Several studies were conducted to compute the optimal path for data forward to meet their Quality of Service demand. Other studies focus on reducing the frequency of link failure. Some studies were conducted to manage the constraint Flowtable resources. This survey focuses on Routing rules placement, unoptimized routing, link, and switch load balancing, failure detection, and recovery. The paper extensively discusses each issue and analyzes the weakness of the current solutions. Finally, it highlights potential challenges that need future research attention.

Distributed Controller Placement in Software-Defined Networks with Consistency and Interoperability Problems

“…For that, it monitors the topology at regular intervals to collect network statistics for the operation of network applications at AP. Some of the activities it monitors are trafc arrival patterns, trafc types, or topology changes due to events such as failures, which have increased lately [27]. Tis enables it to recalculate new instructions and install them on the switch's fow table at DP upon the occurrence of any of these events.…”

FMDADM: A Multi-Layer DDoS Attack Detection and Mitigation Framework Using Machine Learning for Stateful SDN-Based IoT Networks

“…Software-defined networking (SDN) and machine learning (ML) technologies have attracted the interest of researchers to address different IoT security concerns [8]. On the one hand, a new technique known as "Stateful SDN" has expanded the basic functions of OpenFlow, the most widely used protocol for communication between data and control planes [9], by adding the ability to apply multiple matchaction rules depending on the distinct states detected in the switch's SDN flow tables [10], [11], [12]. This feature gives the switch the ability to respond to events at the packet-level.…”