생체의 모델링과 Simulation

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

This paper studies the dynamic web service selection problem in a failure-prone environment, which aims to determine a subset of Web services to be invoked at run-time so as to successfully orchestrate a composite web service. We observe that both the composite and constituent web services often constrain the sequences of invoking their operations and therefore propose to use finite state machine to model the permitted invocation sequences of Web service operations. We assign each state of execution an aggregated reliability to measure the probability that the given state will lead to successful execution in the context where each web service may fail with some probability. We show that the computation of aggregated reliabilities is equivalent to eigenvector computation and adopt the power method to efficiently derive aggregated reliabilities. In orchestrating a composite Web service, we propose two strategies to select Web services that are likely to successfully complete the execution of a given sequence of operations. A prototype that implements the proposed approach using BPEL for specifying the invocation order of a web service is developed and served as a testbed for comparing our proposed strategies and other baseline Web service selection strategies.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=1772&context=sis_research

Dynamic Web Service Selection for Reliable Web Service Composition

Web services orchestrations and choreographies require establishing quality of service (QoS) contracts with the user. This is achieved by performing QoS composition, based on contracts established between the orchestration and the called Web services. These contracts are typically stated in the form of hard guarantees (e.g., response time always less than 5 msec). In this paper we propose using soft contracts instead. Soft contracts are characterized by means of probability distributions for QoS parameters. We show how to compose such contracts, to yield a global contract (probabilistic) for the orchestration. Our approach is implemented by the TOrQuE tool. Experiments on TOrQuE show that overly pessimistic contracts can be avoided and significant room for safe overbooking exists.

Probabilistic QoS and soft contracts for transaction based Web services

Companies realize their services by business processes to stay competitive in a dynamic market environment. In particular, they track the current state of the process to detect undesired deviations, to provide customers with predicted remaining durations, and to improve the ability to schedule resources accordingly. In this setting, we propose an approach to predict remaining process execution time, taking into account passed time since the last observed event.

While existing approaches update predictions only upon event arrival and subtract elapsed time from the latest predictions, our method also considers expected events that have not yet occurred, resulting in better prediction quality. Moreover, the prediction approach is based on the Petri net formalism and is able to model concurrency appropriately. We present the algorithm and its implementation in ProM and compare its predictive performance to state-of-the-art approaches in simulated experiments and in an industry case study.

/pdf/prediction-of-remaining-service-execution-time-using-fg431dqajk.pdf

Prediction of Remaining Service Execution Time Using Stochastic Petri Nets with Arbitrary Firing Delays

Web services promise to become a key enabling technology for B2B e-commerce. Several languages have been proposed to compose Web services into workflows. The QoS of the Web services-based workflows may play an essential role in choosing constituent Web services and determining service level agreement with their users. In this paper, we identify a set of QoS metrics in the context of Web services and propose a unified probabilistic model for describing QoS values of (atomic/composite) Web services. In our model, each QoS measure of a Web service is regarded as a discrete random variable with probability mass function (PMF). We describe a computation framework to derive QoS values of a Web services-based workflow. Two algorithms are proposed to reduce the sample space size when combining PMFs. The experimental results show that our computation framework is efficient and results in PMFs that are very close to the real model.

Haojun Wang

Papers

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

A Probabilistic QoS Model and Computation Framework for Web Services-Based Workflows

A Probabilistic QoS Model and Computation Framework for Web Services-Based Workflows

A probabilistic QoS model and computation framework for Web services-based workflows