A cognitive QoS management framework for WLANs

Due to the precipitous growth of wireless networks and the paucity of spectrum, more interference is imposed to the wireless terminals which constraints their performance. In order to preserve such performance degradation, this paper proposes a framework which uses cognitive radio techniques for quality of service (QoS) management of wireless local area networks (LANs). The framework incorporates radio environment maps as input to a cognitive decision engine that steers the network to optimize its QoS parameters such as throughput. A novel experimentally verified heuristic physical model is developed to predict and optimize the throughput of wireless terminals. The framework was applied to realistic stationary and time-variant interference scenarios where an average throughput gain of 344% was achieved in the stationary interference scenario and 70% to 183% was gained in the time-variant interference scenario

Implementing quality of service for the software defined networking enabled future internet

Achieving ever-growing Quality of Service (QoS) requirements for business customers is a major concern over the current Internet. However, presently, its architecture and infrastructures are inflexible to meet the demand of increased QoS requirements. OpenFlow, OF-Config (OpenFlow Configuration and Management protocol), and OVSDB (Open vSwitch Database Management protocol) protocols are well-known software defined networking (SDN) technologies for the Future Internet, enabling flexibility by decoupling the control plane from networking devices. In this paper, we propose a QoS framework using the SDN technologies and test the framework in failure-conditions using single and multiple autonomous system scenarios of the current Internet. We show that an effectively high QoS can be achieved for business customers using our framework

A framework for QoS driven user-side cloud service management

This thesis presents a comprehensive framework that assists the cloud service user in making cloud service management decisions, such as service selection and migration. The proposed framework utilizes the QoS history of the available services for QoS forecasting and multi-criteria decision making. It then integrates all the inherent necessary processes, such as QoS monitoring, forecasting, service comparison and ranking to recommend the best and optimal decision to the user

A New QoS Renegotiation Mechanism for Multimedia Applications.

While there are a lot of advances in the area of QoS deployment and management over IP networks, there is still a need for a robust QoS renegotiation framework for multimedia applications. A QoS renegotiation framework has three concurrent modules which should be integrated with the least amount of overhead. These muddles include a feedback mechanism, a load control mechanism, and a service-response mechanism. This thesis proposes a new feedback mechanism which is based on call rejection notification for QoS renegotiation. The difference between the proposed mechanism and previous approaches is that it uses flow information (not packet information) as a feedback mechanism. The new feedback mechanism provides a better QoS, improves the system performance, and maximizes the service revenue

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