Complex User Requirements Research Articles

A Combinatorial Auction-based collaborative cloud services platform

In this paper, we introduce a combinatorial auction-based market model to enable Cloud Service Providers (CSPs) to satisfy complex user requirements collaboratively, where the CSPs are connected in a social network and communication costs among them cannot be ignored. However, in many situations CSPs may lie about their private information in order to maximize their earnings. Therefore, we combine the combinatorial auction with the VCG-auction mechanism to ensure that CSPs do not lie in auction. Based on above market model, we construct a collaborative cloud platform, which is divided into three layers: The user-layer receives requests from end-users, the auction-layer matches the requests with the cloud services provided by the Cloud Service Provider (CSP), and the CSP-layer forms a coalition to improve serving ability to satisfy complex requirements of users. In fact, the aim of the coalition formation is to find suitable partners for a particular CSP, and, we propose two heuristic algorithms for the coalition formation. The Breadth Traversal Algorithm (BTA) and Revised Ant Colony Algorithm (RACA) are proposed to form a coalition when bidding for a single cloud service in the auction. The experimental results show that RACA outperforms the BTA in bid price and our methods work well compared to the existing auction-based method in terms of economic efficiency. Other experiments were conducted to evaluate the impact of the communication cost on coalition formation and to assess the impact of iteration times for the optimal bidding price.

Learning improves service discovery

SummaryA distributed system of services assembled according to a service‐oriented architecture requires an efficient mechanism to discover appropriate services deployed over a network. The recent emergence of many service marketplaces makes the case for the existence of such a discovery service. These marketplaces typically provide rudimentary techniques to publish service information and associated matching activities. Such simple matching techniques are typically not suitable to address complex user requirements. Therefore, it is a challenge to discover relevant services, with a high degree of accuracy, out of existing choices. This paper discusses experiments performed on a discovery service whose search techniques incorporate learning profiles to accomplish these complex tasks. The UniFrame Resource Discovery System, which searches for required services, provided an experimental test bed for these experiments. The article describes these techniques and explains their algorithms. Experimental results illustrate the gains in the quality of selected services and reduction in the discovery time using the proposed techniques. Copyright © 2014 John Wiley & Sons, Ltd.

Efficiently Compositing and Optimizing the Quality of Heterogeneous Services

The emerging service-oriented computing bring various services together to a ‘market' for clients to request, enabling the integration of services across the distributed, heterogeneous, and wide area networks. By encapsulating diverse implementations of physical and software resources in the interface that standardizes the functions, loosely coupled services have show great potential to support a versatile integration platform for complex applications. Existing techniques can successfully support integrating services from different providers to provide applications with more complex user requirements. However, to automatically composite services with guaranteed QoS is not trivial. In this paper, the authors propose Q-SAC, a QoS optimized service automatic composition model. Two main features of Q-SAC include automatic service composition, and multidimensional QoS optimization for the composition plan. The authors show that the problem of Q-SAC QoS optimization is NP-hard, and design a heuristic simulated annealing algorithm. Through simulation the authors show that this design is practical and efficient.

A Novel User-based Web Services Evaluation and Selection Model

Abstract—Evaluation of non-functional properties (NFP) of web services is of crucial importance to successful service selection. Current web services evaluation methods have not realized that evaluation of each NFP should be in accordance with its inherent character, nor have they taken the complex requirements of users on multiple NFPs into consideration. Service which can best meet the demands of users could not be chosen based on existing web services evaluation methods. To address this problem, a novel evaluation and selection model for web services based on users is proposed in this paper. The proposed model considers all possible requirements on each NFP, and employs degree of membership, fuzzy judgment theory, probabilistic statistical method as well as the TOPSIS multi-attribute decision making method based on the hosted mathematical property for each NFP. Fuzziness sub-model F and Randomness sub-model R are established, followed with Certainty sub-model C, which finally realizes comprehensive evaluation and selection of services. Simulation results demonstrate that the proposed model can effectively select an appropriate service according to the user's requirements.

Double auction-inspired meta-scheduling of parallel applications on global grids

Meta-schedulers map jobs to computational resources that are part of a Grid, such as clusters, that in turn have their own local job schedulers. Existing Grid meta-schedulers either target system-centric metrics, such as utilisation and throughput, or prioritise jobs based on utility metrics provided by the users. The system-centric approach gives less importance to users’ individual utility, while the user-centric approach may have adverse effects such as poor system performance and unfair treatment of users. Therefore, this paper proposes a novel meta-scheduler, based on the well-known double auction mechanism that aims to satisfy users’ service requirements as well as ensuring balanced utilisation of resources across a Grid. We have designed valuation metrics that commodify both the complex resource requirements of users and the capabilities of available computational resources. Through simulation using real traces, we compare our scheduling mechanism with other common mechanisms widely used by both existing market-based and traditional meta-schedulers. The results show that our meta-scheduling mechanism not only satisfies up to 15% more user requirements than others, but also improves system utilisation through load balancing.

Exception handling in pervasive service composition using normative agents

The full integration of pervasive computing services into daily life leads to smart spaces with a wide range of intelligent devices that must be dynamically composed to provide a transparent service to users. To achieve this, pervasive services have to coordinate among themselves in both an automated and autonomous manner, with the aim of satisfying complex user requirements that no single service can fulfill. However, existing pervasive environments are normally ad-hoc and isolated systems incapable of: 1) reacting to dynamic and unforeseen situations that may raise exceptions, and 2) collaborating with pervasive services beyond their physical limits. The contributions of this work are: 1) Proposing a normative agent-based service composition method capable of handling exceptions in open pervasive systems. 2) Using the Web as the underlying infrastructure where pervasive services provided by either smart devices or web services can coexist and interact with each other. 3) Providing a modular and hierarchical agent coordination method based on virtual organizations to sustain coordination among agents belonging to multiple organizations (smart spaces). 4) Integrating exception handling mechanisms based on social norms and formalized by event calculus predicates into virtual organizations to support and guide exception handling in both a dynamic and autonomous manner.

Early quality monitoring in the development of real-time reactive systems

The increasing trend toward complex software systems has highlighted the need to incorporate quality requirements earlier in the development cycle. We propose a new methodology for monitoring quality in the earliest phases of real-time reactive system (RTRS) development. The targeted quality characteristics are functional complexity, performance, reliability, architectural complexity, maintainability, and test coverage. All these characteristics should be continuously monitored throughout the RTRS development cycle, to provide decision support and detect the first signs of low or decreasing quality as the system design evolves. The ultimate goal of this methodology is to assist developers in dealing with complex user requirements and ensure that the formal development process yields a high-quality application. Each aspect of quality monitoring is formalized mathematically and illustrated using a train–gate–controller case study.

Specification and execution of composite trading activities

With the growing number of trading opportunities available online, software tools designed to act on behalf of human traders are increasingly being used to automate trading activities. The next logical step in this evolution is the automation of composite trading activities designed to fulfill complex user goals and requirements. In this paper, we describe a model for specifying composite trading activities involving concurrent and interrelated negotiations with multiple parties and heterogeneous protocols. The model supports the specification of several types of constraints, such as the number of required successful negotiations, the limit price for the items to be traded, and the temporal constraints imposed by all trading parties. In order to guide the execution of the trading activities, we describe a novel planning and execution model for composite trading activities which is designed to maximize the expected utility of the user.

Improvement of Steel Quality by Advanced Tundish Technology in New Slab Caster at Kakogawa Works, Kobe Steel, Ltd.

Recently, continuously cast steel production is subject to complex user requirements and also to small lot sizes. To meet these requirements, a new tundish technology has been adopted in No. 4 slab caster in which a hot tundish is used repeatedly. As a result, refractory, energy and labor costs have been reduced, and higher productivity and higher quality have been achieved.These quality improvements have been achieved during the start and end of casting and also during the ladle exchange in unstable casting conditions. The countermeasures taken for quality, especially for inclusions, in unstable casting conditions are reported in this paper.