Generalized Performance Management of Multi-Class Real-Time Imprecise Data Services

Wireless link quality is closely related to the received signal strength. Hence, transmit power control can be used to adjust the communication link quality to avoid asymmetric or weak links. This paper proposes a spatial radio resource model for quality of service (QoS) reliability in General Packet Radio Service (GPRS) networks. To model the propagation parameters, we adopt the empirical modeling approach by measuring signal outages at various base stations from a well established network, operating in Nigeria. We collect these data over a period of three months and apply the resultant data as parameters to the proposed model. We simulate the model under ideal conditions and evaluate the system’s performance as well as the power control tradeoff. A qualitative analysis using processed GIS maps, calls for effective power control in the system. Simulation results indicate that the existing system still requires further improvements to enhance its QoS reliability.

In WiMAX architecture to avoid congestion at the base station, a mechanism namely WiMAX Fair Intelligent Congestion Control (WFICC) was proposed in [11]. WFICC ensures that the traffic is scheduled in such a way that the base station output buffer operates around a target operating point, without violating the Quality of Service (QoS) requirements of connections. However, only preliminary results were presented in [11]. The aim of this paper is to investigate WFICC thoroughly and evaluate its performance in terms of throughput, delay and jitter for various Class of Services (CoSs) under various parameter settings of the algorithm. A detailed and comprehensive simulation study on various settings of parameters is performed in ns-2. The results show that WFICC performs extremely well in allocating resources fairly among Class of Services (CoSs), yet preserving their QoS requirements. Furthermore, WFICC is robust and easily adapted to various traffic conditions.

The demand for real-time e-commerce data services has been increasing recently. In many e-commerce applications, it is essential to process user requests within their deadlines, i.e., before the market status changes, using fresh data reflecting the current market status. However, current data services are poor at processing user requests in a timely manner using fresh data. To address this problem, we present a differentiated real-time data service framework for e-commerce applications. User requests are classified into several service classes according to their importance, and they receive differentiated real-time performance guarantees in terms of deadline miss ratio. At the same time, a certain data freshness is guaranteed for all transactions that commit within their deadlines. A feedback-based approach is applied to differentiate the deadline miss ratio among service classes. Admission control and adaptable update schemes are applied to manage potential overload. A simulation study, which reflects the e-commerce data semantics, shows that our approach can achieve a significant performance improvement compared to baseline approaches. Our approach can support the specified per-class deadline miss ratios maintaining the required data freshness even in the presence of unpredictable workloads and data access patterns, whereas baseline approaches fail.

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.

QNoC: QoS architecture and design process for network on chip

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.

Implementing the dynamic couple of searching paths and resource allocation becomes increasingly important in the research of Quality of Service (QoS) routing. In this paper, we formulate the problem of dynamic QoS routing with resource allocation based on Worst-case Fair Weighted Fair Queuing (WF2Q) packet scheduling style. We propose an algorithm using the proved results -- the optimal path resource allocation algorithm (OPRA2), which can allow intelligent choice of the maximized reliability and minimized delay path according to the types of applications and adapt QoS parameters and allocated resources related to WF2Q in process of routing. It can find an optimal path which satisfies different QoS requirements and compute the amount of bandwidth and buffer resources needed at each node. We provide the simulation results to illustrate the effect of buffers and bandwidths according to the values of control parameters.

In cloud computing, there exist several classes of web services, and each class of web service contains multiple services which have different Quality of Service (QoS) values. To provide QoS for users, determining the best service composition that meet user's preferences is necessity. Finding the best service composition needs a certain method in order to provide the best combination of QoS with minimum response time, while in some cases, the process is bounded by budget constraint provided in user's preferences. In a simple method, for selecting the best service composition, we need to evaluate nm combinations of n classes of service and m services member. This condition leads to a large computing costs and memory consumption that occur during the process of generating the combination of data. To reduce the number of combinations, we propose an approach for finding the best service composition of web services using skyline algorithm which can adapt different user preferences of QoS. While selecting the best composition, we also take consideration in budget as constraint by selecting the closest user's budget with the best skyline result of services. SalSa skyline algorithm have given the new alternative ways to finding the best candidates of services in each class of service in reasonable time. These happened because we only need to evaluate the smaller parts of members in each class which will need much time if the number of services is plenty. Moreover, we also presented a method to find the best composition of services which able to present the composition that meet the user's budget.

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.

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.

There has been active research on quality of service (QoS) multicasting. Very few, however, addresses QoS inter-domain multicasting. We present a QoS extension to BGMP (border gateway multicast protocol) which, proposed by Kumar, et al., is currently one of the most promising inter-domain multicasting proposals. With limited modification to the current BGMP, we extend it to support the so called two-bit differentiated service (including the classes of premium, assured, and best-effort services) proposed by Nichols, Jacobson, and Zhang. Detailed mechanisms of the QoS extension are described. Performance is evaluated through extensive simulation of real-network scenarios, supporting CBR, VBR, and best-effort traffic flows with the three classes of services. We believe that the work presented here is significant to the advancement of the next-generation Internet supporting QoS communications.