Quality of service (QoS) time series forecasting for web services (WSs) has been studied for over a decade. In recent years, this problem has been investigated in its multistep-ahead version (namely, the problem where the prediction horizon is greater than one) for the long-term rental and use of cloud-based WSs. To solve this multistep-ahead QoS time series forecasting problem, previous research has adopted single-predictor-based strategies and conventional time series methods, such as autoregressive integrated moving average (ARIMA) models and exponential smoothing. In this article, however, we propose applying genetic programming (GP) to search for and evolve a set of multiple predictors, in which each predictor is dedicated to forecasting a specific future time point. Our GP-based approach proposes and tests two types of multiple predictors that differ from the consumed predictor inputs that drive each predictor to produce its QoS forecasting results. In the first type, the input of each predictor is a sequence of fixed previous QoS observations; in the second type, each predictor dynamically consumes the most recent QoS values, which could consist of the forecasting results of its previous predictors). Furthermore, we propose two techniques for our multipredictor-based GP approach, namely, elite individual composition (EIC) and hybrid evolution, and apply them to enhance the forecasting accuracy of our approach. Finally, based on a real-world QoS time series dataset, the proposed approach is validated and compared with several conventional methods to demonstrate its superiority in terms of accuracy; in addition, the effectiveness and efficiency of the proposed multiple predictors and the two techniques are verified in the experiment.

In research, time series-based (time-aware) QoS modeling and forecasting for Web services (WSs) has been studied for over a decade, and a large number of research papers have been published; however, this current research lacks a systematic and comprehensive survey. Thus, in this article, an insightful and detailed review and investigation of current WS QoS time series modeling and forecasting research is provided. Based on our investigation of the collected literature, we identify and divide the entire research subject into four essential research concerns (components): the addressed problem, the proposed or employed approach, the considered performance measure, and the adopted QoS time series dataset. This article reviews, classifies, and discusses current studies in terms of the four identified research concerns. Moreover, for each research concern, a set of criteria and classification are proposed for its detailed categorization and comparison. Finally, this survey also discusses the insufficiency of current studies in each research concern and the potential future directions of this research area. Overall, this article can be an informative guide for researchers to understand the concerns that they should address when studying this research subject and to clearly comprehend what has been done in current research for each identified research concern.

Web API/service technology has been attracting considerable attention and the REST (REpresentational State Transfer) architecture is now widely accepted as mainstream technology. Nonetheless, developing the means by which to discover RESTful Web APIs/services is crucial to the further development of this technology. Unfortunately, existing search engines for RESTful Web APIs/services provide only keyword-based or tag-based search functions. A failure to take into account the semantics and/or characteristics (e.g., their interface compatibility) greatly hampers the ability to find suitable APIs/services. In this study, we propose a novel approach to the discovery of RESTful Web API/services, referred to as Test-Oriented API Search with Semantic Interface Compatibility (TASSIC). This scheme involves expanding the terms of Web API/service documents based on DBpedia and WordNet. Unsuitable APIs/services are then filtered out using a systematic process, as follows: 1) calculation of semantic similarity between a set of candidate APIs/services and a user query, 2) analysis of interface compatibility between candidate APIs/services using the Hungarian Algorithm, 3) invocation of candidate APIs/services to verify functionality and availability, and 4) analysis of similarity between the actual response of the candidate services and the expected response specified in the user query. The proposed TASSIC increases the likelihood of matching APIs/services that are semantically equivalent or similar to user queries. Unit test and acceptance test are used to verify that a set of candidate APIs/services are actually available and that they actually meets user requirements. Experiment results demonstrate the efficacy of TASSIC, the accuracy of which is superior to that of existing methods.

Currently, statistical time-series methods have primarily been employed to predict time-aware dynamic quality of service (QoS) attributes for Web services. In this paper, we propose the application of genetic programming (GP) for such predictions. Our experimental results indicate that the GP-based approach is more accurate than the other approaches presented for comparison. However, for the efficient management of such attributes for cloud services, including their modeling and forecasting, the current research is insufficient because a set of research questions remains unanswered. In this paper, we first clearly define these research questions and then design and perform a set of empirical experiments to address the questions. Finally, the experimental results are exhaustively discussed to answer the studied research questions. The empirical study and analysis presented in this paper could be informative for the management (modeling and forecasting) of the time-aware dynamic QoS attributes of cloud services. For example, we verify that machine-learning approaches are generally superior to the widely used statistical time-series methods in terms of both modeling accuracy and forecasting accuracy. Furthermore, after considering a variety of situations and cases, the GP-based approach is still the best option for the studied problem. In addition, except for the technical approaches, this paper also exhaustively studies the influence of the properties of the cloud dynamic QoS attributes, including their size and time granularity.

In recent years, automated service composition has been a fervid research area in service computing. Within this area, service description plays a crucial role in terms of the development of a diverse number of automation schemes. In this paper, we provide an investigation and classification of the service description approaches that have been used in a diverse collection of automated service composition studies. To position the service description approaches used in automated service composition throughout the service description world and to clearly classify them, we propose five two-value dimensions. Using the proposed dimensions, we first perform a categorization and provide a simple introduction to current representative industrial service description standards. Subsequently, because we discovered that most of the studied automated composition approaches follow a tuple-based service description paradigm, an exhaustive classification and discussion of this paradigm is made, with the automated composition approaches adopting this paradigm as an example. Finally, we discuss issues that are currently relevant to the service description field and possible solutions. With this study, the reader can obtain a complete understanding of service description approaches used in automated service composition research, including their common formulation and assumptions.

Service-oriented architecture implemented by Web Services is one of the most popular and promising software development paradigm that has brought some challenging research issues today. One of the most important issues is how to automate web service composition at design phase. Currently, there are many researchers concentrating on service composition problem that can be partitioned into three parts, dynamic workflow composition, QoS-aware, and transaction-aware service selection. This paper addresses the issue of automatic composing Web Services into an executable workflow not only according to user's functional requirements but also to their transactional properties and QoS characteristics. We propose an automatic composition approach through genetic algorithm to satisfy user's functional requirements, QoS criteria, and transactional requirements automatically at the same time. Experimental results are presented.