Query: Dynamic service composition

title: Applying improved particle swarm optimization for dynamic service composition focusing on quality of service evaluations under hybrid networks
abstract: Hybrid services use different protocols on various networks, such as WIFI networks, Bluetooth networks, 5G communications systems, and wireless sensor networks. Hybrid service compositions can be varied, representing an effective method of integrating into wireless scenarios context aware applications that can sense mobility via changes in user location and combining services to support target functions. In this article, improved particle swarm optimization is introduced into the quality service evaluation of dynamic service composition to meet the mobility requirements of hybrid networks. First, this work classifies hybrid services into different task groups to generate candidate sets and then interface matching is used to compare the operations of candidate services with user requirements to select the appropriate services. Second, the service composition is determined by the particle swarm optimization simulation process, which aims to identify an optimal plan based on the calculated value from quality of service. Third, considering a change of service repository, when the quality of a composite service is lower than a predefined threshold, the local greedy algorithm and global reconfiguration method are adopted to dynamically restructure composite services. Finally, a set of experiments is conducted to demonstrate the effectiveness of the proposed method for determining the dynamic service composition, particularly when the scale of hybrid services is large. The method provides a technical reference for engineering practice that will fulfill mobile computing needs.

title: A multi agent reinforcement learning approach to dynamic service composition
abstract: 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.

title: A Novel Pattern Based Dynamic Service Composition Framework
abstract: Web services are more frequently accessed from a broad range of mobile users. Service composition integrates existing services to fulfill more complex tasks facing more dynamic environment. Pattern based service composition has been proven as an appealing approach to accelerate the service composition. This paper proposes a novel pattern based dynamic service composition framework that integrates elaborately designed pattern generation and pattern enforcement approaches to produce composited services. The pattern generation is formalized as a special graph similarity matching problem and an algorithm is designed to calculate the similarity of these service composition solutions. Patterns are chosen out of the solutions by similarity with specific criteria and applied for fast service composition out of the services in the repository. The pattern enforcement applies pattern to produce service composition instances with dynamic quality based metrics. Benchmark and analysis show this framework accelerates dynamic service composition effectively and efficiently.

title: Adaptive and Dynamic Service Composition in eFlow

title: Transactional and QoS aware dynamic service composition based on ant colony optimization
abstract: Service composition facilitates seamless and flexible integration of applications from different providers. With the growing number of services that offer the same functionality but differ in non functional properties published online, an efficient approach for dynamic service selection and composition is required. Traditionally, the problem is mostly addressed either from the quality of service (QoS) aspect or from the transaction aspect. In this paper, we first investigate the transactional properties of services and focus on how to compose individual services in a transactional manner, and then formulate the problem of transactional and QoS aware dynamic service composition. By modeling the problem as a constrained directed acyclic graph, the ant colony optimization algorithm is utilized to seek a near to optimal solution efficiently. At last empirical studies are conducted and the experiments show that the proposed approach can approximate the optimal solution well while staying efficient.

title: Adaptive and Dynamic Service Composition for Cloud Based Mobile Application
abstract: Adaptive and dynamic service composition are the main research challenges in Service oriented Architecture (SOA) We are looking into improving the efficiency of dynamic composition and the methods on how to improve the discovery of web services. Currently the approaches which are available do not compose while checking for errors. In this paper, we will be composing web services using Multi Agent Methods (MAS) and Petri Net for error checking to improve the efficiency of the composition. We are implementing the composition engine using Business Process Model (BPM) to compose web services while MAS is used to locate web services. Proposed architecture is expected to produce a dynamic web service composition engine and improve reusability for Cloud Based Mobile Application.

title: MR IDPSO: A Novel Algorithm for Large Scale Dynamic Service Composition
abstract: In the era of big data, data intensive applications have posed new challenges to the field of service composition. How to select the optimal composited service from thousands of functionally equivalent services but different Quality of Service (QoS attributes has become a hot research in service computing. As a consequence, in this paper, we propose a novel algorithm MR IDPSO (MapReduce based on Improved Discrete Particle Swarm Optimization) which makes use of the improved discrete Particle Swarm Optimization (PSO) with the MapReduce to solve large scale dynamic service composition. Experiments show that our algorithm outperforms the parallel genetic algorithm in terms of solution quality and is efficient for large scale dynamic service composition. In addition, the experimental results also demonstrate that the performance of MR IDPSO becomes more better with increasing number of candidate services.

title: Adaptive and Dynamic Service Composition via Multi agent Reinforcement Learning
abstract: In the era of big data, data intensive applications have posed new challenges to the filed of service composition, i.e. composition efficiency and scalability. How to compose massive and evolving services in such dynamic scenarios is a vital problem demanding prompt solutions. As a consequence, we propose a new model for large scale adaptive service composition in this paper. This model integrates the knowledge of reinforcement learning aiming at the problem of adaptability in a highly dynamic environment and game theory used to coordinate agents' behavior for a common task. In particular, a multi agent Q learning algorithm for service composition based on this model is also proposed. The experimental results demonstrate the effectiveness and efficiency of our approach, and show a better performance compared with the single agent Q learning method.

title: A Dynamic Service Composition Model for Adaptive Systems in Mobile Computing Environments
abstract: Service based applications must be adaptable to cope with the dynamic environments in which they reside. Dynamic service composition is a common solution to achieving adaptation, but it is challenging in mobile ad hoc network (MANET) environments where devices are resource constrained and mobile. Existing solutions to dynamic service composition predefine the multiple configurations that may be possible, but this requires knowledge of the configurations a priori. Alternatively, some solutions provide on demand composition configurations, but they depend on central entities which are inappropriate in MANET environments. We propose a decentralized service composition model, in which a system dynamically adapts its business process by composing its fragments on demand, as appropriate to the constraints of the service consumer and service providers. Results show a high composition success rate for the service compositions in high mobility environments.

title: Robust Dynamic Service Composition in Sensor Networks
abstract: Service modeling and service composition are software architecture paradigms that have been used extensively in web services where there is an abundance of resources. They mainly capture the idea that advanced functionality can be realized by combining a set of primitive services provided by the system. Many efforts in web services domain focused on detecting the initial composition, which is then followed for the rest of service operation. In sensor networks, however, communication among nodes is error prone and unreliable, while sensor nodes have constrained resources. This dynamic environment requires a continuous adaptation of the composition of a complex service. In this paper, we first propose a graph based formulation for modeling sensor services that maps to the operational model of sensor networks and is amenable to analysis. Based on this model, we formulate the process of sensor service composition as a cost optimization problem and show that it is NP complete. Two heuristic methods are proposed to solve the composition problem: the top down and the bottom up approaches. We discuss centralized and distributed implementations of these methods. Finally, using ns 2 simulations, we evaluate the performance and overhead of our proposed methods.