Service Composition Research Articles

Improved Teaching Learning-Based QoS-Aware Services Composition for Internet of Things

The emergence of Internet of Things (IoT) paradigm has led to a continuous increase in the number of services that have similar functionalities but different quality of service (QoS). The IoT services composition, therefore, becomes an important challenge that aims at creating added-value services by combining several services offered by thousands of smart devices in the IoT environment to meet the user's QoS requirements. In this context, an improved teaching learning-based QoS-aware services composition algorithm (ITL-QCA) is proposed to find a very close to the optimal composition in a reasonable amount of time. Unlike the evolutionary computation-based and swarm intelligence-based approaches, the proposed services composition algorithm is characterized by a few tuning parameters and a high exploration capability of the search space of compositions. This allows obtaining compositions with a high optimality in terms of QoS and without requiring some hard tuning parameters. The simulation results show clearly that the ITL-QCA algorithm performs better in terms of the composition optimality and has a reasonable execution time in a large-scale environment compared to other services composition approaches proposed in the literature.

Collaborative Agent Communities for Resilient Service Composition in Mobile Environments

Automatic planning, with dynamic binding and adaptive composition recovery, has been used to tackle complex service provisioning in mobile environments, but given frequent network topology changes, and services with time-dependent QoS, finding composites that can functionally and non-functionally satisfy a user's request remains difficult. Many service composition mechanisms either require a centralised perspective of the environment, or use optimisation mechanisms that trade off computational efficiency for optimality. Stigmergy-based approaches have been used to model decentralised service interactions between service providers, using a community of mobile software agents that share the same goal to approximate the set of QoS-optimal service compositions. Inspired by this model, this article addresses computational efficiency concerns using a collaborative approach to engage multiple communities of agents for provisioning QoS-optimal service compositions in mobile environments. New compositions can emerge from local decisions and interactions with agents from diverse communities. We assess whether having multiple communities improves the diversity and optimality of solutions. We also measure the proposed approach’ efficiency in dealing with incomplete information. The results show that the proposed approach trades optimality for a more diverse set of solutions, at a cost of higher overhead.

Research and Development of Cloud Manufacturing Service Composition System Based on Intelligent Optimal Algorithm

For solving the problems of manufacturing resource allocation and industrial structure, an Intelligent Cloud Manufacturing Service Composition (ICMSC) method is proposed with cloud-entropy-genetic-algorithm. The development platform hierarchical construction and the development tools application are studied, and a CMSC system is developed. The traditional genetic operators are improved by using the normal cloud model. The improved cloud-entropy-genetic-algorithm combined with business intelligence is applied to the service composition and optimization module of CMSC system. Taking the CMSC of medical oxygen inhaler as an example, service selection, composition and optimization are carried out in CMSC system. The results show that it is feasible to apply the ICMSC method based on cloud-entropy-genetic-algorithm to CMSC system.

A deep learning based framework for optimizing cloud consumer QoS-based service composition

The service composition problem in Cloud computing is formulated as a multiple criteria decision-making problem. Due to the extensive search space, Cloud service composition is addressed as an NP-hard problem. In addition, it is a long term based and economically driven. Building an accurate economic model for service composition has great attention to interest and importance for the Cloud consumer. A deep learning based service composition (DLSC) framework has been proposed in this paper. The proposed DLSC framework is considered an amalgamation between the deep learning long short term memory (LSTM) network and particle swarm optimization (PSO) algorithm. The LSTM network is applied to accurately predict the Cloud QoS provisioned values, and the output of LSTM network is fed to PSO algorithm to compose the best Cloud providers to contract with them for composing the needed services to minimize the consumer cost function. The proposed DLSC framework has been implemented using a real QoS dataset. According to the comparative results, it is found that the performance of the proposed framework outperforms the existing models with respect to the predictive accuracy and composition accuracy.

Privacy in Data Service Composition

In modern information systems different information features, about the same individual, are often collected and managed by autonomous data collection services that may have different privacy policies. Answering many end-users' legitimate queries requires the integration of data from multiple such services. However, data integration is often hindered by the lack of a trusted entity, often called a mediator, with which the services can share their data and delegate the enforcement of their privacy policies. In this paper, we propose a flexible privacy-preserving data integration approach for answering data integration queries without the need for a trusted mediator. In our approach, services are allowed to enforce their privacy policies locally. The mediator is considered to be untrusted, and only has access to encrypted information to allow it to link data subjects across the different services. Services, by virtue of a new privacy requirement, dubbed k-Protection, limiting privacy leaks, cannot infer information about the data held by each other. End-users, in turn, have access to privacy-sanitized data only. We evaluated our approach using an example and a real dataset from the healthcare application domain. The results are promising from both the privacy preservation and the performance perspectives.

An Energy-Aware Multi-Target Service Composition Method in a Multi-Cloud Environment

Service composition is always employed to enhance function by composing atomic services that reside in different clouds together. In a multi-cloud environment (MCE), the time and energy consumption of service composition may differ because of the dynamic of the network. The network varies between clouds, or even between composite requests and clouds. A cloud provides a limited variety of services, which makes a composite request that requires multiple clouds to jointly compose all atomic services together. All of this makes it more difficult than ever to implement energy-aware service composite in a MCE. In this paper, we model service composition in the MCE and propose an energy-aware multiple targets service composition method for executing atomic services in a composite request. Since the proposed method gets the scheduling by searching all possible mappings between service request blocks (a set of requests to multiple atomic services) and clouds, we call it “All-Search”. To reduce the complexity of All-Search, we propose “Itersplit”, a heuristics algorithm that can achieve multiple targets. The performance of All-Search, Cloud-SEnergy, and Itersplit is tested through simulations. The results in multiple aspects indicate that Itersplit performs better than other algorithms when we take Itersplit-20%.

Predictive-Trend-Aware Composition of Web Services With Time-Varying Quality-of-Service

Service composition is a technology capable of combing a collection of existing services where many smaller services are coordinated together to form a larger one. Functionally similar services can often show different quality-of-service (QoS) properties. For a specific service composition request, how to choose from a bag of suitable services that fulfill the required functions under given quality-of-service constraints is widely believed to be a great challenge. The traditional approach usually tackles this problem by assuming fixed, bounded, or statistic QoS and views the decision-making of service composition as a static process. Instead, we address this problem by considering time-varying and fluctuating QoS and presenting a predictive-trend-aware service composition method by using a time series prediction model and genetic algorithms. We conduct extensive case studies based on multiple randomly-generated service templates with varying process configurations and show that our method outperforms existing ones.

Evaluating the trustworthiness of BPEL processes based on data dependency and XBFG

Composite services implement value-added functionality by composing service components of various granularities. Trust is an important criterion to judge whether a composite service can behave as expected. There is a great need for a flexible trust evaluation method for composite services. In this paper, a data dependency-based trust evaluation approach for composite services is proposed. Firstly, we derive define-use pairs of variables to identify data dependency between service components in BPEL processes modelled by an extensible BPEL flow graph (XBFG); then, dependency links including both direct and indirect data dependencies are used to evaluate the trust of these service components; furthermore, on the basis of BPEL structure and XBFG, reduction rules are proposed to evaluate the global trust of BPEL processes. Experimental results demonstrate that the proposed approach is effective for the trust evaluation of BPEL composite services and stable with the growing number of service components in BPEL.

Evaluating the trustworthiness of BPEL processes based on data dependency and XBFG

Composite services implement value-added functionality by composing service components of various granularities. Trust is an important criterion to judge whether a composite service can behave as expected. There is a great need for a flexible trust evaluation method for composite services. In this paper, a data dependency-based trust evaluation approach for composite services is proposed. Firstly, we derive define-use pairs of variables to identify data dependency between service components in BPEL processes modelled by an extensible BPEL flow graph (XBFG); then, dependency links including both direct and indirect data dependencies are used to evaluate the trust of these service components; furthermore, on the basis of BPEL structure and XBFG, reduction rules are proposed to evaluate the global trust of BPEL processes. Experimental results demonstrate that the proposed approach is effective for the trust evaluation of BPEL composite services and stable with the growing number of service components in BPEL.

A Hybrid Service Selection and Composition Model for Cloud-Edge Computing in the Internet of Things

Cloud-edge computing is a hybrid model of computing where resources and services provided via the Internet of Things (IoT) between large-scale and long-term data informs of the cloud layer and small-scale and short-term data as edge layer. The main challenge of the cloud service providers is to select the optimal candidate services that are doing the same work but offer different Quality of Service (QoS) values in IoT applications. Service composition in cloud-edge computing is an NP-hard problem; therefore, many meta-heuristic methods introduced to solve this issue. Also, the correctness of meta-heuristic and machine learning algorithms for evaluating service composition problem should be proven using formal methods to guarantee functional and non-functional specifications. In this paper, a hybrid Artificial Neural Network-based Particle Swarm Optimization (ANN-PSO) Algorithm presented to enhance the QoS factors in cloud-edge computing. To illustrate the correctness and improve the reachability rate of candidate composited services and QoS factors for the proposed hybrid algorithm, we present a formal verification method based on a labeled transition system to check some critical Linear Temporal Logics (LTL) formulas. The experimental results illustrated the high performance of the proposed model in terms of minimum verification time, memory consumption, and guaranteeing critical specifications rules as the Linear Temporal Logic (LTL) formulas. Also, we observed that the proposed model has optimal response time, availability, and price with maximum fitness function value than other service composition algorithms.

Service composition based on pre-joined service network in graph database

To perform an arbitrary service composition task in a graph database, and to support plug-in semantic matching of services, we present a novel service composition approach in a graph database named pre-joined service network. Firstly, the proposed approach constructs and stores a composition network with all services and compositions in a graph database. Then, it fetches a satisfying solution by converting the user's request into queries in the graph database. To illustrate the process of searching for a solution, a simple but meaningful example is provided. Furthermore, we test the performance of the proposed approach with a challenge dataset. Experiment results show that the proposed approach can always find a valid solution and lead to higher user satisfaction when compared with the pre-joined semantic indexing graph approach.

A Framework for Trustworthy Web Service Composition and Optimization

Recently, Web service composition and optimization have received an increasing attention from both academia and industrial community. Most current methods have not paid enough attention to the user specific trust requirement for composite services. However, Trust is an important metric to judge whether a composite service can behave as user expected. In this work, firstly, a multifactor concept of trust of composite service is defined based on the trust of component services, interface compatibility and optimal binding schemes. Secondly, a trustworthy Web service composition and optimization framework called TWSCO is proposed to guarantee the trust of composite service and efficiency of Web service composition process. The interface-matching problem among component services and user preference are considered in TWSCO, which firstly uses component services filter to remove untrusted component services. Secondly, the concrete services, based on interface similarity, are organized as clusters. Thirdly, a composite template among component services is formed at the cluster level to guarantee the trust and efficiency of composite service. finally, the best binding scheme is discovered by an optimization method based on user specific trust metrics. In the end, experiments based on real Web services are presented to illustrate the proposed framework TWSCO can effectively guarantee the user preference trust of the composite service.

A Choreography Analysis Approach for Microservice Composition in Cyber-Physical-Social Systems

Choreography-driven microservice composition has provided a better way to integrate components in the Cyber-physical-Social System (CPSS). Choreography is a global contract that specifies interactions among microservices participating in a composite service. After modeling a choreography, a problem arises here is whether the choreography specification at design time can be implemented correctly by generated microservices that interact with each other via exchanging messages. In this paper, we propose a novel approach for choreography analysis. Specifically, a choreography is specified using a Labeled Transition Systems (LTSs); then, the microservices participating in a composite service can be generated from the given choreography via projection and $\varepsilon $ -remove; finally, the analysis of the choreography can be checked for both synchronous and asynchronous compositions using refinement checking. Our approach is completely automated under the support of our developed tool and the Process Analysis Toolkit (PAT) tool.

QoS-Constrained Service Selection for Networked Microservices

In the microservice system, microservices are generally deployed as many microservice instances connected by a network system, and there are several composite services (CSs) in the system, each of which consists of several tasks that need to be executed on instances and has some Quality of Service (QoS) constraints to represent the user requirement for QoS. At runtime, it is necessary to select the microservice instance for each task under constraints and form an execution path of the CS. However, traditional service selection methods focus on the QoS optimization of a single CS, while ignoring sharing and competition of instances. To address this problem, this paper presents a Microservice Service Selection algorithm (MSS) based on list scheduling. Firstly, a workflow model is employed to describe the CS and to analyze the processing speed of instances, the transferring speed of the network and the degree of task concurrency, to calculate the sub-deadline of each task. Then, according to the sub-deadline and other information, the scheduling urgency of each task is calculated and updated in real-time. Finally, two service selection strategies based on the sub-deadline and urgency are proposed to complete the microservice instances selection and constitute the service path. Experiments have been carried out using the standard workflow examples of real-world applications. The results show that the proposed selection strategies can effectively improve the performance of service selection in network systems.

Agile Latency Estimation for a Real-time Service-oriented Software Architecture

This paper presents our testbed and software pipeline for automatic latency estimation for a service-oriented software architecture (SOA). This type of architecture consists of modular services that are dynamically combined at runtime to form a functioning system. As different service combinations become possible at runtime, agile approaches for testing the resulting systems become necessary. Besides other factors, latencies are of particular interest for the implementation of control systems. Our agile approach automatically generates dummy services, including interfaces and tasks for internal processing, based on a service description in a human-readable format. Services are then automatically distributed to the computers of our testbed, which are connected through Ethernet. We empirically obtain latency estimates for processing and communication steps for a given composition of services. In this paper we describe our data format, abstractions about internal run-time behavior of services and the code generation pipeline. The evaluation presents latency estimates that we are able to obtain through our testbed that resembles the sense-plan-act paradigm.

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.

Multi-Clusters Adaptive Brain Storm Optimization Algorithm for QoS-Aware Service Composition

Service composition provides an effective means to fulfill users' personalized requirements and complex business applications. With the number of web services rapidly growing, finding the best combination among services based on quality of service (QoS) poses a critical computational challenge due to the exponential growth of alternative composite services. Some efforts are developed to find the near-optimal service combination within an acceptable time range. They fall into two categories of solutions: exploring partial combinations in the service space and downsizing the optimization problem in scale. Although they solve the scalability problem to some extent, the required computational time is usually high. A promising direction is to integrate these two categories of solutions. However, its practical application suffers from three challenges: no good search scheme, no consideration of the natural organization of sub-problems, and the lack of diverse combinations. In this work, we propose a novel approach, called multi-clusters adaptive brain storm optimization (MCaBSO) algorithm. The proposed method uses brain storm optimization (BSO) as the search scheme to combine the division of search space with the exploration of the reduced search space. MCaBSO uses the twin support vector machine (TWSVM) to effectively divide the search space according to the natural organization of sub-problems. MCaBSO provides an adaptive dual strategy that gives guidance for the generation of diverse combinations. MCaBSO enables the agile exploration of the reduced search space and generates more high-quality combinations. MCaBSO is evaluated on two datasets to show effectiveness and efficiency.

Validating trustworthy service composition through VIPLE and pi-calculus

The current formal verification practice focuses on functionality and does not consider verification of the non-functional attributes. In this study, we propose a method in which non-functional attributes are incorporated into the logic rules of inference in terms of composition of the linear logic and pi-calculus. Giving the credibility to non-functional attributes is important, especially in the cloud computing platform and IoT environments, where trust and security are ultra-important. Such studies have not been paid much attention by researchers and practitioners. In our approach, the evolvement of the non-functional attributes is included in the process of formal verification of the service composition scheme. In addition to theoretical analysis, we applied a tool named Visual IoT/robotics Programming Language Environment (VIPLE) to execute and verify the validity of the service composition model's function. We translate the proving process of the linear logic into the corresponding pi-calculus expressions. VIPLE can translate visual work flow into pi-calculus and can verify the correctness of pi-calculus expressions.

A HRGO approach for resilience enhancement service composition and optimal selection in cloud manufacturing.

Cloud manufacturing (CM) establishes a collaborative manufacturing services chain among dispersed producers, which enables the efficient satisfaction of personalized manufacturing requirements. To further strengthen this effect, the manufacturing service composition and optimal selection (SCOS) in CM, as a NP-hard combinatorial problem, is a crucial issue. Quality of service (QoS) attributes of manufacturing services, as the basic criterion of functions and capabilities, are decisive criterions of SCOS. However, most traditional QoS attributes of CM ignore the dynamic equilibrium of manufacturing services and only rely on initial static characterizations such as reliability and availability. In a high uncertainty and dynamicity environment, a major concern is the equilibrium of manufacturing services for recovering their functions after dysfunctional damage. Therefore, this paper proposes a hybrid resilience-aware global optimization (HRGO) approach to address the SCOS problem in CM. This approach helps manufacturing demanders to acquire efficient, resilient, and satisfying manufacturing services. First, the problem description and resilience measurement method on resilience-aware SCOS is modeled. Then, a services filter strategy, based on the fuzzy similarity degree, is introduced to filter redundant and unqualified candidate services. Finally, a modified non-dominated sorting genetic algorithm (MNSGA-III) is proposed, based on diversity judgment and dualtrack parallelism, to address combination optimization step processing in SCOS. A series of experiments were conducted, the results show the proposed method is more preferable in optimal services searching and more efficient in scalability.

Self-healing of web service compositions: a specification rewriting approach

We present an approach that improves the robustness of web service compositions enabling their recovery from failures that can happen at different execution times. We first present a taxonomy of failures as an overview of previous research works on the topic of fault recovery of service compositions. The resulting classification is used to propose our self-healing method for web service compositions. The proposed method, based on the refinement process of compositions, takes user preferences into account to generate the best possible recovering compositions. In order to validate our approach, we produced a prototype implementation capable of simulating and analysing different scenarios of faults. Our work introduces algorithms for generating synthetic compositions and web services. In this setting, the recovery time, the user preference degradation and the impact of different locations of failure are investigated under different strategies, namely local, partial or total recovery. These strategies represent different levels of intervention on the composition.