Automatic Dynamic Web Service Composition: A Survey and Problem Formalization

The aim of Web service composition is to arrange multiple services into workflows supplying complex user needs. Due to the huge amount of Web services and the need to supply dynamically varying user goals, it is necessary to perform the composition automatically. The objective of this article is to overview the issues of automatic dynamic Web service composition. We discuss the issues related to the semantics of services, which is important for automatic Web service composition. We propose a problem formalization contributing to the formal definition of the pre-/post-conditions, with possible value restrictions, and their relation to the semantics of services. We also provide an overview of several existing approaches dealing with the problem of Web service composition and discuss the current achievements in the field and depict some open research areas

Cognitively-inspired Agent-based Service Composition for Mobile & Pervasive Computing

Automatic service composition in mobile and pervasive computing faces many challenges due to the complex and highly dynamic nature of the environment. Common approaches consider service composition as a decision problem whose solution is usually addressed from optimization perspectives which are not feasible in practice due to the intractability of the problem, limited computational resources of smart devices, service host's mobility, and time constraints to tailor composition plans. Thus, our main contribution is the development of a cognitively-inspired agent-based service composition model focused on bounded rationality rather than optimality, which allows the system to compensate for limited resources by selectively filtering out continuous streams of data. Our approach exhibits features such as distributedness, modularity, emergent global functionality, and robustness, which endow it with capabilities to perform decentralized service composition by orchestrating manifold service providers and conflicting goals from multiple users. The evaluation of our approach shows promising results when compared against state-of-the-art service composition models.Comment: This paper will appear on AIMS'19 (International Conference on Artificial Intelligence and Mobile Services) on June 2

Qos-Aware Web Services Composition Using Grasp with Path Relinking

In service oriented scenarios, applications are created by composing atomic services and exposing the resulting added value logic as a service. When several alternative service providers are available for composition, quality of service (QoS) properties such as execution time, cost, or availability are taken into account to make the choice, leading to the creation of QoS-aware composite web services. Finding the set of service providers that result in the best QoS is a NPhard optimization problem. This paper presents QoS-Gasp, a metaheuristic algorithm for performing QoS-aware web service composition at runtime. QoS-Gasp is an hybrid approach that combines GRASP with Path Relinking. For the evaluation of our approach we compared it with related metaheuristic algorithms found in the literature. Experiments show that when results must be available in seconds, QoS-Gasp improves the results of previous proposals up to 40%. Beside this, QoS-Gasp found better solutions than any of the compared techniques in a 92% of the runs when results must be available in 100ms; i.e. it provides compositions with a better QoS, implying cost savings, increased availability and reduced execution times for the end-user.CICYT TIN2009-07366CICYT TIN2012-32273Junta de Andalucía P12-TIC-1867Junta de Andalucía TIC-590

Requirements-driven self-optimization of composite services using feedback control

In an uncertain and changing environment, a composite service needs to continuously optimize its business process and service selection through runtime adaptation. To achieve the overall satisfaction of stakeholder requirements, quality tradeoffs are needed to adapt the composite service in response to the changing environments. Existing approaches on service selection and composition, however, are mostly based on quality preferences and business processes decisions made statically at the design time. In this paper, we propose a requirements-driven self-optimization approach for composite services. It measures the quality of services (QoS), estimates the earned business value, and tunes the preference ranks through a feedback loop. The detection of unexpected earned business value triggers the proposed self-optimization process systematically. At the process level, a preference-based reasoner configures a requirements goal model according to the tuned preference ranks of QoS requirements, reconfiguring the business process according to its mappings from the goal configurations. At the service level, selection decisions are optimized by utilizing the tuned weights of QoS criteria. We used an experimental study to evaluate the proposed approach. Results indicate that the new approach outperforms both fixed-weighted and floating-weighted service selection approaches with respect to earned business value and adaptation flexibility

Service selection with qos correlations in distributed service-based systems

© 2013 IEEE. Service selection is an important research problem in distributed service-based systems, which aims to select proper services to meet user requirements. A number of service selection approaches have been proposed in recent years. Most of them, however, overlook quality-of-service (QoS) correlations, which broadly exist in distributed service-based systems. The concept of QoS correlations involves two aspects: 1) QoS correlations among services and 2) QoS correlations of user requirements. The first aspect means that some QoS attributes of service not only depend on the service itself but also have correlations with other services, e.g., buying service 1 and then getting service 2 with half price. The second aspect means the relationships among QoS attributes of user requirements, e.g., a user can accept a service with fast response time and high service cost or the user can also accept a service with slow response time and low service cost (Fig. 1). These correlations significantly affect user selection of services. Currently, only a few existing approaches have considered QoS correlations among services, i.e., the first aspect, but they still overlook QoS correlations of user requirements, i.e., the second aspect, which are also very important in distributed service-based systems. In this paper, a novel service selection approach is proposed, which not only considers QoS correlations of services but also accounts for QoS correlations of user requirements. This approach, to the best of our knowledge, is the first one which considers QoS correlations of user requirements. Also, this approach is decentralized which can avoid the single point of failure. The experimental results demonstrate the effectiveness of the proposed approach

Semantics-aware planning methodology for automatic web service composition

Service-Oriented Computing (SOC) has been a major research topic in the past years. It is based on the idea of composing distributed applications even in heterogeneous environments by discovering and invoking network-available Web Services to accomplish some complex tasks when no existing service can satisfy the user request. Service-Oriented Architecture (SOA) is a key design principle to facilitate building of these autonomous, platform-independent Web Services. However, in distributed environments, the use of services without considering their underlying semantics, either functional semantics or quality guarantees can negatively affect a composition process by raising intermittent failures or leading to slow performance. More recently, Artificial Intelligence (AI) Planning technologies have been exploited to facilitate the automated composition. But most of the AI planning based algorithms do not scale well when the number of Web Services increases, and there is no guarantee that a solution for a composition problem will be found even if it exists. AI Planning Graph tries to address various limitations in traditional AI planning by providing a unique search space in a directed layered graph. However, the existing AI Planning Graph algorithm only focuses on finding complete solutions without taking account of other services which are not achieving the goals. It will result in the failure of creating such a graph in the case that many services are available, despite most of them being irrelevant to the goals. This dissertation puts forward a concept of building a more intelligent planning mechanism which should be a combination of semantics-aware service selection and a goal-directed planning algorithm. Based on this concept, a new planning system so-called Semantics Enhanced web service Mining (SEwsMining) has been developed. Semantic-aware service selection is achieved by calculating on-demand multi-attributes semantics similarity based on semantic annotations (QWSMO-Lite). The planning algorithm is a substantial revision of the AI GraphPlan algorithm. To reduce the size of planning graph, a bi-directional planning strategy has been developed

Resource management of replicated service systems provisioned in the cloud

Service providers seek scalable and cost-effective cloud solutions for hosting their applications. Despite significant recent advances facilitating the deployment and management of services on cloud platforms, a number of challenges still remain. Service providers are confronted with time-varying requests for the provided applications, inter- dependencies between different components, performance variability of the procured virtual resources, and cost structures that differ from conventional data centers. Moreover, fulfilling service level agreements, such as the throughput and response time percentiles, becomes of paramount importance for ensuring business advantages.In this thesis, we explore service provisioning in clouds from multiple points of view. The aim is to best provide service replicas in the form of VMs to various service applications, such that their tail throughput and tail response times, as well as resource utilization, meet the service level agreements in the most cost effective manner. In particular, we develop models, algorithms and replication strategies that consider multi-tier composed services provisioned in clouds. We also investigate how a service provider can opportunistically take advantage of observed performance variability in the cloud. Finally, we provide means of guaranteeing tail throughput and response times in the face of performance variability of VMs, using Markov chain modeling and large deviation theory. We employ methods from analytical modeling, event-driven simulations and experiments. Overall, this thesis provides not only a multi-faceted approach to exploring several crucial aspects of hosting services in clouds, i.e., cost, tail throughput, and tail response times, but our proposed resource management strategies are also rigorously validated via trace-driven simulation and extensive experiment