Dynamic resource management in SDN-based virtualized networks

Network virtualization allows for an abstraction between user and physical resources by letting a given physical infrastructure to be shared by multiple service providers. However, network virtualization presents some challenges, such as, efficient resource management, fast provisioning and scalability. By separating a network's control logic from the underlying routers and switches, software defined networking (SDN) promises an unprecedented simplification in network programmability, management and innovation by service providers, and hence, its control model presents itself as a candidate solution to the challenges in network virtualization. In this paper, we use the SDN control plane to efficiently manage resources in virtualized networks by dynamically adjusting the virtual network (VN) to substrate network (SN) mappings based on network status. We extend an SDN controller to monitor the resource utilisation of VNs, as well as the average loading of SN links and switches, and use this information to proactively add or remove flow rules from the switches. Simulations show that, compared with three state-of-art approaches, our proposal improves the VN acceptance ratio by about 40% and reduces VN resource costs by over 10%

Fast network configuration in Software Defined Networking

Software Defined Networking (SDN) provides a framework to dynamically adjust and re-program the data plane with the use of flow rules. The realization of highly adaptive SDNs with the ability to respond to changing demands or recover after a network failure in a short period of time, hinges on efficient updates of flow rules. We model the time to deploy a set of flow rules by the update time at the bottleneck switch, and formulate the problem of selecting paths to minimize the deployment time under feasibility constraints as a mixed integer linear program (MILP). To reduce the computation time of determining flow rules, we propose efficient heuristics designed to approximate the minimum-deployment-time solution by relaxing the MILP or selecting the paths sequentially. Through extensive simulations we show that our algorithms outperform current, shortest path based solutions by reducing the total network configuration time up to 55% while having similar packet loss, in the considered scenarios. We also demonstrate that in a networked environment with a certain fraction of failed links, our algorithms are able to reduce the average time to reestablish disrupted flows by 40%

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

Software Defined Networking (SDN) introduced network management flexibility that eludes traditional network architecture. Nevertheless, the pervasive demand for various cloud computing services with different levels of Quality of Service requirements in our contemporary world made network service provisioning challenging. One of these challenges is path selection (PS) for routing heterogeneous traffic with end-to-end quality of service support specific to each traffic class. The challenge had gotten the research community\u27s attention to the extent that many PSAs were proposed. However, a gap still exists that calls for further study. This paper reviews the existing PSA and the Baseline Shortest Path Algorithms (BSPA) upon which many relevant PSA(s) are built to help identify these gaps. The paper categorizes the PSAs into four, based on their path selection criteria, (1) PSAs that use static or dynamic link quality to guide PSD, (2) PSAs that consider the criticality of switch in terms of an update operation, FlowTable limitation or port capacity to guide PSD, (3) PSAs that consider flow variabilities to guide PSD and (4) The PSAs that use ML optimization in their PSD. We then reviewed and compared the techniques\u27 design in each category against the identified SDN PSA design objectives, solution approach, BSPA, and validation approaches. Finally, the paper recommends directions for further research

Efficient binary cutting packet classification

Packet classification is the process of distributing packets into ‘flows’ in an internet router. Router processes all packets which belong to predefined rule sets in similar manner& classify them to decide upon what all services packet should receive. It plays an important role in both edge and core routers to provideadvanced network service such as quality of service, firewalls and intrusion detection. These services require the ability to categorize & isolate packet traffic in different flows for proper processing. Packet classification remains a classical problem, even though lots of researcher working on the problem. Existing algorithms such asHyperCuts,boundary cutting and HiCuts have achieved an efficient performance by representing rules in geometrical method in a classifier and searching for a geometric subspace to which each inputpacket belongs. Some fixed interval-based cutting not relating to the actual space that eachrule covers is ineffective and results in a huge storage requirement. However, the memoryconsumption of these algorithms remains quite high when high throughput is required.Hence in this paper we are proposing a new efficient splitting criterion which is memory andtime efficient as compared to other mentioned techniques. Our proposed approach known as (ABC) Adaptive Binary Cuttingproducesa set of different-sized cuts at each decision step, with the goal to balance the distribution offilters and to reduce the filter duplication effect. The proposed algorithmuses stronger andmore straightforward criteria for decision treeconstruction. Experimental results will showthe effectiveness of proposed algorithm as compared to existing algorithm using differentparameters such as time & memory. In this paper, no symmetrical size cut at each decision node, with aim to make a distribution of filters balanced and also to reduce redundancy in filter