An Adaptive Standard Meta-data Aware Proxy Cache

Due to the increasing availability and use of digital video data on the Web, video caching will be an important performance factor in the future WWW. We propose an architecture of a video proxy cache that integrates modern multimedia and communication standards. Especially we describe features of the MPEG-4 and MPEG-7 multimedia standards that can be helpful for a video proxy cache. QBIX supports real-time adaptation in the compressed and in the decompressed domain. It uses adaptation to improve the cache replacement strategies in the proxy, but also to realize media gateway functionality driven by the clients' terminal capabilities.

Little is known about effective treatment for personality-disordered (PD) offenders. We aimed to obtain the perspective of service users and staff on: (a) the experience of receiving treatment; and (b) the experience of delivering treatment, within new forensic services for PD offenders. Thematic analysis was applied to qualitative interviews with 30 service users and 22 staff. Service users perceived that they were making positive changes in the areas of anger management, communication, self-harm, self-esteem and insight into their difficulties. Undertaking the clinical work was extremely stressful for staff. Forensic PD services may be having an important impact on the quality of service users' lives. Whether treatment is successful in reducing long-term risk to others remains to be seen, and the cost-effectiveness of these services needs to be examined.

The purpose of this paper is to study quality of service (QoS) management and performance monitoring issues in B-ISDN. A QoS framework is proposed in order to satisfy the user's service quality requirements. The basic idea of this QoS framework is that the user's QoS requirement should be represented in terms of objectives set to QoS parameters which are in turn translated into asynchronous transfer mode (ATM) network performance (NP) parameters objectives; then, meeting NP parameters objectives ensures that the user's QoS requirements are fulfilled. Performance monitoring mechanisms based on operation and maintenance (OAM) cells flows in the ATM layer and estimation methods, which are both part of a QoS framework, are reviewed. We focus in particular on the definition and estimation of NP parameters in the case of non-conforming cells transmitted on an ATM connection. Finally, a QoS management scenario for the ATM layer is proposed. >

Spectrum handoff in cognitive radio networks is to have a seamless switch between channels consequently, a high quality of service for the secondary users. This paper proposes three techniques for spectrum handoff. The first proposed one based on the low priority user leaves the channel to another channel. The second proposed model based on the low priority user stays on the same channel with resuming its transmission when the high priority user finished its transmission. Finally, the third proposed model based on the low priority user stays on the same channel with restarting its transmission when the high priority user finished its transmission. The obtained results indicate that the three proposed system models give different queuing delay performances for each user. The techniques of the models and the obtained results are important for evaluating the total system time, service time and queuing delay for secondary users. Consequently, the quality of service for the secondary users and hence the useful for cognitive radio network design and optimization will be obtained.

The data center Energy Consumption (EC) and the user Quality of Service (QoS) are two important influencing factors, which the cloud service provider must balance in the process of providing virtual cloud services resources (VCSR). For the VCSR over-deployment phenomenon happening in cloud computing Infrastructure as a Service (IAAS), a user-oriented deployment constraint of VCSR is proposed, and the definition of user's Service Level Agreements (SLA) and the energy consumption of data center are also suggested. On the basis of that, the model of dynamic migration of the virtual nodes is given, solved by simulated annealing algorithm. In the experiments, different strategies are compared using CloudSim. The results show that the suggested strategy has advantages on balancing the energy consumption of data center and user's QoS.

Cognitive radio (CR) systems have been proposed for efficient usage of spare spectrum licensed to primary systems. This leads to the issue of providing as much spectrum to CR users as possible while not degrading the quality of service (QoS) of primary users of the spectrum. This paper proposes a novel cooperation scheme between primary and CR users to guarantee QoS of primary users up to the cell edge while making the licensed spectrum available for opportunistic access by the CR users. Thus the proposed method can provide a so called ¿win-win strategy¿ by benefiting both primary and CR users. The proposed scheme solves efficiently the Gaussian multiple access problem for power allocation of primary systems that cooperates with CR systems. We show that the Gaussian multiple access problem can be formulated as a standard semi-definite programming (SDP) problem. Numerical results show that the QoS of primary users at the cell edge is improved by the proposed cooperative access scheme.

The global optimal Web service selection based on quality of service (QoS) in cloud environment has become a research focus when there are lots of the same or similar services. In this environment, it is possible that many service users request the same or similar services at the same time, which will result in users' unsatisfied requirement and services' load imbalance. The existing service selection approaches usually suppose that service's load capacity is infinite and user always select the service with the best expected QoS despite the amount of user requests. Therefore, it is very important problem how to get a tradeoff between the users' satisfied requirement and service load balance. To solve this problem, this paper presents a global optimal service selection approach based on QoS and load-aware in cloud environment. In this approach, we first build a user QoS utility model which describes the relationship between user's request and services' QoS, and design a service's load capacity model to achieve the load capacity of a service. Then, we use 0-1 integer programming to build a global optimal model based on QoS utility of users and services' load capacity, and provide the optimal service selection algorithm for users. Finally, by conducting large-scale experiments based on a Web service dataset, we show that our approach can effectively help users to select high qualified services while keeping load balance of services in cloud environment.

Mobile cloud computing is a new paradigm to improve the quality of mobile services, which has drawn considerable attentions in both industrial and academic fields. In this paper, we consider the resource management and sharing problems for radio and computing resources to support mobile applications in mobile cloud computing. In such an environment, service providers can cooperate to form coalition to share their idle resources with each other. We propose a coalition game model based on two-sided matching theory. The coalition game model efficiently reflects the scalable cooperation among the service providers for sharing their idle resources. As a result, the resources can be better utilized and the quality of service for users can be improved. The simulation results indicate that our scheme can optimize the resource utilization and significantly improve the quality of service of the users.

1),A construction service company is a company engaged in the development sector in Indonesia. Construction contractor service business in the form of planning, design, cost calculation, bidding and construction implementation until completion. After all the work is completed, it will be handed over to the project owner or owner. The field of work for construction service companies is in the form of building construction, bridge construction, factory construction, industrial development and road construction. The aim of this study was to test and analyze the evaluation of the satisfaction and quality of service users at the construction company PT. Isa Putra Construction. The analytical method used in this research is a quantitative descriptive research type using Statistical Product and Service Solution (SPSS). The research was conducted at companies engaged in construction services, namely PT. Isa Putra Construction with a sample of 17 respondents. Based on the results of the calculations that have been done, the CSI value is 47%. This CSI value is obtained from the division between the total Weight Score (WS) value and the maximum scale used in this study, namely 5 and multiplying by 100%.Keywords: Satisfaction, Quality, Construction Services, Service User Satisfaction Construction

Nowadays, IoT systems can better satisfy the service requirements of users with effectively utilizing edge computing resources. Designing an appropriate pricing scheme is critical for users to obtain the optimal computing resources at a reasonable price and for service providers to maximize profits. This problem is complicated with incomplete information. The state-of-the-art solutions focus on the pricing game between a single service provider and users, which ignoring the competition among multiple edge service providers. To address this challenge, we design an edge-intelligent hierarchical dynamic pricing mechanism based on cloud-edge-client collaboration. We introduce an improved double-layer Stackelberg game model to describe the cloud-edge-client collaboration. Technically, we propose a novel pricing prediction algorithm based on double-label Radius K-nearest Neighbors, thereby reducing the number of invalid games to accelerate the game convergence. The experimental results show that our proposed mechanism effectively improves the quality of service for users and realizes the maximum benefit equilibrium for service providers, compared with the traditional pricing scheme. Our proposed mechanism is highly suitable for the IoT applications (e.g., intelligent agriculture or Internet of Vehicles), where there are multiple competing edge service providers for resource allocation.

An improved cloud resource revenue optimization models are proposed, which include the benefits of service providers and users negotiate SLA agreement. And the models define the constraint mechanism for describing service quality problems such as service response time and customer satisfaction. We design the comprehensive response indicator and use this indicator to quantify the rationality of the distribution of resources, which are the equilibrium between maximal revenue of cloud service providers and the quality of service for users. The parallel PSO-ACO algorithm based on MapReduce for distributed systems is presented to solve the above model, which represents the massive resource allocation optimization problem. The experiments are implemented to analyze the algorithm performance and task allocation strategy. And the experiments results show that the models and algorithms are robust and efficient.

In Grid computing environment, quality of service (QoS) provisioning must be provided to the end users on the basis of their specific requirements. This paper proposes in a first step QoS attributes for Grid applications. In this matter, a mix of quantitative and qualitative parameters have to be considered. In the context, the analytical hierarchy process (AHP) technique [1] is a possible approach to formulate the QoS requirements of the users for Grid services. In order to apply QoS preference to actual application, we introduce a QoS function and a metric for user's satisfaction degrees. These both tools can be used as an evaluation criterion by the user. Subsequently, an algorithm of service selection considering the user's QoS preference is presented. Our empirical studies indicate that the application can reliably select the optimal service for users.

Hadoop capacity scheduler algorithms can not meet the different requirements of different users on service quality of cloud computing. Therefore, this paper proposed the SLA-Capacity scheduler algorithm which on the basis of the existing capacity scheduler algorithm, introduce the concept of SLA, design weighted scheduler algorithm based Qos (Quality of Service) constraints. Due to each user's jobs with different QoS preference, According to the part of QoS parameters to calculate the weight value which be used to decide the job scheduler order, so as to ensure that the user's service quality, improve user satisfaction. Experiments on Hadoop show that improved algorithm can effectively reduce the waiting time for user operation, improve the overall operating efficiency.

Cloud Service Providers (CSPs) offer a wide variety of scalable, flexible, and cost-efficient services to cloud users on demand and pay-per-utilization basis. However, vast diversity in available cloud service providers leads to numerous challenges for users to determine and select the best suitable service. Also, sometimes users need to hire the required services from multiple CSPs which introduce difficulties in managing interfaces, accounts, security, supports, and Service Level Agreements (SLAs). To circumvent such problems having a Cloud Service Broker (CSB) be aware of service offerings and users Quality of Service (QoS) requirements will benefit both the CSPs as well as users. In this work, we proposed a Fuzzy Rough Set based Cloud Service Brokerage Architecture, which is responsible for ranking and selecting services based on users QoS requirements, and finally monitor the service execution. We have used the fuzzy rough set technique for dimension reduction. Used weighted Euclidean distance to rank the CSPs. To prioritize user QoS request, we intended to use user assign weights, also incorporated system assigned weights to give the relative importance to QoS attributes. We compared the proposed ranking technique with an existing method based on the system response time. The case study experiment results show that the proposed approach is scalable, resilience, and produce better results with less searching time.

Scalable video coding is a promising solution for efficient video content distribution to users having heterogeneous network and terminal capabilities. Thanks to its inherent multidimensional adaptability, a scalable bit stream can be used to simultaneously transmit multiple video sequences having different bit rates to corresponding target users, without necessity of re-encoding or transcoding. In order to exploit such an advantage effectively, it is crucial to understand the effects of multidimensional scalability options on the perceived quality and the trade-off between the scalability dimensions through subjective quality assessment. This paper reviews existing studies aiming at achieving this goal in order to summarize their results and common findings.