Non-functional Properties Of Web Services Research Articles

DeepTSQP: Temporal-aware service QoS prediction via deep neural network and feature integration

Quality of service (QoS) has been mostly applied to represent non-functional properties of web services and differentiate those with the same functionality. How to accurately predict unknown service QoS has become a hot research issue. Although existing researches have been investigated on temporal-aware service QoS prediction, conventional approaches are restricted to a couple of limitations. (1) most of them cannot well mine the time-series relationships and the interaction invocation information among users and services. (2) even although some sophisticated approaches make use of recurrent neural networks for temporal service QoS prediction, they mainly focus on the learning of user-service temporal relationship and have paid less attention to more effectively represent implicit features, resulting in low accuracy on service QoS prediction. To deal with the challenges, we propose a novel deep learning based approach called DeepTSQP to perform the task of temporal-aware service QoS prediction by feature integration. In DeepTSQP, we first present an improved temporal feature representation of users and services by integrating binarization feature and similarity feature. Then, we propose a deep neural network with gated recurrent units (GRU), learning and mining temporal features among users and services. Finally, DeepTSQP model can be trained by parameter optimization and applied to predict unknown service QoS. Extensive experiments are conducted on a large-scale real-world temporal QoS dataset WS-Dream with 27,392,643 historical QoS invocation records. The results demonstrate that DeepTSQP significantly outperforms state-of-the-art approaches for temporal-aware service QoS prediction in terms of multiple evaluation metrics.

NDMF: Neighborhood-Integrated Deep Matrix Factorization for Service QoS Prediction

Quality of service (QoS) has been mostly applied to represent non-functional properties of Web services and differentiate those with the same functionality. How to accurately predict service QoS has become a key research topic. Researchers have employed neighborhood information into matrix factorization (MF) for service QoS prediction in recent years. However, they are restricted to traditional matrix factorization that may incur a couple of limitations. 1) Conventional MF for QoS prediction linearly combines the multiplication of the latent feature representation of users and services through inner product, failing to fully capture the implicit features of user and service. 2) Most of approaches integrate user or service neighborhood as heuristics into MF model, where either location context or historical invocation records are used to calculate similar users or services. Nevertheless, combining both of them together in a collaborative way is ignored for neighborhood selection that has yet to be properly explored. To deal with the challenges, we propose a novel approach for service QoS prediction called ${N}$ eighborhood-integrated ${D}$ eep ${M}$ atrix ${F}$ actorization (NDMF), which integrates user neighborhood selected by a collaborative way into an enhanced matrix factorization model via deep neural network (DNN). We implement a prototype system and conduct extensive experiments on public and real-world large Web service dataset with almost 2,000,000 service invocations called WS-DREAM which is widely used in service QoS prediction. The experimental results demonstrate that our proposed approach significantly outperforms state-of-the-art ones in terms of multiple evaluation metrics.

IFOA4WSC: a quick and effective algorithm for QoS-aware servicecomposition

In the service-oriented environment, large-scale Service Composition (SC) has become an important research, where Quality of Service (QoS) has been applied widely. QoS is generally used to represent non-functional properties of web services and differentiate them with the same functionality. Studying how to select appropriate services for a composition from a very large number of similar candidate services is an NP-hard problem. However, there are limitations among existing methods, e.g. poor scalability and massive calculation in the exhaustion method, slow convergence speed and easy to fall into local optimum in the traditional evolutionary computation method. Thus, in this paper, an improved fruit fly optimisation algorithm called IFOA4WSC is proposed, which is quick and effective for web service composition. We analyse the convergence of fruit fly swarms in IFOA4WSC algorithm, and carry out a large number of simulation experiments in the common and random data sets. Experimental results show that IFOA4WSC is effective and highly efficient, and has better stability against classical PSO, GA and FOA.

An optimization algorithm for service composition based on an improved FOA

Large-scale service composition has become an important research topic in Service-Oriented Computing (SOC). Quality of Service (QoS) has been mostly applied to represent nonfunctional properties of web services and to differentiate those with the same functionality. Many studies for measuring service composition in terms of QoS have been completed. Among current popular optimization methods for service composition, the exhaustion method has some disadvantages such as requiring a large number of calculations and poor scalability. Similarly, the traditional evolutionary computation method has defects such as exhibiting slow convergence speed and falling easily into the local optimum. In order to solve these problems, an improved optimization algorithm, WS FOA (Web Service composition based on Fruit Fly Optimization Algorithm) for service composition, was proposed, on the basis of the modeling of service composition and the FOA. Simulated experiments demonstrated that the algorithm is effective, feasible, stable, and possesses good global searching ability.

Towards optimal discovery of web services for multiple QoS constraints and preferences

Web service discovery (WSD) is the task of matchmaking a set of relevant web services. Quality of service (QoS) has recently been applied to represent non-functional properties of web services. Therefore, when those services provide the same functionality but have different QoS values, how to effectively filter out the services that cannot satisfy the QoS constraints and rank the remaining services is still an open research issue. In this paper, we propose an integrated approach that allows a service requester to specify a functionality request, multiple QoS constraints and their preferences, and our method discovers a set of the most appropriate ranked services with QoS utility aggregation. By conducting empirical experiments on simulated web services, we validate the feasibility of our service discovery approach. The running example shows that, our approach can find more appropriate services by the satisfiability of multiple QoS constraints and the ranking of aggregated QoS.

QoS-Aware Dynamic Composition of Web Services Using Numerical Temporal Planning

Web service composition (WSC) is the task of combining a chain of connected single services together to create a more complex and value-added composite service. Quality of service (QoS) has been mostly applied to represent nonfunctional properties of web services and differentiate those with the same functionality. Many research has been done on QoS-aware service composition, as it significantly affects the quality of a composite service. However, existing methods are restricted to predefined workflows, which can incur a couple of limitations, including the lack of guarantee for the optimality on overall QoS and for the completeness of finding a composite service solution. In this paper, instead of predefining a workflow model for service composition, we propose a novel planning-based approach that can automatically convert a QoS-aware composition task to a planning problem with temporal and numerical features. Furthermore, we use state-of-the-art planners, including an existing one and a self-developed one, to handle complex temporal planning problems with logical reasoning and numerical optimization. Our approach can find a composite service graph with the optimal overall QoS value while satisfying multiple global QoS constraints. We implement a prototype system and conduct extensive experiments on large web service repositories. The experimental results show that our proposed approach largely outperforms existing ones in terms of solution quality and is efficient enough for practical deployment.

Making Web Services Tradable: A Policy-Based Approach for Specifying Preferences on Web Service Properties

In service-oriented architectures, applications are developed by incorporating services which are often provided by different organizations. Since a service might be offered under different configurations by various different organizations, sophisticated service selection and negotiation algorithms are required. Policies capture the conditions under which services are offered or requested and thereby constrain the negotiation space. However, current policy languages are ill-suited to realize beneficial trade-offs within a negotiation, since they support only Boolean decisions and cannot capture all relevant service information. Therefore, we present a novel policy language in this work that provides two main contributions: (i) we enable the specification of constraints on functional as well as non-functional properties of Web services. The functional properties include data objects and the behaviour, whereas the non-functional properties include QoS attributes. (ii) Given such constraints,we show how the concept of utility function policies can be used to define cardinal preferences over functional as well as non-functional properties. This is required to rank Web service offers, define their prices, and consequently to realize automated negotiations between service providers and requesters.

Making Web services tradable: A policy-based approach for specifying preferences on Web service properties

In service-oriented architectures, applications are developed by incorporating services which are often provided by different organizations. Since a service might be offered under different configurations by various different organizations, sophisticated service selection and negotiation algorithms are required. Policies capture the conditions under which services are offered or requested and thereby constrain the negotiation space. However, current policy languages are ill-suited to realize beneficial trade-offs within a negotiation, since they support only Boolean decisions and cannot capture all relevant service information. Therefore, we present a novel policy language in this work that provides two main contributions: (i) we enable the specification of constraints on functional as well as non-functional properties of Web services. The functional properties include data objects and the behaviour, whereas the non-functional properties include QoS attributes. (ii) Given such constraints, we show how the concept of utility function policies can be used to define cardinal preferences over functional as well as non-functional properties. This is required to rank Web service offers, define their prices, and consequently to realize automated negotiations between service providers and requesters.