QoS Experience Research Articles

Realistic Vertical Handoff Predictive Trigger Thresholding in Heterogeneous Networks

The contemporary wireless networks exhibit heterogeneity, i.e., different types of wireless networks co-exist and fulfill the user's needs. Users amongst the available networks prefer the Wi-Fi and 4G networks. Wi-Fi is a choice for homes and offices for limited coverage and high data rate. While for outdoor, 4G is a better choice due to broader coverage and reasonably better average data rates. As user mobility is high nowadays, smooth session transfer is essential amongst such heterogeneous networks to provide seamless connectivity with the best QoS. Currently, video real-time application traffic is in high demand. This paper investigates the heterogeneous /vertical handover performance for realistic traffic and terminal speeds based on the MIH framework. The MIH is IEEE 802.21 standard to maintain the service continuity amongst heterogeneous networks. The primary focus of this paper is to derive the thresholds for speeds and the link layer predictive triggers. These thresholds are helpful in the design of the algorithms to enhance the QoS and user experience. Further, to understand the limitations of the said standard for a given scenario. The packet loss ratio and handover latency are the QoS parameters. The detailed simulations are carried out in NS-2 by considering a realistic scenario of terminal speeds and application traffic (MPEG-4, H.261, and HDTV). The networks chosen for the analysis are Wi-Fi and 4G. The packet loss ratio and handover latency are considered QoS parameters. For the accuracy of results, simulation time is varied as per the user's speed. We derived predictive trigger and speed thresholds critically examined for the given application traffic.

Multiple context based service scheduling for balancing cost and benefits of mobile users and cloud datacenter supplier in mobile cloud

This paper makes the use of cloud architecture as computational power for mobile device application. The multiple context based service scheduling scheme for balancing the cost and the benefits of mobile users and cloud datacenter supplier in mobile cloud is proposed, which can adapt to the system context without compromising mobile device user's QoS. The multiple context based mobile cloud service scheduling is formulated as a utility optimization problem, which is expressed as the summation of utilities of mobile cloud users. The proposed model aims to satisfy huge number of mobile requests, improve mobile user's QoS experiences and reduce system overheads. The proposed problem consists of cloud resources allocation optimization subproblem and mobile user QoS optimization subproblem. The multiple context based service scheduling algorithm is proposed to efficiently and adaptively schedule resource according to the context information. The proposed algorithm is validated through a series of experiments. The proposed algorithm can provide better QoS satisfaction for mobile device user, improve cloud resource utilization and reduce the cost.

QoS-Aware Forward Error Correction Cooperating with Opportunistic Routing in Wireless Multi-hop Networks

QoS provisioning in wireless multi-hop networks is always a hot researching issue, and QoS is mainly influenced by symbol error rate and transmission delay. Conventional adaptive forward error correction and opportunistic routing cannot perceive the wireless networks' condition well for improving the QoS performance, because the transmission delay and reliability are contradictory for each other, and both of adaptive forward correction and opportunistic routing only focus on optimizing one side. In this paper, we propose QoS-aware forward error correction cooperating with opportunistic routing (QFEC-OR), which balances the reliability and effectiveness to improve QoS performance. Then we adopt a better QoS performance perception parameter--20---80 perception parameter to make QFEC-OR superior for QoS experience. We compare QFEC-OR with two kinds of encoding schemes combined with OR to illustrate QFEC-OR's efficiency, and compare QFEC-OR employing 20---80 perception parameter with two kinds of perception parameters employed in QFEC-OR to illustrate the 20---80 perception parameter makes QFEC-OR provide better QoS performance. Simulation results demonstrate the higher effectiveness of QFEC-OR with 20---80 perception parameter for QoS provisioning in wireless multi-hop networks.

Multimedia services quality prediction based on the association mining between context and QoS properties

With the increasing presence and adoptionof multimedia services over the Internet, quality has become an essential factor in choosing a service from a set of functionally similar services. To address the time-consuming, inaccurate and expensive multimedia quality evaluations in traditional methods, this paper proposes a context-aware matrix factorization (CAMF) approach to collaborative and personalized quality prediction for multimedia services. We first mine the association rules between context and QoS properties oriented to multimedia services to determine how a QoS value to be predicted is dependent on context properties. Then the service QoS experiences collected from other users are filtered out according to the context which is similar to current user. Based on the collected QoS data, a context-aware approach is designed for personalized service QoS value prediction. Experimental results demonstrate that this approach can make significant improvement on the accuracy of QoS prediction.

Multi-user web service selection based on multi-QoS prediction

In order to find best services to meet multi-user's QoS requirements, some multi-user Web service selection schemes were proposed. However, the unavoidable challenges in these schemes are the efficiency and effect. Most existing schemes are proposed for the single request condition without considering the overload of Web services, which cannot be directly used in this problem. Furthermore, existing methods assumed the QoS information for users are all known and accurate, and in real case, there are always many missing QoS values in history records, which increase the difficulty of the selection. In this paper, we propose a new framework for multi-user Web service selection problem. This framework first predicts the missing multi-QoS values according to the historical QoS experience from users, and then selects the global optimal solution for multi-user by our fast match approach. Comprehensive empirical studies demonstrate the utility of the proposed method.

Opportunistic spectrum scheduling for multiuser cognitive radio: a queueing analysis

We develop a queueing analytic framework to study the data link layer quality-of-service performance measures for cognitive radio users in an infrastructure-based dynamic spectrum access environment. In order to allocate the available spectrum white spaces among the cognitive radio users in a spectrum overlay scenario, an opportunistic scheduling scheme is considered. The queueing model considers bursty traffic arrival pattern at the cognitive radio user ends, finite buffer size, activity of primary users (i.e., dynamic channel availability), and correlated channel fading. We present a step-by-step procedure to derive the delay distribution, average throughput, and packet loss rate for the cognitive radio users. The proposed framework facilitates cross-layer design for improved QoS experience in cognitive radio networks. Usefulness of the developed analytical model is demonstrated through example applications.