Adaptive Service Composition Research Articles

A feature-driven variability-enabled approach to adaptive service compositions

A feature-driven variability-enabled approach to adaptive service compositions

A feature-driven variability-enabled approach to adaptive service compositions

A feature-driven variability-enabled approach to adaptive service compositions

A Multi-Population Genetic Algorithm for Adaptive Qos-Aware Service Composition in Fog-Iot Healthcare Environment

The growth of Internet of thing (IoT) implies the availability of a very large number of services which may be similar or the same, managing the Quality of Service (QoS) helps to differentiate one service from another.The service composition provides the ability to perform complex activities by combining the functionality of several services within a single process. Very few works have presented an adaptive service composition solution managing QoS attributes, moreover in the field of healthcare, which is one of the most difficult and delicate as it concerns the precious human life.In this paper, we will present an adaptive QoS-Aware Service Composition Approach (P-MPGA) based on multi-population genetic algorithm in Fog-IoT healthcare environment. To enhance Cloud-IoT architecture, we introduce a Fog-IoT 5-layared architecture. Secondly, we implement a QoS-Aware Multi-Population Genetic Algorithm (P-MPGA), we considered 12 QoS dimensions, i.e., Availability (A), Cost (C), Documentation (D), Location (L), Memory Resources (M), Precision (P), Reliability (R), Response time (Rt), Reputation (Rp), Security (S), Service Classification (Sc), Success rate (Sr), Throughput (T). Our P-MPGA algorithm implements a smart selection method which allows us to select the right service. Also, P-MPGA implements a monitoring system that monitors services to manage dynamic change of IoT environments. Experimental results show the excellent results of P-MPGA in terms of execution time, average fitness values and execution time / best fitness value ratio despite the increase in population. P-MPGA can quickly achieve a composite service satisfying user’s QoS needs, which makes it suitable for a large scale IoT environment.

Integrating reinforcement learning and skyline computing for adaptive service composition

In service computing, combining multiple services through service composition to address complex user requirements has become a popular research topic. QoS-aware service composition aims to find the optimal composition scheme with the QoS attributes that best match user requirements. However, certain QoS attributes may continuously change in a dynamic service environment, so service composition methods need to be adaptive. Furthermore, the large number of candidate services poses a key challenge for service composition, where existing service composition approaches based on reinforcement learning (RL) suffer from low efficiency. To deal with the problems above, in this paper, a new service composition approach is proposed which combines RL with skyline computing where the latter is used for reducing the search space and computational complexity. A WSC-MDP model is proposed to solve the large-scale service composition within a dynamically changing environment. To verify the proposed method, a series of comparative experiments are conducted, and the experimental results demonstrate the effectiveness, scalability and adaptability of the proposed approach.

A model-driven approach for the verification of an adaptive service composition

The development of web service compositions is a complex task that needs coherent mechanisms in order to maintain the quality of the provided business process and to satisfy user needs. This paper proposes a development process of an adaptable composed web service and mainly focuses on the reliability and the performance properties. It explores the model driven architecture transformation technique in order to formally model the whole service orchestration using the timed coloured Petri net formalism. The software CPN Tools offers, among others, the ASK-computational tree logic, the model checking technique and several monitors that will be exploited to describe and verify different properties at design time. They will also be used via access/CPN library in order to reason about the reconfiguration technique at runtime. An example of an identity card management process is given to prove the feasibility of the proposed solution.

Large-scale and adaptive service composition based on deep reinforcement learning

Service composition is a research hotspot with practical value. With the development of Web service, many Web services with the same functional attributes emerge. However, service composition optimization is still a big challenge since the complex and unstable composition environment. To solve this problem, we propose an adaptive service composition based on deep reinforcement learning, where recurrent neural network (RNN) is utilized for predicting the objective function, improving its expression and generalization ability, and effectively solving the shortcomings of traditional reinforcement learning in the face of large-scale or continuous state space problems. We leverage heuristic behavior selection strategy to divide the state set into hidden state and fully visible state. Effective simulation of hidden state space and fully visible state of the evaluation function can further improve the accuracy and efficiency of the combined results. We conduct comprehensive experiment and experimental results have shown the effectiveness of our method.

Quantifying the adaptability of workflow-based service compositions

Service composition, which is the integration of heterogeneous Web-accessible services, has introduced an open phenomenon for creating software systems that are deployed in an unpredictable execution context, where changes in the requirements, user preferences, and component services frequently occur. The consideration of adaptability in designing such systems is necessary in this unstable and volatile environment. There have been intensive efforts on solutions to make service compositions adaptable, but little attention is given to the evaluation of such adaptability. This paper proposes a set of metrics to quantify the adaptability of service compositions that are specified from the adaptive WS-BPEL-based frameworks. The metrics are based on two adaptability dimensions: the structure and binding variabilities. Structure variability, enables runtime changes to the composition workflow while binding variability, allows dynamic binding to concrete partner services. We evaluate the metrics through a case study, utilising variants of a travel booking process specified from different adaptive WS-BPEL-based frameworks found in the literature. The metrics are applicable to a wide spectrum of adaptive service compositions and their frameworks. A support tool is also developed to automate metrics computation. Through the metrics, adaptability of service compositions and their frameworks can be assessed and compared. This would facilitate design decisions in building flexible and robust services where adaptability is a first class concern.

A parallel refined probabilistic approach for QoS-aware service composition

Abstract Service composition integrates existing online services to provide a value-added service. With the rapid growth of web services with similar functionalities, Quality of Service (QoS) has emerged as an important quantitative criterion on non-functional aspects. The optimization of QoS-aware service composition, depending on different aggregated QoS attributes has attracted significant attention. The dynamic nature of QoS-aware service composition adds further challenges to the optimization problem. Most existing approaches ignore the diversity of solutions, which have the potential to provide alternative compositions when changes occur. A few works only partially explore the search space and do not consider the optimality of solutions and the computational cost concurrently. To address these issues, we propose a novel reactive approach, called MrEDA, which integrates the estimation of distribution algorithm (EDA), restricted boltzmann machine (RBM), and multi-agent technology. It constructs a refined probabilistic model to diversify alternative solutions and guide the search by adaptively capturing the promising information of a service composition. Meanwhile, multiple agents make use of a flexible parallelism with distinct explorations and adaptive sampling to improve the global optimization and speed up the optimization. The effectiveness and efficiency of our approach for adaptive service composition is validated through an extensive experimental evaluation.

Adaptive BPEL Service Compositions via Variability Management

Service-Oriented Architectures are a popular development paradigm to enable distributed applications constructed from independent web services. When coordinated, web services are an infrastructure to fulfill dynamic and vertical integration of business. They may face frequent changes of both requirements and execution environments. Static and predefined service compositions using business process execution language (BPEL) are not able to cater for such rapid and unpredictable context shifts. The authors propose a variability management-based adaptive and configurable service composition approach that treats changes as first-class citizens and consists of identifying, expressing, realizing, and managing changes of service compositions. The proposed approach is realized with a language called VxBPEL to support variability in service compositions and a platform for design, execution, analysis, and maintenance of VxBPEL-based service compositions. Four case studies validate the feasibility of the proposed approach while exhibiting good performance of the supporting platform.

On personalized cloud service provisioning for mobile users using adaptive and context-aware service composition

Cloud service providers typically compose their services from a number of elementary services, which are developed in-house or built by third-party providers. Personalization of composite services in mobile environments is an interesting and challenging issue to address, given the opportunity to factor-in diverse user preferences and the plethora of mobile devices at use in multiple contexts. This work proposes a framework to address personalization in mobile cloud-service provisioning. Service personalization and adaptation may be considered at different levels, including the user profile, the mobile device in use, the context of the user and the composition specification. The user’s mobile device and external services are typical sources of context information, used in our proposed algorithm to elicit context-aware services. The selection process is guided by quality-of-context criteria that combine cloud-service provider requirements and user preferences. Hence, the paper proposes an integrated framework for enhancing personalized mobile cloud-services, based on a composition approach that adapts context information using a common model of service metadata specification.

A Genetic-Based Adaptive Approach for Reliable and Efficient Service Composition

Composite services often run in dynamically changing environments where new composition requirements can be added and Web services may disappear or their quality of service may decline. The service composition requirements are classified into three groups: functional, nonfunctional, and transactional requirements. The functional computing requirements concern functional capabilities of Web services. The nonfunctional computing requirements are defined as service-level agreements (SLA) constraints. The transactional computing requirements define behavioral constraints and are expressed by designers using the acceptable termination states (ATS) concept. In this work, we proposed an adaptive service composition (ASC) approach. Such an approach is adaptive as it can be used to either define or reconfigure composite services according to SLA and ATS constraints. It introduces a new recovery capability (RC) concept for capturing the recoverability level supported by Web services. We adopted a genetic-based algorithm to model the ASC approach. The choice of such an algorithm is justified by the nature of the problem of our ASC process which belongs to the NP-hard class. We experimentally showed the effectiveness of our ASC approach and empirically studied the service composition RC in particular.

A Dynamic and Adaptable Service Composition Architecture in the Cloud Based on a Multi-Agent System

Nowadays, service composition is one of the major problems in the Cloud due to the exceptional growth in the number of services deployed by providers. Recently, atomic services have been found to be unable to deal with all client requirements. Traditional service composition gives the clients a composite service without non-functional parameters. To respond to both functional and non-functional parameters, we need a service composition. Since web services cannot communicate with each other or participate dynamically to handle changes service parameters in service composition, this issue has led us to use a dynamic entity represented by an agent based on dynamic architecture. This work proposes an agent-based architecture with a new cooperation protocol that can offer an automatic and adaptable service composition by providing a composite service with the maximum quality of service. The implementation of this model has been provided in order to evaluate the authors' system. The obtained results demonstrate the effectiveness of their proposed system.

Уніфікований процес композиції адаптивного сервісу в семантичному Веб-середовищі

Уніфікований процес композиції адаптивного сервісу в семантичному Веб-середовищі

Adaptive service composition in an ambient environment with a multi-agent system

The ambient intelligence consists of the integration of computer science technologies in the objects of the environment around us in order to create a smart one. Each object in the environment performs a different service as part of achieving a task. The adaptive Service composition is a context-aware system used in the situation where the user’s needs cannot be satisfied by a single service but by the combination of several ones. One of the main and principal characteristics of ambient environments is the adaptability to the context. The multi-agent systems with their characteristics of autonomy, proactivity, mobility and sociability are one of the most promising techniques of service composition and they became a major and significant paradigm for developping ambient systems. In this paper, we present a model of adaptive service composition in ambient environment based on multi-agent system named Multi Agent System for Adaptive Service Composition (MAS-ASC). To each object of the environment is associated an agent. The set of the agents coordinate their competences taking into account the context in order to perform an activity. MAS-ASC is based on the Contract Net Protocol in which the agents exchange their proposals in the form of call for proposal. It is implemented on Java Agent DEvelopment framework platform and has been tested to economize the consumption of energy in an intelligent lighting system example.

Integrating Reinforcement Learning with Multi-Agent Techniques for Adaptive Service Composition

Service-oriented architecture is a widely used software engineering paradigm to cope with complexity and dynamics in enterprise applications. Service composition, which provides a cost-effective way to implement software systems, has attracted significant attention from both industry and research communities. As online services may keep evolving over time and thus lead to a highly dynamic environment, service composition must be self-adaptive to tackle uninformed behavior during the evolution of services. In addition, service composition should also maintain high efficiency for large-scale services, which are common for enterprise applications. This article presents a new model for large-scale adaptive service composition based on multi-agent reinforcement learning. The model integrates reinforcement learning and game theory, where the former is to achieve adaptation in a highly dynamic environment and the latter is to enable agents to work for a common task (i.e., composition). In particular, we propose a multi-agent Q-learning algorithm for service composition, which is expected to achieve better performance when compared with the single-agent Q-learning method and multi-agent SARSA (State-Action-Reward-State-Action) method. Our experimental results demonstrate the effectiveness and efficiency of our approach.

Adaptive and context-aware service composition for IoT-based smart cities

Smart Cities are advancing towards an instrumented, integrated, and intelligent living space, where Internet of Things (IoT), mobile technologies and next generation networks are expected to play a key role. In smart cities, numerous IoT-based services are likely to be available and a key challenge is to allow mobile users perform their daily tasks dynamically, by integrating the services available in their vicinity. Semantic Service Oriented Architectures (SSOA) abstract the environment’s services and their functionalities as Semantic Web Services (SWS). However, existing service composition approaches based on SSOA do not support dynamic reasoning on user tasks and service behaviours to deal with the heterogeneity of IoT domains. In this paper, we present an adaptive service composition framework that supports such dynamic reasoning. The framework is based on wEASEL, an abstract service model representing services and user tasks in terms of their signature, specification (i.e., context-aware pre-conditions, post-conditions and effects) and conversation (i.e., behaviour with related data-flow and context-flow constraints). To evaluate our composition framework, we develop a novel OWLS-TC4-based testbed by combining simple and composite services. The evaluation shows that our wEASEL-based system performs more accurate composition and allows end-users to discover and investigate more composition opportunities than other approaches.

An approach towards adaptive service composition in markets of composed services

On-the-fly composition of service-based software solutions is still a challenging task. Even more challenges emerge when facing automatic service composition in markets of composed services for end users. In this paper, we focus on the functional discrepancy between “what a user wants” specified in terms of a request and “what a user gets” when executing a composed service. To meet the challenge of functional discrepancy, we propose the combination of existing symbolic composition approaches with machine learning techniques. We developed a learning recommendation system that expands the capabilities of existing composition algorithms to facilitate adaptivity and consequently reduces functional discrepancy. As a representative of symbolic techniques, an Artificial Intelligence planning based approach produces solutions that are correct with respect to formal specifications. Our learning recommendation system supports the symbolic approach in decision-making. Reinforcement Learning techniques enable the recommendation system to adjust its recommendation strategy over time based on user ratings. We implemented the proposed functionality in terms of a prototypical composition framework. Preliminary results from experiments conducted in the image processing domain illustrate the benefit of combining both complementary techniques.

A Goal-Directed Orchestration Approach for Agile Service Composition

Composition of services provides value added service by combining existing services and is essential to meet the varying users' requests. The need for on-demand, automated, on-the fly and failure resilient service composition led to various dynamic and adaptive service composition approaches. An overview of several existing composition approaches is provided and the limitations in these approaches are identified and depicted as research opportunities. It has been found that all these approaches behave in a rigid way to respond to the changing services environment. They are bridged by proposing a Goal-Directed Orchestration approach which employs an orchestration engine to provide flexibility in responding to the changes in dynamic services environment. To illustrate how our approach could work better than the other existing approaches, we discussed with a usage scenario in travel trip planning domain. Our proposed model is compared with the existing models based on a set of defined features.

A Goal Model for Adaptive Service Composition

A Goal Model for Adaptive Service Composition

QoS Aware Adaptive Service Composition

In current business scenario the business processes of enterprises are service oriented. The services of various applications are advertised in service registry using Web Service Description Language description for service selection and composition. Service composition process integrates services based on Business Process Execution Language description of composite service and executed by the orchestration coordinator. As all compliant services description are included in the description, the service selection and execution consumes more time users need select the service form set of complaint services. This study proposes an adaptive service composition methodology over extended web service Quality Aware Service Integration architecture (QASI). The proposed service composition methodology reads the composite service description and constructs a composition tree. In each service node of composition tree a service is replaced by one that satisfies functional and QoS requirement. During execution of composite service or binding of service, the parameters like server maximum capacity, current load on server and expected time for completing current load for service at each level of composition tree are analyzed. An alternative service will be allocated at particular level to improve the overall response time of composite service. The QASI architecture provides a repository to store composition tree as symbolic notations to ensure repeatability. Experimental results indicates selection of best service and better response time by proposed methodology using QASI architecture compared to contemporary web service composition methodology using static BPEL description.