Abstract Big services are collections of interrelated web services across virtual and physical domains, processing Big Data. Existing service selection and composition algorithms fail to achieve the global optimum solution in a reasonable time. In this paper, we design an efficient quality of service‐aware big service composition methodology using a distributed co‐evolutionary algorithm. In our proposed model, we develop a distributed NSGA‐III for finding the optimal Pareto front and a distributed multi‐objective Jaya algorithm for enhancing the diversity of solutions. The distributed co‐evolutionary algorithm finds the near‐optimal solution in a fast and scalable way.

Abstract Healthcare fraud is a significant problem greatly affecting the quality of healthcare services. Manual auditing of insurance claims extends to the delay in finding fraudulent behaviors causing huge financial loss and also putting the patients' health conditions at risk. Since the past decade, the automation of fraud detection using machine learning techniques has become a prominent research topic. Several automated fraud detection systems using machine learning techniques have been proposed so far. However, developing a healthcare fraud detection system that is adaptive to the systematic changes is still missing. Therefore, in this article, we develop primitive sub peer group analysis (PSPGA) for identifying the suspicious behaviors in health insurance claims. PSPGA is inspired by peer group analysis, a popular unsupervised learning technique, which identifies suspicious behaviors based on local pattern analysis. PSPGA distinguishes between the concept drifts and the sudden drifts and flags the sudden drifts as fraudulent. Moreover, PSPGA makes the fraud detection system adaptive to the concept drifts by considering the updates for peer groups over time.

Model checking is a popular formal technique facilitating automatic verification of finite-state transition systems, and it has been applied to almost all of the Web service verification aspects, such as control-flow, data-flow, interaction, time requirements, quality of service, security requirements, etc. However, no systematic literature review focused on model checking of Web services is available. Motivated by this fact, in this paper, we systematically review existing research works on model checking based Web service verification appeared during the period of 2002-2017. We present a verification goal based classification of the collected articles, and for each paper, we identify the verification technique, target Web service standard, flavor of the employed model checking technique and tool, and other supplementary techniques, if any. Further, we highlight some of the issues, gaps, key challenges in this area and propose some future directions.

The design principle of composability among Web services is one of the most crucial reasons for the success and popularity of Web services. However, achieving error-free automatic Web service composition is still a challenge. In this paper, we propose a recursive composition based modeling and verification technique for Web service interaction. The application of recursive composition over a Web service with respect to a given set of Web services yields a recursive composition interaction graph (RCIG). In order to capture the requirement specifications of a Web service interaction scenario, we propose recursive composition specification language (RCSL) as a requirement specification language. Further, we employ the proposed RCIG as an interpretation model to interpret the semantics of a RCSL formula. Our verification technique is based on the generation and analysis of all possible interaction patterns. Performance evaluation results, provided in this paper, show that our proposition is implementable for the real world applications. The key advantages of the proposed approach are: (i) it does not require explicit system modeling as in model checking based approaches, (ii) it captures primitive characteristics of Web service interaction patterns, such as recursive composition, sequential and parallel flow, etc, and (iii) it supports automatic composition of services.

Summary Big services are the collection of interrelated web services across virtual and physical domains, integrating service oriented computing and big data. The rapid growth of Big services that offer similar functionality with varying QoS attributes makes the process of selection and composition of these big services as highly challenging and complex. In this paper, we develop an efficient QoS‐aware Big service composition approach by applying a MapReduce based Modified Grey Wolf Optimizer (MR‐MGWO) that explores more search space, especially in a multidimensional environment. Our approach ensures an optimal balance of exploration and exploitation that enhances the convergence rate and minimizes the computational time. The empirical analysis illustrates that the performance of MR‐MGWO is superior to other similar approaches for solving Big service composition.

Summary Given a composition request, the formation of possible Web Service Composition Graphs (WSCGs) depends on the set of available services. Since the availability of Web services is dynamic, at any time, a new service can join or an existing service can leave the set of available services. A change in the set may bring the structural change in a previously formed WSCG. However, it is not always the case that a structural change in the WSCG brings the semantic change. In this paper, our aim is to verify the compositional equivalence between two WSCGs formed before and after the structural change caused by the change in the set of available services. Our proposed solution is based on an algebraic formalism, and by using the formalism, directed acyclic WSCGs are formed for a given composition request. Then, by using WSCGs, we propose the concept of composition expression and canonical composition expression. On the basis of the proposed concept of canonical composition expression, we verify compositional equivalence between two WSCGs. The advantage of our approach is that it reduces the equivalence verification to the subsumption checking between two algebraic expressions instead of directly using the WSCGs and solving a subgraph matching problem. The proposed mechanism is implemented and evaluated for the exhaustive possibilities in a travel agency case study with respect to a given composition request.

Web service composition concerns the building of new value added services by integrating the sets of existing web services. Due to the seamless proliferation of web services, it becomes difficult to find a suitable web service that satisfies the requirements of users during web service composition. This paper systematically reviews existing research on QoS-aware web service composition using computational intelligence techniques (published between 2005 and 2015). This paper develops a classification of research approaches on computational intelligence based QoS-aware web service composition and describes future research directions in this area. In particular, the results of this study confirms that new meta-heuristic algorithms have not yet been applied for solving QoS-aware web services composition.

There are a multitude of metrics available to analyze individual key performance indicators of data centers. In order to predict growth or set effective goals, it is important to choose the correct metric and be aware of their expressivity and potential limitations. As cloud based services and the use of ICT infrastructure are growing globally, continuous monitoring and measuring of data center facilities are becoming essential to ensure effective and efficient operations. In this work, we explore the diverse metrics that are currently available to measure numerous data center infrastructure components. We propose a taxonomy of metrics based on core data center dimensions. Based on our observations, we argue for the design of new metrics considering factors such as age, location, and data center typology (e.g., co-location center), thus assisting in the strategic data center design and operations processes.

Summary Cloud computing is emerging as an increasingly popular computing paradigm, allowing dynamic scaling of resources available to users as needed. This requires a highly accurate demand prediction and resource allocation methodology that can provision resources in advance, thereby minimizing the virtual machine downtime required for resource provisioning. In this paper, we present a dynamic resource demand prediction and allocation framework in multi‐tenant service clouds. The novel contribution of our proposed framework is that it classifies the service tenants as per whether their resource requirements would increase or not; based on this classification, our framework prioritizes prediction for those service tenants in which resource demand would increase, thereby minimizing the time needed for prediction. Furthermore, our approach adds the service tenants to matched virtual machines and allocates the virtual machines to physical host machines using a best‐fit heuristic approach. Performance results demonstrate how our best‐fit heuristic approach could efficiently allocate virtual machines to hosts so that the hosts are utilized to their fullest capacity. Copyright © 2016 John Wiley & Sons, Ltd.

In this paper, we propose the use of fuzzy multi attribute decision making to rank Cloud services with respect to a host of standard quality of service attributes. The important contribution of the paper is the recognition of the quality of service attributes as fuzzy sets and then formulating the selection of the best Cloud service as a fuzzy multi attribute decision making problem. We employed fuzzy `and' operator to model the final decision as the intersection of the underlying fuzzy sets. Results are compared with a previous study where only analytic hierarchy process was used and the rankings produced by the current study turned out to be different.

Currently, rich and diverse data types have been increasingly provided using the Data-as-a-Service (DaaS) model, a form of cloud computing services. However, data offered by DaaS are constrained by several data concerns that, if not automatically being reasoned properly, will lead to a wrong way of using them. In this paper, we support the assumption that data concerns should be explicitly modeled and specified in data contracts to support concern-aware data selection and utilization. Instead of relying on a specific definition of data contracts, we analyze contemporary data contracts and we present an abstract model for data contracts. Based on the abstract model, we propose several techniques for evaluating data contracts that can be integrated into data service selection and composition frameworks. We also illustrate our approach with some real world scenarios.

Services enable the transformation of the World Wide Web as distributed interoperable systems interacting beyond organizational boundaries. Service licensing enables broader usage of services and a means for designing business strategies and relationships. A service license describes the terms and conditions for the use and access of the service in a machine interpretable way that services could be able to understand. Service-based applications are largely grounded on composition of independent services. In that scenario, license compatibility is a complex issue, requiring careful attention before attempting to merge licenses. The permissions and the prohibitions imposed by the licenses of services would deeply impact the composition. Thus, service licensing requires a comprehensive analysis on composition of these rights and requirements conforming to the nature of operations performed and compensation of services used in composition. In this paper, we analyze the compatibility of service license by describing a matchmaking algorithm. Further, we illustrate the composability of service licenses by creating a composite service license that is compatible with the licenses being composed.