An Intelligent QoS Identification for Untrustworthy Web Services Via Two-phase Neural Networks

QoS identification for untrustworthy Web services is critical in QoS management in the service computing since the performance of untrustworthy Web services may result in QoS downgrade. The key issue is to intelligently learn the characteristics of trustworthy Web services from different QoS levels, then to identify the untrustworthy ones according to the characteristics of QoS metrics. As one of the intelligent identification approaches, deep neural network has emerged as a powerful technique in recent years. In this paper, we propose a novel two-phase neural network model to identify the untrustworthy Web services. In the first phase, Web services are collected from the published QoS dataset. Then, we design a feedforward neural network model to build the classifier for Web services with different QoS levels. In the second phase, we employ a probabilistic neural network (PNN) model to identify the untrustworthy Web services from each classification. The experimental results show the proposed approach has 90.5% identification ratio far higher than other competing approaches.Comment: 8 pages, 5 figure

Considering Human Aspects on Strategies for Designing and Managing Distributed Human Computation

A human computation system can be viewed as a distributed system in which the processors are humans, called workers. Such systems harness the cognitive power of a group of workers connected to the Internet to execute relatively simple tasks, whose solutions, once grouped, solve a problem that systems equipped with only machines could not solve satisfactorily. Examples of such systems are Amazon Mechanical Turk and the Zooniverse platform. A human computation application comprises a group of tasks, each of them can be performed by one worker. Tasks might have dependencies among each other. In this study, we propose a theoretical framework to analyze such type of application from a distributed systems point of view. Our framework is established on three dimensions that represent different perspectives in which human computation applications can be approached: quality-of-service requirements, design and management strategies, and human aspects. By using this framework, we review human computation in the perspective of programmers seeking to improve the design of human computation applications and managers seeking to increase the effectiveness of human computation infrastructures in running such applications. In doing so, besides integrating and organizing what has been done in this direction, we also put into perspective the fact that the human aspects of the workers in such systems introduce new challenges in terms of, for example, task assignment, dependency management, and fault prevention and tolerance. We discuss how they are related to distributed systems and other areas of knowledge.Comment: 3 figures, 1 tabl

Stochastic reo: a case study

QoS analysis of coordinated distributed autonomous services is currently of interest in the area of service-oriented computing and calls for new technologies and supporting tools. In previous work, the first three authors have proposed a compositional automata model to provide semantics for stochastic Reo, a channel based coordination language that supports the specification of QoS values (such as request arrivals or processing rates). Furthermore, translations from this automata model into stochastic models, such as continuous-time Markov chains (CTMCs) and interactive Markov chains (IMCs) have also been presented. Based on those results, we describe in this paper a case study of QoS analysis. We analyze a certain instance of the ASK system, an industrial software system for connecting people offering professional services to clients requiring those services. We develop a model of the ASK system using stochastic Reo. The distributions used in this model were obtained by applying statistical analysis techniques on the raw values that we obtained from the real logs of an actual running ASK system. These distributions are used for the derived CTMC model for the ASK system to analyze and to improve the performance of the system, under the assumption that the distributions are exponentially distributed. In practice, this is not always the case. Thus, we also carry out a simulation-based analysis by a Reo simulator that can deal with non-exponential distributions. Compared to the analysis on the derived CTMC model, the simulation is approximation-based analysis, but it reveals valuable insight in the behavior of the system. The outcome of both analyses helps both the developers and the installations of the ASK system to improve the performance of the system

Flexible provisioning of Web service workflows

Web services promise to revolutionise the way computational resources and business processes are offered and invoked in open, distributed systems, such as the Internet. These services are described using machine-readable meta-data, which enables consumer applications to automatically discover and provision suitable services for their workflows at run-time. However, current approaches have typically assumed service descriptions are accurate and deterministic, and so have neglected to account for the fact that services in these open systems are inherently unreliable and uncertain. Specifically, network failures, software bugs and competition for services may regularly lead to execution delays or even service failures. To address this problem, the process of provisioning services needs to be performed in a more flexible manner than has so far been considered, in order to proactively deal with failures and to recover workflows that have partially failed. To this end, we devise and present a heuristic strategy that varies the provisioning of services according to their predicted performance. Using simulation, we then benchmark our algorithm and show that it leads to a 700% improvement in average utility, while successfully completing up to eight times as many workflows as approaches that do not consider service failures

Dagstuhl News January - December 2000

"Dagstuhl News" is a publication edited especially for the members of the Foundation "Informatikzentrum Schloss Dagstuhl" to thank them for their support. The News give a summary of the scientific work being done in Dagstuhl. Each Dagstuhl Seminar is presented by a small abstract describing the contents and scientific highlights of the seminar as well as the perspectives or challenges of the research topic

An Intelligent Framework for Energy-Aware Mobile Computing Subject to Stochastic System Dynamics

abstract: User satisfaction is pivotal to the success of mobile applications. At the same time, it is imperative to maximize the energy efficiency of the mobile device to ensure optimal usage of the limited energy source available to mobile devices while maintaining the necessary levels of user satisfaction. However, this is complicated due to user interactions, numerous shared resources, and network conditions that produce substantial uncertainty to the mobile device's performance and power characteristics. In this dissertation, a new approach is presented to characterize and control mobile devices that accurately models these uncertainties. The proposed modeling framework is a completely data-driven approach to predicting power and performance. The approach makes no assumptions on the distributions of the underlying sources of uncertainty and is capable of predicting power and performance with over 93% accuracy. Using this data-driven prediction framework, a closed-loop solution to the DEM problem is derived to maximize the energy efficiency of the mobile device subject to various thermal, reliability and deadline constraints. The design of the controller imposes minimal operational overhead and is able to tune the performance and power prediction models to changing system conditions. The proposed controller is implemented on a real mobile platform, the Google Pixel smartphone, and demonstrates a 19% improvement in energy efficiency over the standard frequency governor implemented on all Android devices.Dissertation/ThesisDoctoral Dissertation Computer Engineering 201

Stochastic models for quality of service of component connectors

The intensifying need for scalable software has motivated modular development and using systems distributed over networks to implement large-scale applications. In Service-oriented Computing, distributed services are composed to provide large-scale services with a specific functionality. In this way, reusability of existing services can be increased. However, due to the heterogeneity of distributed software systems, software composition is not easy and requires additional mechanisms to impose some form of a coordination on a distributed software system. Besides functional correctness, a composed service must satisfy various quantitative requirements for its clients, which are generically called its quality of service (QoS). Particularly, it is tricky to obtain the overall QoS of a composed service even if the QoS information of its constituent distributed services is given. In this thesis, we propose Stochastic Reo to specify software composition with QoS aspects and its compositional semantic models. They are also used as intermediate models to generate their corresponding stochastic models for practical analysis. Based on this, we have implemented the tool Reo2MC. Using Reo2MC, we have modeled and analyzed an industrial software, the ASK system. Its analysis results provided the best cost-effective resource utilization and some suggestions to improve the performance of the system.UBL - phd migration 201