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

A survey of QoS-aware web service composition techniques

Web service composition can be briefly described as the process of aggregating services with disparate functionalities into a new composite service in order to meet increasingly complex needs of users. Service composition process has been accurate on dealing with services having disparate functionalities, however, over the years the number of web services in particular that exhibit similar functionalities and varying Quality of Service (QoS) has significantly increased. As such, the problem becomes how to select appropriate web services such that the QoS of the resulting composite service is maximized or, in some cases, minimized. This constitutes an NP-hard problem as it is complicated and difficult to solve. In this paper, a discussion of concepts of web service composition and a holistic review of current service composition techniques proposed in literature is presented. Our review spans several publications in the field that can serve as a road map for future research

DSOL: a declarative approach to self-adaptive service orchestrations

Service oriented computing (SOC) has brought a simplification in the way distributed applications can be built. Mainstream approaches, however, failed to support dynamic, self-managed compositions that would empower even non-technical users to build their own orchestrations. Indeed, because of the changeable world in which they are embedded, service compositions must be able to adapt to changes that may happen at run-time. Unfortunately, mainstream SOC languages, like BPEL and BPMN, make it quite hard to develop such kind of self-adapting orchestrations. We claim that this is mostly due to the imperative programming paradigm they are based on. To overcome this limitation we propose a radically different, strongly declarative approach to model service orchestration, which is easier to use and results in more flexible and self-adapting orchestrations. An ad-hoc engine, leveraging well-known planning techniques, interprets such models to support dynamic service orchestration at run-time

Technical debt-aware and evolutionary adaptation for service composition in SaaS clouds

The advantages of composing and delivering software applications in the Cloud-Based Software as a Service (SaaS) model are offering cost-effective solutions with minimal resource management. However, several functionally-equivalent web services with diverse Quality of Service (QoS) values have emerged in the SaaS cloud, and the tenant-specific requirements tend to lead the difficulties to select the suitable web services for composing the software application. Moreover, given the changing workload from the tenants, it is not uncommon for a service composition running in the multi-tenant SaaS cloud to encounter under-utilisation and over-utilisation on the component services that affects the service revenue and violates the service level agreement respectively. All those bring challenging decision-making tasks: (i) when to recompose the composite service? (ii) how to select new component services for the composition that maximise the service utility over time? at the same time, low operation cost of the service composition is desirable in the SaaS cloud. In this context, this thesis contributes an economic-driven service composition framework to address the above challenges. The framework takes advantage of the principal of technical debt- a well-known software engineering concept, evolutionary algorithm and time-series forecasting method to predictively handle the service provider constraints and SaaS dynamics for creating added values in the service composition. We emulate the SaaS environment setting for conducting several experiments using an e-commerce system, realistic datasets and workload trace. Further, we evaluate the framework by comparing it with other state-of-the-art approaches based on diverse quality metrics

Novel optimization schemes for service composition in the cloud using learning automata-based matrix factorization

A thesis submitted to the University of Bedfordshire, in partial fulfilment of the requirements for the degree of Doctor of PhilosophyService Oriented Computing (SOC) provides a framework for the realization of loosely couple service oriented applications (SOA). Web services are central to the concept of SOC. They possess several benefits which are useful to SOA e.g. encapsulation, loose coupling and reusability. Using web services, an application can embed its functionalities within the business process of other applications. This is made possible through web service composition. Web services are composed to provide more complex functions for a service consumer in the form of a value added composite service. Currently, research into how web services can be composed to yield QoS (Quality of Service) optimal composite service has gathered significant attention. However, the number and services has risen thereby increasing the number of possible service combinations and also amplifying the impact of network on composite service performance. QoS-based service composition in the cloud addresses two important sub-problems; Prediction of network performance between web service nodes in the cloud, and QoS-based web service composition. We model the former problem as a prediction problem while the later problem is modelled as an NP-Hard optimization problem due to its complex, constrained and multi-objective nature. This thesis contributed to the prediction problem by presenting a novel learning automata-based non-negative matrix factorization algorithm (LANMF) for estimating end-to-end network latency of a composition in the cloud. LANMF encodes each web service node as an automaton which allows v it to estimate its network coordinate in such a way that prediction error is minimized. Experiments indicate that LANMF is more accurate than current approaches. The thesis also contributed to the QoS-based service composition problem by proposing four evolutionary algorithms; a network-aware genetic algorithm (INSGA), a K-mean based genetic algorithm (KNSGA), a multi-population particle swarm optimization algorithm (NMPSO), and a non-dominated sort fruit fly algorithm (NFOA). The algorithms adopt different evolutionary strategies coupled with LANMF method to search for low latency and QoSoptimal solutions. They also employ a unique constraint handling method used to penalize solutions that violate user specified QoS constraints. Experiments demonstrate the efficiency and scalability of the algorithms in a large scale environment. Also the algorithms outperform other evolutionary algorithms in terms of optimality and calability. In addition, the thesis contributed to QoS-based web service composition in a dynamic environment. This is motivated by the ineffectiveness of the four proposed algorithms in a dynamically hanging QoS environment such as a real world scenario. Hence, we propose a new cellular automata-based genetic algorithm (CellGA) to address the issue. Experimental results show the effectiveness of CellGA in solving QoS-based service composition in dynamic QoS environment

A service composition platform in cloud computing using mobile devices for smart shopping

The development of the Next Generation Networks (NGN) such as LTE, WiMax and 5G networks has resulted in the development of more diverse mobile services. Many voice and video services have been developed (e.g. Viber, Skype and WhatsApp). Social networking sites have also been developed (e.g. Facebook, Instagram and Twitter). Users of these services are increasingly expecting and demanding more complex services which have more capabilities that can improve their day to day business. Users want services that are reliable, fast and easy to use. To effectively design and implement services, Service Oriented Architecture (SOA) principles are useful and some of the advantages of designing services using SOA principles are: • Improved interoperability; • Cross platform and cross application integration; • Reusability; • Service composition. Service composition has the advantage that customized services with more features can be developed by combining two or more basic services. In this research, SOA principles are used to design a cloud based Mobile Smart Shopping Service Platform. Canal Walk Shopping Mall, which is located in Cape Town, South Africa is used as a case study. Various mobile services are composed in order to solve the problem of getting information about the services provided by the shopping mall and also to show the available parking bays, which has become a major concern due to the rapid growth of the surrounding residential and business areas. Performance measurements for the Smart Shopping service are then conducted to test its power consumption, memory usage, bandwidth usage and application timeline. Conclusions are drawn and recommendations for possible future development are then provided