Optimal Web Service Composition Research Articles

User's intention and context as pertinent factors for optimal web service composition

User's intention and context as pertinent factors for optimal web service composition

An Enhanced Energy-Efficient Web Service Composition Algorithm Based on the Firefly Algorithm

Numerous web services with the same function but different service qualities are constantly emerging on the network. Optimizing web service composition based on multiple candidate services sets an urgent problem in the service composition neighborhood. This paper modifies the traditional Firefly algorithm and adds exchange and mutation mechanisms to optimize the Web service composition efficiently in multiple candidate service sets. Meanwhile, it discretizes the continuous space of its solution set and better adapts to the service composition optimization problem. Experimental results show that compared with the GA, IA, SA, ACO, FACO, and EFACO algorithms, this algorithm has better optimization performance, faster speed, and higher energy efficiency for solving service composition optimization problems in the case of large-scale data. The higher the combined complexity of the solution, the stronger the performance compared to other algorithms. It can better deal with the increasingly complex situation of Web service composition problems.

Domain Constraints-Driven Automatic Service Composition for Online Land Cover Geoprocessing

With the rapid development of web service technology, automatic land cover web service composition has become one of the key challenges in solving complex geoprocessing tasks of land cover. Service composition requires the creation of service chains based on semantic information about the services and all the constraints that should be respected. Artificial intelligence (AI) planning algorithms have recently significantly progressed in solving web service composition problems. However, the current approaches lack effective constraints to guarantee the accuracy of automatic land cover service composition. To address this challenge, the paper proposes a domain constraints-driven automatic service composition approach for online land cover geoprocessing. First, a land cover service ontology was built to semantically describe land cover tasks, data, and services, which assist in constructing domain constraints. Then, a constraint-aware GraphPlan algorithm was proposed, which constructs a service planning graph and searches services based on the domain constraints for generating optimal web service composition solutions. In this paper, the above method was integrated into a web prototype system and a case study for the online change detection automatic geoprocessing was implemented to test the accuracy of the method. The experimental results show that with this method, a land cover service chain can generate automatically by user desire objective and domain constraints, and the service chain execution result is more accurate.

Multi‐objective memetic approach for the optimal web services composition

AbstractService composition is the process of combining a set of elementary or atomic services. The aim is to produce a new composite service to satisfy the user's request that cannot be satisfied by the atomic services. Combining multiple services is a complex problem that has been the subject of several research studies. The meta‐heuristic approaches are good techniques that have been used to solve several complex problems in various domains. These techniques are able to discover promising search regions and locate good quality solutions in reasonable time without exploring the whole solution space. In this paper, we deal with the problem of optimal web service composition by using meta‐heuristic approaches. Given a set of services and a set of tasks to be completed, the problem is to find the best set of services composition to complete all tasks where each service must be assigned to a given task. This problem can be modelled as a combinatorial optimization problem with a set of objective functions that need to be optimized. We search for a composite service that allows us to execute the considered tasks and offers the best quality of services (QoS). More precisely, we search for an execution plan that indicates for each task the assigned service. First, we propose a multi‐objective local search based meta‐heuristic (MO‐LS) and a multi‐objective genetic algorithm (MO‐GA) to handle our problem. Then we propose a multi‐objective memetic algorithm (MO‐MA) that combines the two methods LS and GA. The role of GA is to detect promising regions to be explored. The role of LS is to exploit efficiently the potential regions created by GA. Four objective functions are used to compute the Pareto optimal set of solutions. The main objective is to minimize cost and time and to maximize availability and reputation and produce a good composite service. The three proposed approaches namely MO‐LS, MO‐GA, and MO‐MA are evaluated on some datasets generated randomly and on the well‐known QWS dataset to select the best fit services in terms of maximum or minimum aggregated end‐to‐end QoS parameters. The numerical results are encouraging and demonstrate the effectiveness of the proposed MO‐MA for the web service composition.

Web Service Composition Optimization with the Improved Fireworks Algorithm

Even though the number of services is increasing, a single service can just complete simple tasks. In the face of complex tasks, we require composite multiple services to complete them. For the purpose of improving the efficiency of web service composition, we propose a service composition approach based on an improved fireworks algorithm (FWA++). First, we use the strategy of random selection to keep N − 1 individuals for the next generation, and the purpose is to speed up the convergence speed of the FWA++ and enhance the search ability. Second, we randomize the total number of sparks and maximum amplitude of sparks for each generation. In this way, the search ability and the ability to jump out of the local optimal solution are dynamically balanced throughout the execution of the algorithm. Our experimental results show that compared with other existing approaches, the approach proposed in this paper is more efficient and stable.

A Hybrid Strategy Improved Whale Optimization Algorithm for Web Service Composition

AbstractWith the rapid growth of the number of web services on the Internet, various service providers provide many similar services with the same function but different quality of service (QoS) attributes. It is a key problem to be solved urgently to select the service composition quickly, meeting the users’ QoS requirements from many candidate services. Optimization of web service composition is an NP-hard issue and intelligent optimization algorithms have become the mainstream method to solve this complex problem. This paper proposed a hybrid strategy improved whale optimization algorithm, which is based on the concepts of chaos initialization, nonlinear convergence factor and mutation. By maintaining a balance between exploration and exploitation, the problem of slow or early convergence is overcome to a certain extent. To evaluate its performance more accurately, the proposed algorithm was first tested on a set of standard benchmarks. After, simulations were performed using the real quality of web service dataset. Experimental results show that the proposed algorithm is better than the original version and other meta-heuristic algorithms on average, as well as verifies the feasibility and stability of web service composition optimization.

Improved Hunting Search Algorithm for Web Service Composition Optimization

With the rapid development and application of Web services, the problem of Web service composition optimization based on Quality of Service (QoS) has become a research hotspot in the field of service computing. There are many researches on hunting search algorithm in continuous optimization problems, however, there are few researches using it to solve Web service composition optimization. In this paper, we propose an improvement on the hunting search algorithm (IHuS). It introduces an elite guidance strategy to accelerate the convergence of the algorithm, and at the same time it learns from the mutation strategy in the GA algorithm to improve the diversity of combined services. Compared with the standard HuS and GA algorithms, the experimental results demonstrate that IHuS has faster convergence speed and better optimization performance in solving large-scale Web service composition problems.

A multi-objective optimization algorithm based on subgroup stratified coarse-grained model and its application

Low efficiency and the tendency to fall into local optimum are the major obstacles to web service composition optimization. In this paper, we propose a multi-objective optimization algorithm based on the subgroup stratified coarse-grained model to improve the performance of web service composition optimization. Compared to the general particle swarm optimization algorithms, the proposed algorithm improves population structure, dynamically adjusts evolution strategy and increases the local extremum's perturbation. We demonstrate a solution to the constrained multi-objective web service composition optimization problem based on the proposed algorithm. Theoretical analysis and experimental results show that it improves the performance of web service composition optimization.

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.

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 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 Method Based on Improved Multi-objective Artificial Bee Colony Algorithm

Web Service Composition Optimization Method Based on Improved Multi-objective Artificial Bee Colony Algorithm

Optimizing Web Service Composition with Graphplan and Fuzzy Control

Optimizing Web Service Composition with Graphplan and Fuzzy Control

Selecting the Optimal Web Service Composition Based on QoS

The fast spread of web services in our businesses and day-to-day lives has made QoS an essential aspect for both the service providers and consumers. The main problem is how the consumer obtains a high comprehensive quality composite service when there are a large number of web services available; the choice of the optimal path depends on the QoS for every atomic service. Our contribution is studying the influence of the reputation factor in the process of selecting the optimal path in the absence of one of four factors (Availability, Reliability, Response Time, and Price) and the possibility of covering for this absence. We have used the reputation factor when calculating the QoS by using artificial bee colony algorithm for selecting the optimal web service composition; then we analyzed the impact of reputation on the process of selecting web service composition in terms of the QoS and accuracy of the solution. Also, we studied the impact of the reputation factor in the case of the absence of one of the four factors through three experiments and a set of comparisons. The result was that the reputation factor could cover factors such as availability, Response Time, and technical support. We used multiple linear regression and polynomial regression to show the prediction of the reputation factor using the four other factors. The result had higher confidence when we used multiple polynomial regression where the Residual Sum of Squares (RSS) was less than the multiple linear regression. In addition, we analyzed the association between reputation and the four other factors using ANOVA test; the result indicates that there is a significant association between reputation and (availability, response time, and price), but the association is not significant with the reliability.

An iterative mathematical decision model for cloud migration: A cost and security risk approach

SummaryThis paper presents an iterative mathematical decision model for organizations to evaluate whether to invest in establishing information technology (IT) infrastructure on‐premises or outsourcing IT services on a multicloud environment. This is because a single cloud cannot cover all types of users’ functional/nonfunctional requirements, in addition to several drawbacks such as resource limitation, vendor lock‐in, and prone to failure. On the other hand, multicloud brings several merits such as vendor lock‐in avoidance, system fault tolerance, cost reduction, and better quality of service. The biggest challenge is in selecting an optimal web service composition in the ever increasing multicloud market in which each provider has its own pricing schemes and delivers variation in the service security level. In this regard, we embed a module in the cloud broker to log service downtime and different attacks to measure the security risk. If security tenets, namely, security service level agreement, such as availability, integrity, and confidentiality for mission‐critical applications, are targeted by cybersecurity attacks, it causes disruption in business continuity, leading to financial losses or even business failure. To address this issue, our decision model extends the cost model by using the cost present value concept and the risk model by using the advanced mean failure cost concept, which are derived from the embedded module to quantify cloud competencies. Then, the cloud economic problem is transformed into a bioptimization problem, which minimizes cost and security risks simultaneously. To deal with the combinatorial problem, we extended a genetic algorithm to find a Pareto set of optimal solutions. To reach a concrete result and to illustrate the effectiveness of the decision model, we conducted different scenarios and a small‐to‐medium business IT development for a 5‐year investment as a case study. The result of different implementation shows that multicloud is a promising and reliable solution against IT on‐premises deployment.

A multi-agent reinforcement learning approach to dynamic service composition

As a promising implementation of software systems, service composition has attracted significant attention and research, which constructs complex and value-added applications by composing existing single services to reduce the deployment time and cost. However, as the services on the Internet and the external environment are frequently changeable, these demand the service composition must be adaptive and dynamic to address these changes automatically. Therefore, this paper describes a multi-agent reinforcement learning model for the dynamic optimization of web service composition. In this model, agent can utilize reinforcement learning algorithms to interact with environment in real time to compute optimal composition strategy dynamically, and multi-agents mechanism can keep higher effectiveness in contrast to single-agent reinforcement learning. We propose a distributed Q-learning algorithm, which decompose the task into many sub-tasks and make every agent focus on own sub-task, to accelerate the convergence rate. In addition, we also introduce experience sharing strategy to improve the efficiency. As a result, these methods allow composite service to dynamically adjust itself to fit a varying environment, where the properties of the component services continue changing. Finally, a series of comparable experiments with traditional Q-learning algorithm demonstrate that our algorithms have certain validity, higher efficiency and obvious advantages.

Multi-objective quantum inspired Cuckoo search algorithm and multi-objective bat inspired algorithm for the web service composition problem

One of the most interesting challenges introduced by web services is the automatic web service composition design. The goal is to obtain an optimal web service composition by combining existing ones. In this paper two optimisation methods are proposed to design the best composition, a multi-objective quantum inspired Cuckoo search algorithm and a multi-objective bat inspired algorithm. The particularity of the approach is that the composition solution is gradually built using one of the two algorithms starting from the user request. Another particularity is that two optimisation criteria are considered, the quality of service and the semantic distance. The multi-criteria selection is handled by considering the Pareto front which ensures that no criteria can be improved without degrading another one. A prototype has been realised and applied to a text translation case study. The obtained results from the experimentations are encouraging and proves the feasibility and effectiveness of the approach.

A Novel Approach for Optimizing Web Service Composition Considering Inter-Web Service Dependency Using Conditional Probability

Nowadays, due to the rapid growth of the Internet and increasing number of similar services with different qualities, finding the optimum composition of services in response to users’ requests is an important issue. Furthermore, the interaction between service providers and the dependencies between the services, converts the selection of an optimal composition that satisfies the user needs, in to a new problem. In this paper, the mentioned problem is solved using the conditional probability. In the proposed approach, some candidate services participating in the composition process plan are eliminated; therefore, the population is shrinked and the speed of finding the optimum composition service is then increased; which is via evaluations of the approach.

An Online Performance Prediction Framework for Service-Oriented Systems

The exponential growth of Web service makes building high-quality service-oriented systems an urgent and crucial research problem. Performance of the service-oriented systems highly depends on the remote Web services as well as the unpredictability of the Internet. Performance prediction of service-oriented systems is critical for automatically selecting the optimal Web service composition. Since the performance of Web services is highly related to the service status and network environments which are variable over time, it is an important task to predict the performance of service-oriented systems at run-time. To address this critical challenge, this paper proposes an online performance prediction framework, called OPred, to provide personalized service-oriented system performance prediction efficiently. Based on the past usage experience from different users, OPred builds feature models and employs time series analysis techniques on feature trends to make performance prediction. The results of large-scale real-world experiments show the effectiveness and efficiency of OPred.

Optimizing Web Service Composition for Data-intensive Applications

The accessing, transferring and processing of data need to occur parallel in order to tackle the problem brought on by the increasing volume of data on the Internet. In this paper, we put forward the parallel schema for data-intensive Web services and the Benefit Ratio of Composite Services (BROCS) to balance the throughput and cost. Furthermore, we present the method to determine the degree of parallelism (DOP) based on the BROCS model to optimize the quality of the composite services. The experiment demonstrates how DOP affects the benefit ratio of the composite service. Meanwhile, the results based on the 250,000 Microblog items published by Sina show that our proposed parallel schema for Web services could effectively improve the efficiency of the composite Web service.