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Achieving high robustness and performance in QoS-aware route planning for IPTV networks

Admission region of triple-play services in wireless home networks

The joint design of the multi-stream in MIMO relay sigle/multiple user system is investigated. Under the inter-stream interference, a constrained optimization problem with respect to the unknown relay precoder matrix is formulated with a goal of minimizing the total relay transmit power subject to quality of service (QoS) requirement on each data stream. As the original opitimization problem is non-convex, the matrix expression of the cost function and constrained requirement is converting to a scalar optimization problem and the optimal relay precoder and receive filter are obtained. The optimal design of the multiple user relay system under QoS requirement is also provided. By using a reasonable approximation and converting to the generalized Rayleigh form for the cost function, a suboptimal relay precoder matrix for QoS optimization with relay transmit power constraint is obtained. Simulation results demonstrate that for single user model, the minimize relay power will increase with Signal to Interference plus Noise Ratio(SINR) constraint value becoming larger, and for multiple users model, more users in our algorithm will make the MSE gain more obvious as increasing transmitting SNR.

Software-Defined Networking (SDN) is a new efficiently idea of programmable networks that separates the control plane from data plane of all network devices. Internet service provider is responsible for all the control decisions and communication among the forwarding elements from centralized controller. SDN provides the various optimized services. Quality of service (QoS) routing is a path computation method that is suitable for the different traffics generated by several applications, while utilization of network resources has increased. This agreement of service is defined by QoS requirements such as throughput, delay, jitter and packet loss etc. Multimedia applications often require assured from multi QoS constrained, causing the NP-complete problem which cannot be simply solved in polynomial time and high management complexity in the transition network. SDN is able to reduce complexity and it is used to efficiently implement traffic, hence SDN significantly values to development QoS routing. In this paper, we propose QoS routing algorithm called Quantized Level Balance (QLB) for SDN that considers one or many QoS parameters relating to the network application. To satisfy the requirements, QLB selects QoS parameters depending to the level of appropriate application service quality. We have replicated our algorithm on simulate topology with Scalable Video-streaming Evaluation Framework (SVEF). We measure the Peak Signal-to-Noise Ratio (PSNR) and Mean Opinion Score (MOS) of Scalable Video Coding (SVC) at the receiver. Our propose algorithm is improved than single-metric approach that may choose poor QoS parameter paths.

Wireless mobile radio systems have to serve users with different quality of service (QoS) requirements. Scheduling algorithms like weighted proportional fair (WPF) have been proposed considering channel gains to improve the system capacity in terms of sector throughput as well as user priorities to fulfill QoS requirements. These scheduling algorithms allow assigning different priority factors to users and services so that different QoS requirements can be fulfilled. This paper provides an analysis of the influence of users with different priorities on the sector and user throughput. It is shown analytically and by system level simulations that the average user throughput can be adjusted by choosing a specific priority factor. Furthermore, it is shown that the sector throughput decreases if WPF scheduling for users with different priorities and full buffer model are considered compared to a scenario with users having equal priority. If QoS requirements for realistic traffic models, e.g. FTP, have to be fulfilled, for each high priority user low priority users have to be removed from the system in order not to exceed an acceptable number of unsatisfied users. It is shown that the decrease in sector throughput with a realistic traffic model and QoS requirements is higher than for the full buffer model.

End-to-end network slicing in unmanned aerial vehicle (UAV) networks has attracted significant attention for delivering differentiated services tailored to the quality of service (QoS) requirements of user terminals (UTs). In a sliced UAV network, the dual mobility of both UTs and UAVs, combined with the three-layer association of UT-UAV-network slice (NS), makes handoff a critical and challenging problem for maintaining the communication performance of UTs. This paper proposes a sequential handoff process where a central controller makes handoff decisions for each UT sequentially according to the UT queue. This process is modeled as a Markov decision process (MDP). In the MDP, the state space includes NS available resources, UTs' QoS requirements, and UT queue information. Additionally, the reward function accounts for UTs' QoS and handoff overhead. The double deep Q-network (DDQN) technique is then employed to design a handoff algorithm. This algorithm achieves the optimization goal of ensuring UTs' QoS requirements while avoiding frequent handoffs by maximizing the total reward of the MDP. Additionally, a state-dependent exploration technique with adaptive intensity adjustment, called noisy network (NoisyNet), is utilized to drive the DDQN for effective exploration, thereby enhancing the performance of the handoff algorithm. Finally, the runtime complexity of the proposed NoisyNet-DDQN-based sequential handoff (NoisyNet-DDQN-SH) algorithm is analyzed. The simulation results show that the NoisyNet-DDQN-SH algorithm converges well. Moreover, it exhibits strong robustness. Additionally, the NoisyNet technique effectively enhances the algorithm's performance, making the NoisyNet-DDQN-SH algorithm superior to the vanilla DDQN (with an ϵ-greedy strategy), greedy, maximum signal-to-interference-plus-noise ratio (MaxSINR), and random algorithms.

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.

Component-based development (CBD) techniques have been widely used to enhance the productivity and reduce the cost for software systems development. However, applying CBD techniques to embedded software development faces additional challenges. For embedded systems, it is crucial to consider the quality of service (QoS) attributes, such as timeliness, memory limitations, output precision, battery constraints. Frequently, multiple components implementing the same functionality with different QoS properties can be used to compose a system. Also, software components may have parameters that can be configured to satisfy different QoS requirements. Composition analysis, which is used to determine the most suitable component selections and parameter settings to best satisfy the system QoS requirement, is very important in embedded software development process. In this paper, we present a model and the methodologies to facilitate composition analysis. We define QoS requirements as constraints and objectives. Composition analysis is performed based on the QoS properties and requirements to find solutions (component selections and parameter settings) that can optimize the QoS objectives while satisfying the QoS constraints. We use a multiobjective concept to model the composition analysis problem and use an evolutionary algorithm to determine the Pareto-optimal solutions efficiently.

A large number of Internet service providers (ISP) view wireless technology as an attractive option to replace all or part of the hard-wired infrastructure that currently dominates the local loop/last mile. Therefore, these ISP must provide quality of service support to these applications consistent with that offered by the backbone. However, this is a very challenging task in the wireless environment due to the instability of the wireless channel and the diverse quality of service requirements dictated by different multimedia applications. We introduce a resource allocation algorithm for the wireless link that distributes information from the ISP facility to the homes. The algorithm takes into account the wireless channel characteristics, the quality of service required by the applications as well as a pricing value function. Our solution is based on 'time-value' scheduling algorithms originally developed for scheduling real-time processes during transient faults.

The growing emergence of a variety of network applications have stringent Quality of Service (QoS) requirements which cannot be always met by the best-effort Internet. To provide QoS, several QoS frameworks have been explored over last two decades, but none of them has been truly successful and globally implemented. This paper proposes QOF, a novel QoS framework based on the OpenFlow. It uses the superior control capabilities of OpenFlow network to transmit business having QoS requirements. On receiving QoS requirements of an application, QOF employs network status information provided by the measurement system (bandwidth, delay, packet loss), then uses QoS routing algorithm implemented by path computation module to guarantee QoS. Finally, QOF is simulated and verified, simulation results show that QOF can find a path satisfying QoS requirements of application according to network status information, and can improve resource usage.

IEEE 802.16 is a standardisation for a broadband wireless access in metropolitan area networks (MAN). Since the IEEE 802.16 standard defines the concrete quality of service (QoS) requirement, a scheduling scheme is necessary to achieve the QoS requirement. Many scheduling schemes are proposed with the purpose of throughput optimisation and fairness enhancement, however, few scheduling schemes support the delay requirement. In this study, the authors propose a new scheduling scheme reflecting the delay requirement. Specifically, the authors add the delay requirement term in the proportional fair scheduling scheme and the scheduling parameters are optimised with respect to the QoS requirement. Therefore the QoS requirement is achieved without the excessive resource consumption.

A 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) system uses the concept of two-tier heterogeneous networks (HetNets), where low-power and short-range femtocells are laid under macrocells to fulfill the quality of service (QoS) requirements of users and to boost overall network capacity. However, co-channel interference is one of the major issues that need to be resolved for the successful deployment of HetNets. To overcome this problem, fractional frequency reuse (FFR) schemes have been proposed that can efficiently utilize the available spectrum. Nevertheless, these schemes waste limited frequency resources owing to their static allocation and lack of following QoS requirements, network loading conditions, and service priority of users. In this paper, a QoS-based dynamic FFR (QoS-DFFR) scheme is proposed that efficiently allocates the non-occupied center-zone frequency bands, i.e., bonus bandwidth (BBW), to cell-edge users by considering their QoS requirements. Consequently, the proposed QoS-DFFR scheme can optimize cell-edge user throughput and sector throughput and reduce co-channel interference by dynamically allocating the BBW to the most demanding cell-edge users. The proposed QoS-DFFR scheme improves performance because of its ability to dynamically allocate the limited portion of the frequency bands based on the service priorities of users. The system-level simulation results show that the proposed QoS-DFFR scheme performs remarkably well in a HetNet environment. Compared with the usual FFR schemes, the proposed scheme almost doubles the cell-edge user's throughput and reduces the user's packet loss rate.

In the Internet of Things (IoT) paradigm, data is gathered for treatment from locals, machines, vehicles, etc. Cloud computing is providing suitable hardware and software for data processing. Thus, the integration of IoT with cloud capabilities offers several benefits for many applications. However, challenges persist for some use-cases like delay-sensitive services due to the huge amount of information collected by IoT devices and to be processed by cloud servers. Fog computing is expected to overcome several limits and challenges in cloud computing concerning the Quality of Service (QoS) requirements like latency, bandwidth and location awareness. Nevertheless, researchers still have to deal with several issues namely the architectural level and the energetic aspect. In this paper, we investigate fog system architectures and energy consumption in literature, while considering QoS requirements in the synthesis. A system model is then introduced with a potential solution for QoS management for fog computing environment.

QoS routing in GMPLS-capable integrated IP/WDM networks with router cost constraints

Bayesian optimization has been used for the global optimization of communication parameters of Vehicular Ad Hoc Networks (VANETs) for safety applications with stringent quality of service (QoS) requirements. However, the effectiveness of the methodology relies on an accurate analytic model for querying a distribution over functions, which is not practical. Furthermore, incorporating QoS requirements as constraints into the search process is cumbersome, timeconsuming, and even unreliable. In this paper, we present a new approach to the constrained Bayesian optimization of IEEE 802.11 based VANETs for safety messaging with the help of deep learning neural networks (DLNNs). First, we design and train a DLNN using data collected from the analytic models or channel measurements to approximate a mapping from the search parameter space to the QoS metrics. The QoS constraints are naturally incorporated into the DLNN through preprocessing data pairs that cannot meet the QoS requirements. Then, the Bayesian optimization is conducted to find the optimal communication parameters for the best channel usage on the condition that all QoS requirements are met. Accordingly, experiments are carried out on the Google Colab platform where the impact of DLNN structure, data sampling rate, and other optimization parameters are investigated. In comparison to other optimization approaches, utilizing a DLNN in the Bayesian optimization process is more time efficient and flexible.