Web Service Composition Model Research Articles

Evolutionary Computing based Web Service Composition Technique for Scheduling of Workload under Cloud Environment

Objectives: The objective of this model is to design a technique to schedule the workload for the computation of web services in the heterogeneous cloud environment. As the Web Service Composition (WSC) for the execution of scientific workload in a heterogeneous cloud computing environment is a challenging task. Modern workload requires dynamic resource provisioning technique as there exist parallelization among sub-tasks and different task demands different Quality of Service (QoS) requirement. Methods: This study presents an Evolutionary Computing based Web Service Composition (EC-WSC) technique to execute a large-scale scientific workload in a heterogeneous cloud environment. A multi-objective metric for improving energy efficiency and resource utilization is modelled. Then, an improved searching mechanism for the dragonfly evolutionary computing algorithm is modelled. Findings: Experiment outcomes show EC-WSC model attains superior performance in execution time performance analysis and energy efficiency performance analysis when compared with existing resource provisioning models of workload service composition such as Deadline and Budget-Aware Workflow Scheduling (DBAWS)(1), Evolutionary Computing Multi-objective optimization for Hybrid Clouds (EC-MOH)(2), Web Service Composition (WSC)(3), and Evolutionary Multi-Objective Optimization for clouds (EMOC)(4) in terms of heterogeneous computing, workload size, multi-objective optimization, QoS metric, and optimization strategy. Our model EC-WSC has proved to be more efficient in terms of energy efficiency by a reduction of 52.13% and also reduction in execution time by 71% when compared with the WSC(3) existing Web Service Composition model. Novelty: Existing resource provisioning predominantly focused on reducing computation cost and time; however, induces task execution latency and energy overhead. However, EC-WSC is modelled to utilize resources more efficiently and meet task QoS requirements by assuring energy minimization constraints. Keywords: Cloud Computing; Heterogeneous Computing Environment; Multiobjective optimization problem; Resource Provisioning; Workload Scheduling

Hybrid Metaheuristics Web Service Composition Model for QoS Aware Services

Recent advancements in cloud computing (CC) technologies signified that several distinct web services are presently developed and exist at the cloud data centre. Currently, web service composition gains maximum attention among researchers due to its significance in real-time applications. Quality of Service (QoS) aware service composition concerned regarding the election of candidate services with the maximization of the whole QoS. But these models have failed to handle the uncertainties of QoS. The resulting QoS of composite service identified by the clients become unstable and subject to risks of failing composition by end-users. On the other hand, trip planning is an essential technique in supporting digital map services. It aims to determine a set of location based services (LBS) which cover all client intended activities quantified in the query. But the available web service composition solutions do not consider the complicated spatio-temporal features. For resolving this issue, this study develops a new hybridization of the firefly optimization algorithm with fuzzy logic based web service composition model (F3L-WSCM) in a cloud environment for location awareness. The presented F3L-WSCM model involves a discovery module which enables the client to provide a query related to trip planning such as flight booking, hotels, car rentals, etc. At the next stage, the firefly algorithm is applied to generate composition plans to minimize the number of composition plans. Followed by, the fuzzy subtractive clustering (FSC) will select the best composition plan from the available composite plans. Besides, the presented F3L-WSCM model involves four input QoS parameters namely service cost, service availability, service response time, and user rating. An extensive experimental analysis takes place on CloudSim tool and exhibit the superior performance of the presented F3L-WSCM model in terms of accuracy, execution time, and efficiency.

An Intelligent Web Service Composition and Resource-Optimization Method Using K-Means Clustering and Knapsack Algorithms

Service-oriented architecture (SOA) has emerged as a flexible software design style. SOA focuses on the development, use, and reuse of small, self-contained, independent blocks of code called web services that communicate over the network to perform a certain set of simple tasks. Web services are integrated as composite services to offer complex tasks and to provide the expected services and behavior in addition to fulfilling the clients’ requests according to the service-level agreement (SLA). Web service selection and composition problems have been a significant area of research to provide the expected quality of service (QoS) and to meet the clients’ expectations. This research paper presents a hybrid web service composition model to solve web service selection and composition problems and to optimize web services’ resource utilization using k-means clustering and knapsack algorithms. The proposed model aims to maximize the service compositions’ QoS and minimize the number of web services integrated within the service composition using the knapsack algorithm. Additionally, this paper aims to track the service compositions’ QoS attributes by evaluating and tracking the web services’ QoS using the reward function and, accordingly, use the k-means algorithm to decide to which cluster the web service belongs. The experimental results on a real dataset show the superiority and effectiveness of the proposed algorithm in comparison with the results of the state–action–reward–state–action (SARSA) and multistage forward search (MFS) algorithms. The experimental results show that the proposed model reduces the average time of the web service selection and composition processes to 37.02 s in comparison to 47.03 s for the SARSA algorithm and 42.72 s for the MFS algorithm. Furthermore, the average of web services’ resource utilization results increased by 4.68% using the proposed model in comparison to the resource utilization by the SARSA and MFS algorithms. In addition, the experimental results showed that the average number of service compositions using the proposed model improved by 26.04% compared with the SARSA and MFS algorithms.

Optimal web service composition using hybrid optimization algorithm in cloud environment

In recent years, service-based applications are deemed to be one of the new solutions to build an enterpriseapplication system. In order to answer the most demandingneeds or adaptations to the needs of changed servicesquickly, service composition is currently used to exploitthe multi-service capabilities in the Information Technologyorganizations. While web services, which have been independently developed, may not always be compatible with each other, the selection of optimal services and compositionof these services are seen as a challenging issue. In this paper we propose optimal web service composition (OWSC) model, we introduce an improved Dragonfly Algorithm (IDA) to compute services whose network positions are closer to each other and to the users, which ensures the QoS of web service and network. The rule search swarm searcher (RSSA) is used to obtain the desirable characteristics from composite web services for further optimal selection of service composition set in composite planner. Then, we propose optimal tree based replanning mechanism to adapt the execution plan to the actual behavior of already executed services by a dynamic service selection at runtime.Finally, the proposed OWSC model is going to develop by cloudsim toolkit and the performance is compared with the existing models similar to our contributions in terms of QoS parameters.

A Self-Healing Model for QoS-aware Web Service Composition

In the Web Service Composition (WSC) domain, Web Services (WSs) execute in a highly dynamic environment, as a result, the Quality of Service (QoS) of a WS is constantly evolving, and this requires tracking of the global optimization overtime to satisfy the users’ requirements. In order to make a WSC adapt to such QoS changes of WSs, we propose a self-healing model for WSC. Self-healing is the automatic discovery, and healing of the failure of a composite WS by itself due to QoS changes without interruption in the WSC and any human intervention. To the best of our knowledge, almost all the existing self-healing models in this domain substitute the faulty WS with an equivalent one without paying attention to the WS selection processes to achieve global optimization. They focus only on the WS substitution strategy. In this paper, we propose a self-healing model where we use our hybrid approach to find the optimal WSC by using Parallel Genetic Algorithm based on Q-learning, which we integrate with K-means clustering (PGAQK). The components of this model are organized according to IBM’s Monitor, Analyse, Plan, Execute, and Knowledge (MAPE-K) reference model. The PGAQK approach considers as a module in the Execute component. WS substitution strategy has also been applied in this model that substitutes the faulty WS with another equivalent one from a list of candidate WSs by using the K-means clustering technique. K-means clustering is used to prune the WSs in the search space to find the best WSs for the environment changes. We implemented this model over the NET Framework using C# programming language. A series of comparable experiments showed that the proposed model outperforms improved GA to achieve global optimization. Our proposed model also can dynamically substitute the faulty WSs with other equivalent ones in a time-efficient manner

Web Service Composition Optimization Based on Adaptive Mutant Beetle Swarm

with the continuous development of cloud computing technology and big data, web services are widely used in intelligent information processing in all walks of life, when facing a large number of services with similar functions and quality, web service composition technology is a crucial technology in web services, which enable common services to be combined and select the optimal service combination to meet the wide range of users’ needs. Aiming at the problems of algorithm accuracy and stability of existing service composition technologies, the penalty function is used to construct the fitness function in this paper, which transforms the constrained optimization problem into an unconstrained optimization problem, and realizes the global optimization. Traditional web service composition models are aimed at QoS attribute perception. This paper establishes a QoE model that focuses on user experience, which can better meet the needs of users. An adaptive mutant beetle herd algorithm was proposed in this paper, the author introduced Beetle Antennae Searching Algorithm into the common particle swarm optimization algorithm, and added an adaptive mutation factor to enhance the searching ability of the algorithm; The use of dynamic learning factors allows the algorithm to have better population diversity and enhanced convergence. The experimental results that the algorithm proposed in this paper has higher algorithm accuracy, comparatively faster convergence speed and stability.

A QoS-Oriented Novel Optimization Schemes for Web Service Composition for Improved Healthcare

Due to massive increase in Web service in order to provide users an improved service it is necessary to develop a dynamic approach based on multi constraint Quality-of-Service (QoS) driven web service composition model to recommend suitable services to intended users is a big challenge. QoS plays a prominent role in selecting a Web Service. QoS is mainly determined by several non-functional parameters like availability, reliability, robustness, integrity, accessibility, interoperability, accuracy and security. It is evidenced based on experimentation that the proposed Improved Rider Optimization scheme achieves near optimal solution where group of riders racing towards a target location and attains adaptability and scalability by making a more confident web service selection with QoS prediction.

Formal modeling and verification of cloud‐based web service composition

SummaryWeb service composition is a key technology to resolve cross‐organizational business process integration, and it faces many constraints and requirements as unexpected bugs can cause shutdown of the software. A high degree of reliability is expected from such systems, and formal modeling of web service composition is an active area of research. In this work, we propose a new approach for the formal modeling of diverse web service composition. Our approach verifies the terminate property on all possible runs of the system to ensure deadlock‐free workings. For a verification of the properties of interest, we use the NuSMV model checker and specify the properties in CTL*. The approach can be used to verify the soundness property of diverse web service composition to ensure live and deadlock‐free web services.

Modeling and Verification of Response Time of QoS-aware Web Service Composition by Timed CSP

Web service composition enables the provision/reusing of existing services in different business processes to satisfy different business requirements without investing in new infrastructure. QoS-aware web service composition seeks to help users find the optimal solution with maximization of users’ satisfaction. A number of approaches based on Communicating Sequential Processes (CSP) have been proposed to model and verify properties of web service composition. However, little work has been done in verifying inputs, outputs and QoS criteria of web service composition.In this paper, we present a framework to model and verify QoS-aware web service composition by Timed CSP. It helps verify whether the service composition can accept inputs, generate outputs, and meet QoS requirements as specified. To do the verification, transformation rules that map QoS-aware web service composition to Timed CSP are defined. We design a case study, where the model of QoS-aware web service composition is transformed into the model of process composition in Timed CSP and the program in machine-readable CSP (CSPM). Furthermore, experiments are performed by using the Failure Divergence Refinement (FDR) tool to verify inputs, outputs, and QoS of the service composition.

Context-Aware Web Service Composition based on AI Planning

ABSTRACTA web service is an application that is published on the web and can be discovered and used automatically. In many cases, a web service cannot provide the capability that the user has requested. Therefore, according to the user request, it should be possible to combine various services and produce a new composite service for the user request. Context is the information that can be used to characterize the situation of an entity. In this paper, we present our context model for web service composition and implement the newly proposed framework for context-aware web service composition and show how the newly proposed framework reduces the number of failures and composition time.

Multi-Agent Based Model for Web Service Composition

The evolution of the Internet and the competitiveness among companies were factors in the explosion of Web services. Web services are applications available on the Internet each performing a particular task. Web users often need to call different services to achieve a more complex task that can’t be satisfied by a simple service. And users often prefer to have the best services responding to their requests. In this context, we should measure the Quality of Service (QoS) which is a very important aspect of Web services in order to offer to the user the best services. “How can we ensure the composition of different services to respond the user request” is the first problem that we contribute to resolve, proposing a multi-agent based model for the automatic planification of Web services. And “guarantee the required quality of the composite Web services” is a complex task regarding the unpredictable nature and dynamics of composite Web services, so our contribution to remedy to this problem consists of the use of two classes of quality attributes. The first one considers generic and the second contains specific attributes.

A capabilities driven model for web services description and composition

Service-oriented architectures SOA foster the integration of different technologies and platforms coming from various enterprises, and bring a new level of flexible modularity that is able to guarantee end-to-end quality of service. However, a current bottleneck in the current process of modelling compositions in SOA is the expert level needed in order to achieve such a composition. This is mainly due to the imperative programming paradigm they are based on. A language such as BPEL is clearly an expert language, and specifying and programming a composition using BPEL is a lengthy, costly, and high risk process. To overcome this limitation, we propose in this paper to use a declarative approach to model services and services compositions. This approach relies on a capabilities service specification, powered by reasoning techniques to handle both functional and non-functional requirements and highly expressive interaction models without over-specifying them. It enables to support flexible and self-managed compositions that are able to adapt to changes that may happen continuously and unpredictably.

Application of WEB Service Composition in Intelligent Distribution

In 2014, Premier Keqiang Li chaired a State Council executive meeting, discussed and adopted the long-term development of the logistics industry planning, which proposed that by 2020 there will essencially establish modern logistics service system, enhancing logistics industry standards, information, intelligence, intensive level, improving the overall economic efficiency and benefit [1]. A series of modern information technologies like the Internet of things, cloud computing, big data, smart logistics, Internet plus, these promote Chinese intelligent logistics transformation [2]. The development of modern society can not work without information technology, and how to realize information sharing is one of the problem that must be resolve in the modern supply chain management. Only the various companies of supply chain share a variety of information with each others, it can manage effectively and improve ability of the decision-making about the ration of resources. However, due to the change of business, the isomeric change of enterprise information systems, changes in the structure of the supply chain and others, it hinders the information sharing in most cases. This article with the purpose of designing an intelligent distribution system based on WEB service combination, building a WEB service composition model that can make fast reaction to the market. Taking intelligent distribution system as an example to solve the obstacles that affect the information sharing in supply chain.

Effective service composition using multi-agent reinforcement learning

As online services may keep evolving, service composition should maintain certain adaptivity especially for a dynamic composition environment. Meanwhile, the large number of potential candidate services poses scalability concerns, which demand efficient composition solutions. This paper presents a multi-agent reinforcement learning model for Web service composition that effectively addresses the above challenges. In particular, we model a service composition as a Markov Decision Process. Based on the model, agents in a team would benefit from one another. In contrast to single-agent reinforcement-learning, our method can speed up the convergence to an optimal policy. We develop two multi-agent reinforcement learning algorithms. The first one introduces the concept of articulate state and distributed Q-learning to speed up the convergence time. The second one proposes the experience sharing strategy to improve the efficiency of learning. As the learning process continues throughout the life-cycle of a service composition, our algorithms can automatically adapt to the change of environment and the evolving component services. We conduct a simulation study to compare our algorithm with other similar reinforcement learning algorithms, including the traditional Q-learning algorithm, a multi-agent Sarsa algorithm, a Q-learning algorithm based on gaussian process, and a multi-agent Q-learning algorithm, to justify the effectiveness of our model and algorithm.

Preference-Based Web Service Composition: Case-Based Planning Approach

Web service selection is an indispensable process for web service composition. However it became a difficult task as many web services are increased on the web and mostly they offer similar functionalities, which service will be the best. User preferences are the key to retain only the best services for the composition. In this paper, we have proposed a web service composition model based on user preferences. To improve the process of web service composition we propose a case-based planning approach with user preferences which uses successful experiences in past to solve similar problems. In this paper we integrate user preferences in the phase of selection, adaptation and planning. Our main contributions are a new method of case retrieval, an extended algorithm of adaptation and planning with user preferences. Results obtained offer more than a solution to the user and taking both functional and non- functional requirements. trip, preferences on particular transportation companies or hotels, and times/dates for the travel. Based on the assumption that the world is regular, so similar problems have similar solutions; As a consequence, solutions for similar problems are a useful starting point for new problem-solving. Case-based planning (CBP) is a problem-solving method that uses a library of cases, where a case associates a past problem and goal description with a plan that solves the problem by achieving the goal (5). In similar situations, CBP can take advantage of previous planning experiences by reusing stored plans. In this paper, we present a novel approach to compute the best service compositions based on user preferences and CBP. The proposed approach differs from the previous works in that user's preferences are taken into count as an additional input, so we model preferences and incorporate them into the user request. We use an algorithm to determine the relevant services that may be used to answer the composition request. We propose a new composition with user preferences if the request is not solved before and our system is learning from experiences. Our approach integrates the user preferences in the process of Case-Based Planning, the multi plan adaptation and the planning. We generate the plan for the user's new request by finding a composition plan from the library of cases. The newly generated plan together with the new request can now be stored as a case in the case base for future reuse. Many CBP retrieve and adapt a single plan (6) (7). We propose to retrieve multiple plans using an algorithm for retrieving based on two steps find and select according to the preferences. The sub-plans retrieved are merged and adapted to solve the problem.

Conformance Checking and QoS Selection Based on CPN for Web Service Composition

The development of new services by composition of existing ones has gained considerable momentum as a means of integrating heterogeneous applications and realizing business collaborations. More and more Web services with similar function attributes but different QoS are available. The performance of the composed service is determined by the involved Web services. Therefore, QoS properties are crucial for selecting the Web services to take part in the composition, which can identify the best candidate Web services from a set of functionally-equivalent services. Web service composition enables seamless and dynamic integration of business applications on the web. Due to the inherent autonomy and heterogeneity of component Web services, it is difficult to predict the behavior of the overall composite service. Conformance checking identifies failure and conflict of execution of composite Web service that ensures reliable execution. In this paper we use skyline computation to select services for composition efficiently, reducing the number of candidate services to be considered. Then a novel color Petri net model of Web service composition is presented that combines QoS-based optimal service selection and consistence verification. In the model we define aggregation functions, and use a Multiple Attribute Decision Making approach for the utility function to achieve optimal services selection of QoS properties. We also propose a consistence verification approach to identify potential logical inconsistence of the semantic Web service process before the deployment. Proofs are also presented. We evaluate our approach experimentally using both real and synthetically generated datasets.

A high-level Petri net based model for web services composition and verification

The evolution of the internet and the competitiveness between enterprises are the major factors in the explosion of web services use. The concept of web services composition is a natural evolution of this technology. It aims to build new value added services, using existing ones. However, this task remains highly complex and requires formal techniques for its completion. In this paper, we propose a high level Petri nets approach that successfully accomplishes this task. In this context, we define a web service modelling technique using the G-net formalism. We also propose an expressive algebra that permits the combination of the modelled services. In order to take benefit from the Maude formal verification tools, we have suggested a method that permits the translation of a modelled service from a G-net model to an equivalent Maude specification. The defined approach has successfully been automated using the Java programming language.

Algebraic Modeling and Verification of Web Service Composition

Abstract In order to provide a rigorous and sound foundation for formal reasoning about Web services, algebraic modeling is one of the important techniques used as is witnessed from the Web service literature. However, the algebraic modeling approach for Web services (Web service algebra) is still in its infancy. To further facilitate the algebraic modeling of Web services, in this paper, we propose a composition algebra based on the notion of recursive composition. The proposed algebra is fully capable to verify the presence of behavioral equivalences and deadlock conditions in a Web service composition scenario. The main motivation for proposing Web service composition algebra is to capture the recursive nature of composition which cannot be done using traditional approaches like model checking and Petri net.

Formal Specification of the Assurance Point Web Service Composition Model

This paper presents a formal specification of the Assurance Point (AP) web service composition model. The AP model provides a flexible way of checking constraints and responding to execution errors in service composition. An AP is a combined logical and physical checkpoint, providing an execution milestone that stores critical data and interacts with integration rules (IRs) to alter program flow and to invoke different forms of recovery depending on the execution status. In this paper, the execution and recovery semantics of assurance points have been fully defined in the context of if-else, parallel, and loop control structures. The activities and complex control structures of the AP model have been formalized and tested in the Yet Another Workflow Language (YAWL) engine. By doing so, the correctness of the execution and recovery semantics in the AP model has been verified, demonstrating that YAWL nets of the AP model satisfy the soundness property. Different from existing web service composition models, the AP model presented by this research provides multiple levels of protection against service execution failure with a combination of forward and backward recovery techniques.

RDTA

Web services are very dynamic, they are all around us and we use them every day without even knowing it. In this paper, the authors define a formal model for dynamic Web services composition and they investigate how it can be used to specify and analyse backward recovery procedures, updating partner services in case of failure. First, they propose Recursive and Dynamic Timed Automata (RDTA) model, interpreted over composite service configurations, which provide a natural way to design stateful and dynamic Web services. Then, the authors define the concurrent semantics of this timed automata extension in terms of real-time rewrite theories. Analysis of the model is carried out in the Real-Time Maude system, based on checking relevant properties.