QoS-based routing over software defined networks

Quality of Service (QoS) relies on the shaping of preferential delivery services for applications in favour of ensuring sufficient bandwidth, controlling latency and reducing packet loss. QoS can be achieved by prioritizing important broadband data traffic over the less important one. Thus, depending on the users’ needs, video, voice or data traffic take different priority based on the prevalent importance within a particular context. This prioritization might require changes in the configuration of each network entity which can be difficult in traditional network architecture. To this extent, this paper investigates the use of a QoS-based routing scheme over a Software-Defined Network (SDN). A real SDN test-bed is constructed using Raspberry Pi computers as virtual SDN switches managed by a centralized controller. It is shown that a QoS-based routing approach over SDN generates enormous control possibilities and enables automation

QoS-based routing over software defined networks

Quality of Service (QoS) relies on the shaping of preferential delivery services for applications in favour of ensuring sufficient bandwidth, controlling latency and reducing packet loss. QoS can be achieved by prioritizing important broadband data traffic over the less important one. Thus, depending on the users’ needs, video, voice or data traffic take different priority based on the prevalent importance within a particular context. This prioritization might require changes in the configuration of each network entity which can be difficult in traditional network architecture. To this extent, this paper investigates the use of a QoS-based routing scheme over a Software-Defined Network (SDN). A real SDN test-bed is constructed using Raspberry Pi computers as virtual SDN switches managed by a centralized controller. It is shown that a QoS-based routing approach over SDN generates enormous control possibilities and enables automation

AROMA: An adapt-or-reroute strategy for multimedia applications over SDN-based wireless environments

To support new and advanced multimedia-rich applications and services while providing satisfactory user experience, the underlying network infrastructure needs to evolve and adapt. One of the key enabling technologies of the next generation (5G) networks is the integration of Software Defined Networking (SDN) within a heterogeneous wireless environment to enable interoperability and QoS provisioning. Leveraging on the features of the SDN paradigm, it is possible to introduce new solutions to handle the increasing mobile video transmission challenges with strict QoS requirements, such as: low delay, jitter, packet loss, and high bandwidth demands. However, degradation and instability perceived from video traffic makes it difficult to satisfy various end-users. In this context, this paper proposes AROMA, an Adapt-or-reROute strategy for Multimedia Applications over SDN-based wireless environments. AROMA enables QoS provisioning over multimedia-oriented SDN-based WLAN environments. The proposed solution is evaluated using a real experimental test-bed setup

LearnQoS: a learning approach for optimizing QoS over multimedia-based SDNs

As video-based services become an integral part of the end-users’ lives, there is an imminent need for increase in the backhaul capacity and resource management efficiency to enable a highly enhanced multimedia experience to the endusers. The next-generation networking paradigm offers wide advantages over the traditional networks through simplifying the management layer, especially with the adoption of Software Defined Networks (SDN). However, enabling Quality of Service (QoS) provisioning still remains a challenge that needs to be optimized especially for multimedia-based applications. In this paper, we propose LearnQoS, an intelligent QoS management framework for multimedia-based SDNs. LearnQoS employs a policy-based network management (PBNM) to ensure the compliance of QoS requirements and optimizes the operation of PBNM through Reinforcement Learning (RL). The proposed LearnQoS framework is implemented and evaluated under an experimental setup environment and compared with the default SDN operation in terms of PSNR, MOS, throughput and packet loss

LearnQoS: a learning approach for optimizing QoS over multimedia-based SDNs

As video-based services become an integral part of the end-users’ lives, there is an imminent need for increase in the backhaul capacity and resource management efficiency to enable a highly enhanced multimedia experience to the endusers. The next-generation networking paradigm offers wide advantages over the traditional networks through simplifying the management layer, especially with the adoption of Software Defined Networks (SDN). However, enabling Quality of Service (QoS) provisioning still remains a challenge that needs to be optimized especially for multimedia-based applications. In this paper, we propose LearnQoS, an intelligent QoS management framework for multimedia-based SDNs. LearnQoS employs a policy-based network management (PBNM) to ensure the compliance of QoS requirements and optimizes the operation of PBNM through Reinforcement Learning (RL). The proposed LearnQoS framework is implemented and evaluated under an experimental setup environment and compared with the default SDN operation in terms of PSNR, MOS, throughput and packet loss

Policy-based QoS management framework for software-defined networks

With the emerging trends of virtualization of cloud computing and big data applications, network management has become a challenging problem for optimizing the network state while satisfying the applications’ Quality of Service (QoS) requirements. This paper proposes a policy-based management framework over Software-Defined Networks (SDN) for QoS provisioning. The proposed approach monitors the QoS parameters of the active flows and dynamically enforces new decisions on the underlying SDN switches to adapt the network state to the current demanded high-level policies. Moreover, the proposed solution makes use of Neural Networks to identify the violating flows causing the network congestion. Upon detection of a policy violation two route management techniques are implemented, such as: rerouting and rate limiting. The proposed framework was implemented and evaluated within an experimental test bed setup. The results indicate that the proposed PBNM-based SDN framework enables QoS provisioning and outperforms the default SDN in terms of throughput, packet loss rate and latency

Policy-based QoS management framework for software-defined networks

With the emerging trends of virtualization of cloud computing and big data applications, network management has become a challenging problem for optimizing the network state while satisfying the applications’ Quality of Service (QoS) requirements. This paper proposes a policy-based management framework over Software-Defined Networks (SDN) for QoS provisioning. The proposed approach monitors the QoS parameters of the active flows and dynamically enforces new decisions on the underlying SDN switches to adapt the network state to the current demanded high-level policies. Moreover, the proposed solution makes use of Neural Networks to identify the violating flows causing the network congestion. Upon detection of a policy violation two route management techniques are implemented, such as: rerouting and rate limiting. The proposed framework was implemented and evaluated within an experimental test bed setup. The results indicate that the proposed PBNM-based SDN framework enables QoS provisioning and outperforms the default SDN in terms of throughput, packet loss rate and latency

AROMA: An adapt-or-reroute strategy for multimedia applications over SDN-based wireless environments

To support new and advanced multimedia-rich applications and services while providing satisfactory user experience, the underlying network infrastructure needs to evolve and adapt. One of the key enabling technologies of the next generation (5G) networks is the integration of Software Defined Networking (SDN) within a heterogeneous wireless environment to enable interoperability and QoS provisioning. Leveraging on the features of the SDN paradigm, it is possible to introduce new solutions to handle the increasing mobile video transmission challenges with strict QoS requirements, such as: low delay, jitter, packet loss, and high bandwidth demands. However, degradation and instability perceived from video traffic makes it difficult to satisfy various end-users. In this context, this paper proposes AROMA, an Adapt-or-reROute strategy for Multimedia Applications over SDN-based wireless environments. AROMA enables QoS provisioning over multimedia-oriented SDN-based WLAN environments. The proposed solution is evaluated using a real experimental test-bed setup

Compression-based technique for SDN using sparse-representation dictionary

As Software-Defined Networks (SDN) emerged, the control and forwarding planes were abstracted using the standardized OpenFlow protocol which led to the increasing demand for optimal usage of the control link between the two planes especially for network monitoring. This paper proposes a data collection scheme based on a compression technique for SDNbased networks. It employs sparsity approximation algorithms for compressing the aggregated data in the SDN switch, while the recovery of the sparse data is taking place at the controller. The approach aims at further decreasing the link usage for Quality of Service (QoS) applications while increasing the network observability. The proposed solution extends the functionality of the SDN switch by integrating dictionary learning algorithms like K-SVD and Orthogonal Matching Pursuit (OMP) methods for the purpose of sparsity approximation. Experimental setup and the QoS link utilization metric for link monitoring were used for performance evaluation. The proposed solution was analysed over a range of sparsity levels, showing the data recovery accuracy of the controller under different compression ratios and using real internet traces. The results show that the proposed method reduces the control link overhead cost with up to 98% when compared to the case of periodic acquisition network monitoring of the SDN network