QoS Adaptation in Multimedia Multicast Conference Applications for E-Learning Services

The Quality of Service (QoS) support for multimedia multi-user sessions is essential to the success of fourth generation mobile networks. However, it requires the QoS control for those sessions independently of the movement of users, QoS model, and link capacity supported by wired and wireless networks in the end-to-end session path. This paper presents the Multi-user Session Control (MUSC) mechanism to provide QoS mapping and QoS adaptation of multi-user sessions over heterogeneous and mobile networks. The coordination of the QoS mapping and QoS adaptation controllers allows the adaptation of the session to the current network conditions and the dynamic selection of the most suitable network service class to map the session. The MUSC control is based on the session requirements, existing services class and their available bandwidth. Performance evaluation shows the impact of the MUSC proposal by analysing its convergence time, as well as the one-way delay and throughput of multi-user sessions in a QoS-aware mobile scenario.

Quality of Service (QoS) has been widely used to support dynamic Web Service (WS) selection and composition. Due to the volatile nature of QoS parameters, QoS prediction has been put forward to understand the trend of QoS data volatility and estimate QoS values in dynamic environments. In order to provide adaptive and effective QoS prediction, we propose a WS QoS prediction approach, named WS-QoSP, based on the technique of forecast combination. Different from the existing QoS prediction approaches that choose a most feasible forecasting model and predict relying on this "best" model, WS-QoSP selects multiple potential forecasting models, combines the results of the selected models to optimize the overall forecast accuracy. Results of real data experiments demonstrate the diversified forecast accuracy gains by using WS-QoSP.

Large scale distributed real time and embedded (DRE) applications are complex entities that are often composed of different subsystems and have stringent Quality of Service (QoS) requirements. These subsystems are often developed separately by different developers increasingly using commercial off-the shelf (COTS) middleware. Subsequently, these subsystems need to be integrated, configured to communicate with each other, and distributed. However, there is currently no standard way of supporting these requirements in existing COTS middleware. While recently emerging component-based middleware provides standardized support for packaging, assembling, and deploying, there is no standard way to provision QoS required by the DRE applications. We have previously introduced a QoS encapsulation model, qoskets, as part of our QuO middleware framework that can dynamically adapt to resource constraints. In this paper we introduce implementing these QoS behaviors as components that can be assembled with other application components. The task of ensuring QoS then becomes an assembly issue. To do so we have componentized our QuO technology instead of integrating QuO into the middleware as a service. To date, we have demonstrated our approach of QoS provisioning in MICO, CIAO, and Boeing’s Prism component middleware. We present experimental results to evaluate the overhead incurred by these QoS provisioning components in the context of CIAO CCM. We use a simulated Unmanned Aerial Vehicle (UAV) application as an illustrative DRE application for the demonstration of QoS adaptations using qosket components.

Web quality of service (QoS) control can effectively prevent a Web Server from becoming overloaded. This paper presents an adaptive controller for Web QoS, which dynamically adjusts parameters of the proportional-integral (PI) controller according to the changes of the Web server model. Different from the existing methods based on the off-line system identification, the adaptive Web QoS controller is implemented based on the online system identification through a quantum-behaved particle swarm optimization (QPSO) algorithm and a mutated version of QPSO (MuQPSO). The proposed approach for online system identification by QPSO and MuQPSO shows better performance than genetic algorithms and the particle swarm optimization algorithm on the simulation tests. Then, the performance of the adaptive controller for Web QoS is tested in three experiments and compared with that of fixed PI controller. Experimental results show that the adaptive control employing online system identification by QPSO and MuQPSO algorithms is able to control the Web server resource more effectively in case of overload and, thus, improves the Web QoS.

Adaptive call admission control for QoS provisioning in multimedia wireless networks

The development of distributed real-time and embedded (DRE) systems is often challenging due to conflicting quality-of-service (QoS) constraints that must be explored as trade-offs among a series of alternative design decisions. The ability to model a set of possible design alternatives—and to analyze and simulate the execution of the representative model—helps derive the correct set of QoS parameters needed to satisfy DRE system requirements. QoS adaptation is accomplished via rules that specify how to modify application or middleware behavior in response to changes in resource availability. This chapter presents a model-driven approach for generating QoS adaptation rules in DRE systems. This approach creates high-level graphical models representing QoS adaptation policies. The models are constructed using a domain-specific modeling language—the adaptive quality modeling language (AQML)—which assists in separating common concerns of a DRE system via different modeling views. The chapter motivates the need for model transformations to address crosscutting and scalability concerns within models. In addition, a case study is presented based on bandwidth adaptation in video streaming of unmanned aerial vehicles.

The end-to-end quality of service (QoS) control is one of the key factors to the success of next generation networks. Therefore, this paper presents a session-aware QoS mapping and adaptation mechanism that maps the session QoS requirements into the most suitable network service class across heterogeneous mobile networks as well as it adapts the session to the current network conditions. The proposed scheme allows mobile operators to keep network internals sufficiently opaque and to reduce operational costs by mapping and/or adapting sessions to groups of heterogeneous mobile users. Concurrently, the proposed scheme assures acceptable quality levels to on-going sessions, independently of the movement of users. In addition, an experimental evaluation in a QoS mobile multicast environment is presented

The role of Internet of Things (IoT) in facilitating connectivity of the wireless sensor network (WSN) to the remote server or cloud over the Internet encourages the formation of a highly complex system with significant potential. This enable the interaction between the physical and virtual sensor clouds resulting in the ability to allow for flexibility within the physical system to adapt with the service requirements. The study of quality of service (QoS) in the domain of WSN-IoT brings forward potentials and challenges, especially due to cost and scalability limitations in physical testbeds and low credibility of simulation results. In this paper, system architecture of a testbed with Adaptive Quality of Service (AQoS) is proposed. AQoS testbed concept provides an avenue for a flexible experimentation system which is capable of reacting to dynamic changes of network conditions. By employing network simulator tools to derive the network performance information based on historical data of a physical testbed, realistic prediction of network performance can be made, and adjustment to the physical network can be proposed. This paper reports the preliminary experiences of implementing an organization for encouraging AQoS. Early stages of testing the organization for absorbing the system behavior and reacting with necessary actuation to the WSN reconfiguration has shown encouraging results.

This paper presents results of an experiment in which plural video and voice streams transmitted over a network are output synchronously in networked virtual environments. We propose an adaptive QoS (quality of service) control scheme for video and voice traffic. According to the importance of each media object, CPU load and network load, the scheme adjusts the rendition rate of a 3D virtual space and the video resolution as well as the accuracy of media synchronization; also it can display a CG object instead of a less-important video object. In addition, the paper quantitatively examines the effect of the adaptive QoS control on the performance.

The present work proposes an adaptive solution to provide quality of service in Voice over IP communications. This solution is based on three components that interact inorder to achieve higher quality in voice communication. The first two consist in changing the codec and the transport protocol in real-time during a conversation; the third consists in using a forward error correction mechanism to recover from loss packets. To demonstrate the voice quality obtained by this solution, a VoIP client application was developed, compatible with other VoIP clients, to implement the proposed quality of service algorithm and control the voice quality during a conversation. The results of the experimental measurements and simulations performed demonstrate that this solution is viable and significantly increases the voice quality of VoIP communication.

Cloud Computing has become the most popular distributed computing environment because it does not require any user level management and controlling on the low-level implementation of the system. However, efficient resource provisioning is a key challenge for cloud computing and resolving such kind of problem can reduce under or over utilization of resources, increase user satisfaction by serving more users during peak hours, reduce implementation cost for providers and service cost for users. Existing works on cloud computing focuses to accurate estimation of the capacity needs, static or dynamic VM (Virtual Machine) creation and scheduling. But significant amount of time is required to create and destroy VMs which could be used to serve more user requests. In this paper, an adaptive QoS (Quality of Service) aware VM provisioning mechanism is developed that ensures efficient utilization of the system resources. The VM for similar type of requests has been recycled so that the VM creation time can be minimized and used to serve more user requests. In the proposed model, QoS is ensured by serving all the tasks within the requirements described in SLA. Tasks are separated using multilevel queue and the most urgent task is given high priority. The simulation-based experimental results shows that a great number of tasks can be served compared to others which will help to satisfy customers during the peak hour.

유.무선 인터넷 및 하드웨어의 발전으로 인터넷 방송, VoD(Video On Demand)등의 응용 서비스가 증가하면서 QoS(Quality of Service) 보장 문제가 해결해야 될 중요한 이슈로 부각되고 있다. 현재 인터넷은 모든 패킷을 우선순위에 상관없이 FIFO 형태로 전달하는 최선형(Best Effort) 서비스 기법에 근간하고 있기 때문에 보다 안정적인 서비스 지원을 위한 새로운 QoS 프로토콜 개발, QoS 지원 라우터 개발 등의 연구가 진행 중이다. 하지만 이러한 새로운 QoS 기법들은 기존 인프라를 충분히 활용하지 못하고 새로운 하드웨어 및 소프트웨어를 사용해야 하기때문에 고비용, 비효율성의 문제를 수반한다. 이러한 점에 착안하여, 본 논문에서는 기존의 인프라를 최대한 이용하는 클라이언트/서버 기반의 네트워크 적응형 QoS 제어기법을 제안한다. 제안된 Qos 제어기법은 스트리밍 시스템에서 실시간으로 서비스되고 있는 네트워크 대역폭 상태를 파악하여 QoS를 지원하기 때문에 유ㆍ무선 네트워크에 상관없이 적용할 수 있을 뿐만 아니라, 기존의 스트리밍 서비스에서 사용되는 프로토콜들을 이용하기 때문에 저 비용으로 구현할 수 있다. 또한, 스트리밍 서버와 클라이언트에 새로운 모듈 추가만으로 QoS를 지원하기 때문에 라우터나 네트워크 회선의 교체 없이도 QoS를 보장할 수 있는 장점을 지니고 있다. 인터넷의 트래픽이 폭주하는 경우에서 모의실험을 수행한 결과 일반 인터넷 환경에서는 클라이언트의 버퍼 메모리가 비어 스트리밍 재생 시 끊김 현상이 자주 발생하였으나, 제안한 기법을 사용한 인터넷 환경에서는 동영상의 끊김 현상이 없이(seamless) 재생될 수 있는 스트림이 클라이언트 버퍼 메모리에 항상 있음을 확인할 수 있었다. Due to the fast development of wire&wireless internet and computer hardware, more and more internet services are being developed, such as Internet broadcast, VoD (Video On Demand), etc. So QoS (Qualify of Service) is essentially needed to guarantee the quality of these services. Traditional Internet is Best-Effort service in which all packets are transported in FIFO (First In First Out) style. However, FIFO is not suitable to guarantee the quality of some services, so more research in QoS router and QoS protocol are needed. Researched QoS router and protocol are high cost and inefficient because the existing infra is not used. To solve this problem, a new QoS control method, named Network Adaptive QoS, is introduced and applied to client/server-based streaming systems. Based on network bandwidth monitoring mechanism, network adaptive QoS control method can be used in wire&wireless networks to support QoS in real-time streaming system. In order to reduce application cost, the existing streaming service is used in NAQoS. A new module is integrated into the existing server and client. So the router and network line are not changed. By simulation in heavy traffic network conditions, we proved that stream cannot be seamless without network adaptive QoS method.

The scarcity and large fluctuations of link bandwidth in wireless networks have motivated the development of adaptive multimedia services in mobile communication networks, where it is possible to increase or decrease the bandwidth of individual ongoing flows. This paper studies the issues of quality of service (QoS) provisioning in such systems. In particular, call admission control and bandwidth adaptation are formulated as a constrained Markov decision problem. The rapid growth in the number of states and the difficulty in estimating state transition probabilities in practical systems make it very difficult to employ classical methods to find the optimal policy. We present a novel approach that uses a form of reinforcement learning known as Q-learning to solve QoS provisioning for wireless adaptive multimedia. Q-learning does not require the explicit state transition model to solve the Markov decision problem; therefore more general and realistic assumptions can be applied to the underlying system model for this approach than in previous schemes. Moreover, the proposed scheme can efficiently handle the large state space and action set of the wireless adaptive multimedia QoS provisioning problem. Handoff dropping probability and average allocated bandwidth are considered as QoS constraints in our model and can be guaranteed simultaneously. Simulation results demonstrate the effectiveness of the proposed scheme in adaptive multimedia mobile communication networks.

New and more advanced applications are supporting end-to-end quality-of-service (QoS) guarantees through the configuration and management of distributed resources. As an effect of the sharing of static resources across multiple concerns, coupled with the use of dynamically changing resources such as mobile communications, the general availability of resources in a distributed environment is variable and potentially unpredictable. In this paper, we discuss the requirements for QoS adaptation mechanisms and QoS-based distributed resource management, together with our approaches to QoS monitoring and adaptation, in the context of our recently developed Distributed Resource Management Architecture (DRMA).

A generic architecture for management and control of end-to-end quality of service over multiple domains