Quality Of Service Values Research Articles

一种高效的服务组合优化算法

With the rapid increasing number of the independently developed web services that provide the same functionality but differ in quality of service (QoS), how to select corresponding component services for each task of a specific service composition process in the way that maximizes the aggregation QoS value, while satisfying the constraints set by the end-users is practice known as an NP hard problem. Current solutions are not suitable for most real-time decision-making service composition applications which are required to obtain an optimal result in reasonable time. In this paper, we present an approach which addresses the issue of this problem by constructing convex hulls. The proposed heuristic algorithm consists of two steps: first, convex hulls are constructed for reducing the search space, then the multi-objective optimization problem is solved based on M-HEU. The results of the experiments demonstrate that our approach can obtain a close-to-optimal solution in a reasonable amount of time.

Scatter/Gather browsing of web service QoS data

Many of the current Quality of Service (QoS) based web service selection systems assume that users will formulate QoS queries accurately. However, this may not always be the case. It would be helpful if users could browse through the QoS data of the service repository before they start the searching process. In this way, they could know what the actual QoS value distributions are, so that they could put down reasonable numbers in their QoS queries. The browsing process performed on QoS queries, on the other hand, could help service providers understand the actual QoS demands from users. In this paper, we propose to use the Scatter/Gather model—a well-known browsing model for large document collections, to implement QoS browsing. We modified the original model to make it fit QoS data and implemented a few variations of the model. We tested and compared performance on both synthetic and real QoS datasets, mainly focusing on the ability to handle real time interaction with users while balancing efficiency and accuracy.

Modeling and analysis of the effects of QoS and reliability on pricing, profitability, and risk management in multiperiod grid-computing networks

In this paper we develop a network equilibrium model for optimal pricing and resource allocation in Computational Grid Network. We consider a general network economy model with Grid Resource Providers, Grid Resource Brokers and Grid Users. The proposed framework allows for the modeling and theoretical analysis of Computational Grid Markets that considers a non-cooperative behavior of decision-makers in the same tier of the grid computing network (such as, for example, Grid Resource Providers) as well as cooperative behavior between tiers (between Resource Providers and Grid Brokers). We introduce risk management into the decision making process by analyzing the decision-marker's reliability and quality of service (QoS) requirement. We analyze resource allocation patterns as well as equilibrium price based on demand, supply, and cost structure of the grid computing market network. We specifically answer the following questions with several numerical examples: How do system reliability levels affect the QoS levels of the service providers and brokers under competition? How do system reliability levels affect the profits of resource providers and brokers in a competitive market? How do system reliability levels influence the pricing of the services in a competitive environment? How do users' service request types, QoS requirements, and timing concerns affect users' behaviors, costs and risks in equilibrium? How does the market mechanism allocate resources to satisfy the demands of users? We find that for users who request same services certain timing flexibility can not only reduce the costs but also lower the risks. The results indicated that the value of QoS can be efficiently priced based on the heterogeneous service demands.

Trustworthy assurance of service interoperation in cloud environment

Cloud computing can be realized by service interoperation and its essence is to provide cloud services through network. The development of effective methods to assure the trustworthiness of service interoperation in cloud environment is a very important problem. The essence of cloud security is trust and trust management. Combining quality of service (QoS) with trust model, this paper constructs a QoS-aware and quantitative trust-model that consists of initial trust value, direct trust value, and recommendatory trust value of service, making the provision, discovery, and aggregation of cloud services trustworthy. Hence, it can assure trustworthiness of service interoperation between users and services or among services in cloud environment. At the same time, based on this model, service discovery method based on QoS-aware and quantitative trust-model (TQoS-WSD) is proposed, which makes a solid trust relationship among service requestor, service provider and service recommender, and users can find trustworthy service whose total evaluation value is higher. Compared to QoS-based service discovery (QoS-WSD) method, it is proved by the experiment for TQoS-WSD method that more accurate result of service discovery will be achieved by service requestor, while reasonable time cost is increased. Meanwhile, TQoS-WSD method strongly resists the effect of service discovery by untrustworthy QoS values and improves service invocation success-rate and thus assures trustworthiness of services interoperation.

A bargaining-driven global QoS adjustment approach for optimizing service composition execution path

In service-based workflow area, service composition is emerging as a promising technology providing more flexible integration means across a variety of distributed heterogeneous applications. A critical research issue in service composition is how to achieve an optimized overall end-to-end quality of service (QoS) composition’s requirements by effectively coordinating QoS constraints of individual service. However, few of existing representative works have well considered economic strategies closely related to competitive market scenarios to address overall QoS issues in service composition. In this paper, we propose a novel global QoS adjustment approach by employing a recursive bargaining strategy to gradually remove QoS constraint violations for optimizing service composition execution path. Our approach mainly exploits the hidden marketplace competitive techniques from economics for developing a novel bargaining strategy. Based on this strategy, a user agent can simultaneously negotiate with more than one provider for driving them into competition. As a result, concessions may be made by service providers depending on their own economic equilibrium theories to offer better QoS values than their initial proposals and thus a user can win more profits. Through recursively using bargaining strategy, critical nodes of an initially built optimal service composition execution path by local optimization policy can be continuously updated to minimize QoS constraint violations by reselecting better service providers. Eventually, a near-optimal service composition execution path can be reconstructed meeting user’s overall QoS requirements. Meanwhile, an experiment and evaluation have been conducted for the sake of demonstrating the feasibility and effectiveness of our proposed approach.

Dynamic Establishment Of Sla With Automated Negotiation And Privacy Policy For E-Business Application

The Service Level Agreements (SLA) are e- Contracts that need to be established among business partners and monitored to ensure that web services comply with the agreed Quality of Service values. Establishment of SLA among the component services of a composite service and the users becomes important. The establishment is time consuming when done manually because it involves negotiation of parameters to be agreed by the participants. Hence an algorithm is proposed for automated negotiation and a framework for automated contract establishment is designed and implemented. For web services to be successful, privacy must be protected. Today, privacy has become more important concern for both users and web service provider. More people will use web services if they feel all the information released are secure enough. Privacy can often be guaranteed through security measures. To enable privacy protection for Web services, customers are allowed to specify their preferences about the disclosure of their information. In this paper its demonstrated with a suitable e-Business application of Purchase and Registration of Vehicles along with the privacy is ensured during composition of the individual web services for the customer's details.

QoS-Aware Web Service Recommendation by Collaborative Filtering

With increasing presence and adoption of Web services on the World Wide Web, Quality-of-Service (QoS) is becoming important for describing nonfunctional characteristics of Web services. In this paper, we present a collaborative filtering approach for predicting QoS values of Web services and making Web service recommendation by taking advantages of past usage experiences of service users. We first propose a user-collaborative mechanism for past Web service QoS information collection from different service users. Then, based on the collected QoS data, a collaborative filtering approach is designed to predict Web service QoS values. Finally, a prototype called WSRec is implemented by Java language and deployed to the Internet for conducting real-world experiments. To study the QoS value prediction accuracy of our approach, 1.5 millions Web service invocation results are collected from 150 service users in 24 countries on 100 real-world Web services in 22 countries. The experimental results show that our algorithm achieves better prediction accuracy than other approaches. Our Web service QoS data set is publicly released for future research.

Reliable web service selection via QoS uncertainty computing

Performance of web services may fluctuate owing to the dynamic internet environment, which makes the Quality–of–Service (QoS) of web services inherently uncertain. With the increase in web services in the internet, selecting the optimal service from a set of functionally equivalent candidates becomes an important research problem. In this paper, we propose an efficient and effective approach for reliable web service selection. Our approach first employs cloud model to compute the QoS uncertainty for pruning redundant services while extracting reliable services. Then, based on QoS uncertainty computing, Mixed Integer Programming (MIP) is used to select optimal services. We evaluate our approach experimentally on real–world web services as well as randomly generated QoS values. The experimental results show that our approach can provide reliable and efficient service selection for users.

Self-adapting Strategies guided by Diagnosis and Situation Assessment in Collaborative Communicating Systems

Coping with context changes in networked systems requires considering self-adaptive communication protocols in the design of the future generation of communication systems. The communication system configuration then dynamically changes according to the user’s requirements and to the load of the communication resources. Dealing with this problem requires the capacity of detecting the possible degradations of the Quality of Service (QoS) and of dynamically modifying the behavior of the communication protocols for each new context situation. This requires in turn both monitoring the QoS values, detecting the degradations, identifying their origins through appropriate diagnosis and executing reconfiguration actions. We propose to implement such functions by considering an event-based model-driven diagnosis approach leading the dynamic composition of communication protocols for the execution of the reconfiguration. We consider a chronicle-based diagnosis approach and we apply our ideas to the Transport layer of communication systems. In this position paper, we show the relevance of our ideas and present preliminary ideas towards an integrated automated approach.

A QoS Driven Web Service Composition Method Based on ESGA (Elitist Selection Genetic Algorithm) with an Improved Initial Population Selection Strategy

With more and more web services being applied, the quality of service (QoS) becomes an important criterion in a service selection. We propose a UDDI process with QoS extension to support quality requests. Service providers can send functional and QoS information to the UDDI registry. As a component of the UDDI registry, the QoS calculator calculates the QoS value of each service to decide the optimal service and send the information back to the UDDI registry. After receiving the requestor's feedback, the QoS calculator re-calculates QoS of the service. In a service composition, component services and the composed service should satisfy local and global restrictions respectively. The QoS of component services aggregate to QoS of the composed service according to an aggregation rule. The QoS parameters may conflict with each other, so this problem is actually a multi-objective optimization. A service selection can be seen as a path formation and each node of the path is a service class. A genetic algorithm can be used to solve complicated global optimization problems. It is proved that a simplex genetic algorithm cannot converge to optimal global solution but the elitist selection genetic algorithm (ESGA) can. So we use ESGA to solve this problem. We use integer encoding as the encoding rule. A composed service can be seen as a chromosome and a gene is an integer that represents a service number in its service class. We use an initial population selection strategy instead of the random initial population creation method. In the strategy, if the fitness of a new composed service is worse than the average fitness of the selected composed services, it should be abandoned, otherwise it should be kept. We coordinate these objectives by assigning a weight to each parameter and the fitness function is the weighted sum of QoS parameters. In the stage of genetic operation, the penalty function is subtracted from the fitness of the composed service if it does not satisfy the restrictions. We use the roulette wheel selection method, single point crossover operator, and uniform mutation operator as genetic operators. It is shown that this method has better performance than the method using ESGA through an experiment.

Grid Service Discovery with Rough Sets

The computational grid is rapidly evolving into a service-oriented computing infrastructure that facilitates resource sharing and large-scale problem solving over the Internet. Service discovery becomes an issue of vital importance in utilizing grid facilities. This paper presents ROSSE, a Rough sets-based search engine for grid service discovery. Building on the Rough sets theory, ROSSE is novel in its capability to deal with the uncertainty of properties when matching services. In this way, ROSSE can discover the services that are most relevant to a service query from a functional point of view. Since functionally matched services may have distinct nonfunctional properties related to the quality of service (QoS), ROSSE introduces a QoS model to further filter matched services with their QoS values to maximize user satisfaction in service discovery. ROSSE is evaluated from the aspects of accuracy and efficiency in discovery of computing services.

Admission of QoS aware users in a smart network

Smart networks have grown out of the need for stable, reliable, and predictable networks that will guarantee packet delivery under Quality of Service (QoS) constraints. In this article we present a measurement-based admission control algorithm that helps control traffic congestion and guarantee QoS throughout the lifetime of a connection. When a new user requests to enter the network, probe packets are sent from the source to the destination to estimate the impact that the new connection will have on the QoS of both the new and the existing users. The algorithm uses a novel algebra of QoS metrics, inspired by Warshall's algorithm, to look for a path with acceptable QoS values to accommodate the new flow. We describe the underlying mathematical principles and present experimental results obtained by evaluating the method in a large laboratory test-bed operating the Cognitive Packet Network (CPN) protocol.

A Generic Approach to QoS-Based Transceiver Optimization

We propose a generic framework for jointly optimizing the transmit power allocation and the adaptive receive strategies in a multiuser network with individual quality-of-service (QoS) requirements. The QoS is assumed to be a one-to-one mapping of the signal-to-interference-plus-noise ratio (SINR). The feasibility of certain target QoS depends on the mutual interference and on a given sum-power constraint. This coupling can be modeled by interference functions, which determine how the transmit powers cause interference to the individual users. We show fundamental properties, like continuity and feasibility for the most general case when the functions are defined by axioms. Additional properties are shown for the case when the interference functions are based on a parameter-dependent coupling matrix, which allows to apply techniques from the theory of nonnegative matrices. We derive a class of iterative algorithms, which exploit the matrix structure. The proposed iteration converges monotonically to the global optimum. Starting from the same initialization, it is better than the known fixed-point iteration. It achieves arbitrary QoS values within the QoS-feasible region.

A probabilistic approach to modeling and estimating the QoS of web-services-based workflows

Web services promise to become a key enabling technology for B2B e-commerce. One of the most-touted features of Web services is their capability to recursively construct a Web service as a workflow of other existing Web services. The quality of service (QoS) of Web-services-based workflows may be an essential determinant when selecting constituent Web services and determining the service-level agreement with users. To make such a selection possible, it is essential to estimate the QoS of a WS workflow based on the QoSs of its constituent WSs. In the context of WS workflow, this estimation can be made by a method called QoS aggregation. While most of the existing work on QoS aggregation treats the QoS as a deterministic value, we argue that due to some uncertainty related to a WS, it is more realistic to model its QoS as a random variable, and estimate the QoS of a WS workflow probabilistically. In this paper, we identify a set of QoS metrics in the context of WS workflows, and propose a unified probabilistic model for describing QoS values of a broader spectrum of atomic and composite Web services. Emulation data are used to demonstrate the efficiency and accuracy of the proposed approach.

Higher packing density and lower power with complementary MOS : R. A. Bishop. Electron. Engng, October (1972), p. 61

The Wireless Sensor Networks (WSNs) deployment introduces many issues and challenges mainly in terms of energy independence. In this context, we adopted the IBM control loop which is composed of four steps (Monitor, Analyze, Plan and Execute) to manage Quality of Service (QoS)1. This paper focuses on the first step which consists in sending periodically QoS values such as the value of power remaining in the battery of each sensor. We notice that the transmission process is very costly in terms of energy and reduces the battery lifetime. In this work, we propose a probabilistic approach that estimates a part of these QoS monitoring values and therefore economizes their transmission energy. Our approach is based on the hidden Markov chain and the fuzzy logic. It is composed of two steps: (i) learning which allows apprehending the WSNs behavior and (ii) prediction which estimates QoS monitoring values. A WSN application deployed in a datacenter is studied as an illustration. The carried out experiments over AZEM2 WSN simulator show that the gain varies from 25% to 75% of the battery energy.