The Theory of Incentives I: The Principal-Agent Model

This paper summarizes the current state of the art and recent trends in software engineering economics. It provides an overview of economic analysis techniques and their applicability to software engineering and management. It surveys the field of software cost estimation, including the major estimation techniques available, the state of the art in algorithmic cost models, and the outstanding research issues in software cost estimation.

Software engineering economics

QoS-aware Web service composition is regarded as one of the fundamental issues in service computing. Given the open and dynamic internet environment, which lacks a central control of individual service providers, we propose in this paper a novel method that seamlessly considers Quality of Service (QoS) and credibility of service providers to achieve optimal service compositions. Instead of using creditability as one of the QoS attributes, we treat it as the overall capability of a service provider to deliver its promised QoS. We aggregate both user experience (i.e., user trust) and track record (i.e., service reputation) of a provider for accurate creditability evaluation. To facilitate user decision making when multiple (and sometimes conflicting) QoS attributes are involved, we develop an automatic weight calculation approach based on rough set theory and a fuzzy analytic hierarchy process, which assigns higher weights to the more discriminative attributes. Finally, to achieve an optimal service composition, a two phase optimization process is employed, where local optimization chooses services based on creditable QoS assessment and global optimization tackles a multi-objective problem using an effective cuckoo search algorithm. Extensive experimental results show that the proposed QoS-aware service composition approach achieves desirable QoS with credibility guarantees. The performance of our proposed approach also significantly outperforms other competitive methods.

Integrating modified cuckoo algorithm and creditability evaluation for QoS-aware service composition

Service composition is a technology capable of combing a collection of existing services where many smaller services are coordinated together to form a larger one. Functionally similar services can often show different quality-of-service (QoS) properties. For a specific service composition request, how to choose from a bag of suitable services that fulfill the required functions under given quality-of-service constraints is widely believed to be a great challenge. The traditional approach usually tackles this problem by assuming fixed, bounded, or statistic QoS and views the decision-making of service composition as a static process. Instead, we address this problem by considering time-varying and fluctuating QoS and presenting a predictive-trend-aware service composition method by using a time series prediction model and genetic algorithms. We conduct extensive case studies based on multiple randomly-generated service templates with varying process configurations and show that our method outperforms existing ones.

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Predictive-Trend-Aware Composition of Web Services With Time-Varying Quality-of-Service

QoS-aware service selection seeks to find the optimal service providers to achieve the optimization goal of a service requester, such as the maximization of utility, while satisfying global QoS requirements. Service providers are usually self-interested and have some private information, such as minimum prices, that would significantly factor into the decision making of the service requester. Thus, service requesters face a decision making dilemma with incomplete information. Recent work has used iterative combinatorial auctions to address this problem. However, such studies do not sufficiently consider that the service requester can elicit the private information from service providers by observing their actions. This can help the service selection process achieve better outcomes. In this paper, we propose a type of incentive contract that can motivate the service providers to offer the QoS and prices that the service requester prefers. Based on the incentive contracts, we propose an incentive mechanism for effective service selection. In the mechanism, a service requester offers a set of incentive contracts to the service providers and then elicits their private information based on their responses to the incentive contracts. The process is iterated until the service requester finally obtains a solution that fulfills the global QoS requirements. Experimental results show that the incentive contracts have a positive impact on both service requesters and providers and that the incentive mechanism outperforms the existing combinatorial auction-based approaches in finding optimal solutions.

QoS-Aware Service Selection Using an Incentive Mechanism

An important issue in QoS-aware Web service composition is how to select a set of Web services to perform the tasks within a requested service while meeting global QoS constraints. We consider the Web services are self-interested and will use dynamic pricing strategy. In general, the service cost is the minimum price acceptable to a Web service. We can obtain a composite Web service with the maximum utility by assigning the tasks to the Web services with the lowest costs. A Web service usually will not expose his cost, and thus, we face a decision making problem with incomplete information. Recent approaches use iterative combinatorial auction to address the problem. However, truthful bidding is not optimal strategy for Web services in these approaches. In this paper, we propose an incentive mechanism for choosing the optimal Web service for each task and show there exists a Bayesian Nash equilibrium of Web services, in which each Web service will bid truthfully. Finally, the experimental results show that our mechanism outperforms the existing combinatorial auction-based approaches.

A Bayesian Nash Equilibrium of QoS-Aware Web Service Composition

In QoS-aware service selection, a service requester seeks to maximize its utility by selecting a service provider that charges the lowest service price while meeting the requester's QoS requirements. In existing selection approaches, a service requester focuses on finding providers based on their QoS and thereby ignores their service prices that could change with their QoS. High QoS may provide more benefits, but may require a high service price. As a result, the highest QoS may not produce the maximum utility. A service requester and candidate service providers have a conflicting interest over service prices. Since a provider would not reveal its minimum acceptabl price, it is important for a requester to predict the minimum price for a service that meets its QoS requirements. We propose a collaborative approach to predicting a provider's minimum price for a desired QoS based on prior usage experience. The experimental results show our approach can find the optimal service providers efficiently and effectively.

Puwei Wang

Papers

A Bayesian Nash Equilibrium of QoS-Aware Web Service Composition

A Collaborative Approach to Predicting Service Price for QoS-Aware Service Selection