QoS-aware Web Service Composition Research Articles

An innovative approach for QoS-aware web service composition using whale optimization algorithm

With the proliferation of services and the vast amount of data produced by the Internet, numerous services with comparable functionalities but varying Quality of Service (QoS) attributes are potential candidates for meeting user needs. Consequently, the selection of the most suitable services has become increasingly challenging. To address this issue, a synthesis of multiple services is conducted through a composition process to create more sophisticated services. In recent years, there has been a growing interest in QoS uncertainty, given its potential impact on determining an optimal composite service, where each service is characterized by multiple QoS properties (e.g., response time and cost) that are frequently subject to change primarily due to environmental factors. Here, we introduce a novel approach that depends on the Multi-Agent Whale Optimization Algorithm (MA-WOA) for web service composition problem. Our proposed algorithm utilizes a multi-agent system for the representation and control of potential services, utilizing MA-WOA to identify the optimal composition that meets the user’s requirements. It accounts for multiple quality factors and employs a weighted aggregation function to combine them into a cohesive fitness function. The efficiency of the suggested method is evaluated using a real and artificial web service composition dataset (comprising a total of 52,000 web services), with results indicating its superiority over other state-of-the-art methods in terms of composition quality and computational effectiveness. Therefore, the proposed strategy presents a feasible and effective solution to the web service composition challenge, representing a significant advancement in the field of service-oriented computing.

Multi-Objective Service Composition in Uncertain Environments

Web services have the potential to offer the enterprises with the ability to compose internal and external business services in order to accomplish complex processes. Service composition then becomes an increasingly challenging issue when complex and critical applications are built upon services with different QoS criteria. However, most of the existing QoS-aware service composition techniques are simply based on the assumption that multiple QoS criteria, no matter whether these multiple criteria are conflicting or not, can be combined into a single criterion to be optimized, according to some utility functions. In practice, this can be very difficult as these utility functions or weights are not well-known a priori. In addition, the existing approaches are designed to work in certain environments, where the QoS parameters are well-known in advance. These approaches will render fruitless when facing uncertain and dynamic environments, e.g., cloud environments, where no prior knowledge of the QoS parameters is available. In this paper, two novel multi-objective approaches are proposed to handle QoS-aware Web service composition with conflicting objectives and various restrictions on the quality matrices. The proposed approaches use reinforcement learning in order to deal with the uncertainty characteristics inherent in open and dynamic environments. Experimental results reveal the ability of the proposed approaches to find a set of Pareto optimal solutions, which have the equivalent quality to satisfy multiple QoS-objectives with different user preferences.

Cloud Service Selection System Approach based on QoS Model: A Systematic Review

The Internet of Things (IoT) has received a lot of interest from researchers recently. IoT is seen as a component of the Internet of Things, which will include billions of intelligent, talkative "things" in the coming decades. IoT is a diverse, multi-layer, wide-area network composed of a number of network links. The detection of services and on-demand supply are difficult in such networks, which are comprised of a variety of resource-limited devices. The growth of service computing-related fields will be aided by the development of new IoT services. Therefore, Cloud service composition provides significant services by integrating the single services. Because of the fast spread of cloud services and their different Quality of Service (QoS), identifying necessary tasks and putting together a service model that includes specific performance assurances has become a major technological problem that has caused widespread concern. Various strategies are used in the composition of services i.e., Clustering, Fuzzy, Deep Learning, Particle Swarm Optimization, Cuckoo Search Algorithm and so on. Researchers have made significant efforts in this field, and computational intelligence approaches are thought to be useful in tackling such challenges. Even though, no systematic research on this topic has been done with specific attention to computational intelligence. Therefore, this publication provides a thorough overview of QoS-aware web service composition, with QoS models and approaches to finding future aspects.

Coordinate System-Based Trust-Aware Web Services Composition in Edge and Cloud Environment

Abstract In the next few years, the number of devices connected to the web will increase dramatically. This has encouraged the development of complexes and intelligent service-based applications using new paradigms and technologies such as edge computing and cloud computing. These applications are mainly built by the interaction between heterogonous web services. Answering end-users’ queries demands that these services collect, integrate and communicate sensitive data, which makes the vulnerability of data a serious issue. Thereby, this issue is a critical problem in the automatic services composition while users are frequently unquieted about the security and control of their sensitive data. To address this problem, our main idea is that avoiding disclosure of sensitive information can be ensured by selecting services where their mutual trust is high while preserving a good quality of service (QoS) as possible. A new Heuristic Trust-aware Web Services Composition Approach (CWS_SMA) is proposed. CWS_SMA aims to: (i) compute an optimal trust and QoS-aware web services composition based on the mathematical coordinate system, (ii) improve the composition response time based on a set of cooperative intelligent agents. Eventually, the simulations results show the effectiveness of the proposed solution.

Automatic QoS-aware web services composition based on set-cover problem

By definition, web-services composition works on developing merely optimum coordination among a number of available web-services to provide a new composed web-service intended to satisfy some users requirements for which a single web service is not (good) enough. In this article, the formulation of the automatic web-services composition is proposed as several set-cover problems and an approximation algorithm has been exploited to solve them. In proposed method, the web-service composition has been carried out within two main phases, the top-down expansion of the composition tree, and the production of composed service by bottom-up traversal of composition tree. In the first phase, the production of a composition tree (similar to the production of tree in problemsolving by searching) is proposed by starting from the output or post-conditions of the requested service towards its input or pre-conditions. Each node or state of the tree is a set of inputs and/or outputs or conditions, and services as tree edges illustrate the transition from one node to another. In the second phase, finding the path from the leaves of the produced composition tree to the root is considered equal to reaching the output of requested service, and this path specifies the involved services and the composition plan. The requested service input set determines the available leaves of the composition tree. To achieve each non-leaf node of the tree, a set-cover problem is produced and solved using a greedy approximation algorithm. If the production and solving of the set-cover problems continues hierarchically until it reaches the root node, the composition plan and cost of the required composition service will be specified. The main focus of this research is the joint sequential and parallel composition with the aim of producing near-optimal and QoS-aware composed services.

Memetic Harris Hawks Optimization: Developments and perspectives on project scheduling and QoS-aware web service composition

Harris hawks optimization (HHO) is one of the leading optimization approaches due to its efficacy and multi-choice structure with time-varying components. The HHO has been applied in various areas due to its simplicity and outstanding performance. However,the original HHO can be improved and evolved in terms of convergence trends, and it is prone to local optimization under certain circumstances. Therefore, the performance and robustness of the algorithm need to be further improved. In our research, based on the core principle of evolutionary methods, we first developed an elite evolutionary strategy (EES) and then utilized it to advance HHO’s convergence speed and ability to jump out of the local optimum. We named such an enhanced hybrid algorithm EESHHO in this paper. To verify the effectiveness and robustness of the EESHHO, we tested it on 29 numerical optimization test functions, including 23 classic basic test functions and 6 composite test functions from the IEEE CEC2017 special session. Moreover, we apply the EESHHO on resource-constrained project scheduling and QoS-aware web service composition problems to further validate the effectiveness of EESHHO. The experimental results show that proposed EESHHO has faster convergence speed and better optimization performance by comparing it with other mainstream algorithms. The supplementary info and answers to possible queries will be publicly available at https://aliasgharheidari.com/publications/EESHHO.html. Also, the codes and info of HHO are available at: https://aliasgharheidari.com/HHO.html.

QoS 기반 웹서비스 컴포지션을 위한 효율적인 리플래닝 기법

A Web service is a service provided by software systems to support machine to machine inter-operations over Web. Given a set of Web services and a goal, the QoS-aware Web service composition problem constructs a composite Web service with the optimal accumulated QoS value, which satisfies the given goal specification. However, in the real world environment, frequent changes happen inherently - for instance, temporary machine down, heavy system workload, and network failure. If the solution we have constructed is not valid anymore due to the changes, we have to solve a new problem again. Ordinary approaches to find a new solution from scratch may waste computation time, since they explore the whole searching space again. Hence, in this paper, we propose a novel replanning algorithm, which does not solve the new composition problem from scratch but explores only the changed space to identify the new optimal solution. In the experiment, our replanning algorithm can deal with the composition problem much faster than the original algorithm to solve from scratch.

Evaluation of EHO Algorithm’s in solving various real-life problems

Abstract: This paper, presents a new swarm-based metaheuristic search algorithm, known as Elephant Herding Optimization (EHO), which is proposed for solving various daily real-life problems such as benchmark problems, Service Selection in QoS-Aware Web Service Composition, Energy-Based Localization, PID controller tuning, Appliance Scheduling in Smart Grid identification and other problems. The EHO method is inspired by the herding behavior of elephant group. In nature, elephants live in clans under the leadership of a matriarch (female elephant), while the male elephants separate from their family when they grow up. These two behaviors are used by EHO as two operators: clan updating operator and separating operator, which will be used in the optimizing process. Elephant Herding Optimization (EHO) is used to solve NP-Hard problems.

QoS-aware Automatic Web Service Composition with Multiple Objectives

Automatic web service composition has received a significant research attention in service-oriented computing over decades of research. With increasing number of web services, providing an end-to-end Quality of Service (QoS) guarantee in responding to user queries is becoming an important concern. Multiple QoS parameters (e.g., response time, latency, throughput, reliability, availability, success rate) are associated with a service, thereby, service composition with a large number of candidate services is a challenging multi-objective optimization problem. In this article, we study the multi-constrained multi-objective QoS-aware web service composition problem and propose three different approaches to solve the same, one optimal, based on Pareto front construction, and two others based on heuristically traversing the solution space. We compare the performance of the heuristics against the optimal and show the effectiveness of our proposals over other classical approaches for the same problem setting, with experiments on WSC-2009 and ICEBE-2005 datasets.

Simulation and modeling of an improved multi-verse optimization algorithm for QoS-aware web service composition with service level agreements in the cloud environments

Cloud computing is a new trend in information technology that provides resources and shares services by web services over the internet for users. Many web services have similar functionality with different quality of service (QoS) in the cloud. Thus, an appropriate service composition method is needed to assign an optimal composition of services to the users. Furthermore, service level agreement (SLA) which is the level of service expected from the service provider should be satisfied by the service composition method that less considered in previous studies. Introducing an appropriate algorithm for web service composition that could achieve high QoS while satisfying SLA constrains is one of the main issues in the cloud computing area. In this study, an improved multi-verse optimization algorithm for web service composition that is called IMVO algorithm is proposed to improve QoS while satisfying SLA. The simulation results show increasing of normalized QoS up to 57% in comparison with the other approaches, especially for service composition problems with SLA.

A Meta-Heuristic-Based Approach for Qos-Aware Service Composition

Recently, with the rapid increase in the number of web services, QoS-aware Web Service Composition(QWSC) has become a popular topic in both industry and academia. Meta-heuristic algorithm, as an effective way to solve classical optimization problems, has been successfully applied to QWSC nowdays. However, such approach has intrinsic drawbacks and usually lack of good performance in large-scale scenarios. For example, some meta-heuristic algorithms are suitable for continuous search space, while the search space of QWSC is discrete. For solving those problems which were commonly faced when applying meta-heuristic algorithm on QWSC, in this research, we firstly introduce a preprocessing approach for constructing fuzzy continuous neighborhood relations of concrete services, which makes the local search strategy of meta-heuristic algorithms be as effective in discrete space as in continuous space, thus improving the optimization performance. Second, we combine Harris Hawks Optimization (HHO) algorithm and logical chaotic sequence to propose an improved meta-heuristic algorithm named CHHO for solving QWSC. The ergodic and chaotic characteristics of chaotic sequences are used to enhance the ability of the CHHO to jump out of the local optimum for further optimization. Experimental results show that the CHHO has better optimization performance by comparing with the existing mainstream algorithms when solving QWSC problems. Additionally, the preprocessing approach not only greatly improves the optimization performance of the CHHO but also can be freely utilized in other meta-heuristics based approaches.

Advances on QoS‐aware web service selection and composition with nature‐inspired computing

Service-oriented architecture is becoming a major software framework for complex application and it can be dynamically and flexibly composed by integrating existing component web services provided by different providers with standard protocols. The rapid introduction of new web services into a dynamic business environment can adversely affect the service quality and user satisfaction. Therefore, how to leverage, aggregate and make use of individual component's quality of service (QoS) information to derive the optimal QoS of the composite service which meets the needs of users is still an ongoing hot research problem. This study aims at reviewing the advance of the current state-of-the-art in technologies and inspiring the possible new ideas for web service selection and composition, especially with nature-inspired computing approaches. Firstly, the background knowledge of web services is presented. Secondly, various nature-inspired web selection and composition approaches are systematically reviewed and analysed for QoS-aware web services. Finally, challenges, remarks and discussions about QoS-aware web service composition are presented.

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

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

Multi-objective and discrete Elephants Herding Optimization algorithm for QoS aware web service composition

The goal of QoS aware web service composition (QoS-WSC) is to provide new functionalities and find a best combination of services to meet complex needs of users. QoS of the resulting composite service should be optimized. QoS-WSC is a global multi-objective optimization problem belonging to NP-hard class given the number of available services. Most of existing approaches reduce this problem to a single-objective problem by aggregating different objectives, which leads to a loss of information. An alternative issue is to use Pareto-based approaches. The Pareto-optimal set contains solutions that ensure the best trade-off between conflicting objectives. In this paper, a new multi-objective meta-heuristic bio-inspired Pareto-based approach is presented to address the QoS-WSC, it is based on Elephants Herding Optimization (EHO) algorithm. EHO is characterised by a strategy of dividing and combining the population to sub population (clan) which allows exchange of information between local searches to get a global optimum. However, the application of others evolutionary algorithms to this problem cannot avoids the early stagnancy in a local optimum. In this paper a discrete and multi-objective version of EHO will be presented based on a crossover operator. Compared with SPEA2 (Strength Pareto Evolutionary Algorithm 2) and MOPSO (Multi-Objective Particle Swarm Optimization algorithm), the results of experimental evaluation show that our improvements significantly outperform the existing algorithms in term of Hypervolume, Set Coverage and Spacing metrics.

Towards the optimality of QoS-aware web service composition with uncertainty

Quality of service (QoS)-aware web service composition (QWSC) has recently become one of the most challenging research issues. Although much work has been investigated, they mainly focus on certain QoS of web services, while QoS with uncertainty exposes the most important characteristic in real and highly dynamic environment. In this paper, with the consideration of uncertain service QoS and user's preferences, we model the issue of uncertain QoS-aware WSC via interval number and translate it into a multi-objective optimisation problem with global QoS constraints of user's preferences. The encoded optimisation problem is solved by an non-deterministic multi-objective evolutionary algorithm, which exploits new genetic encoding schema, the strategy of crossover and uncertain interval Pareto comparison. To validate the feasibility, large-scale experiments have been conducted on simulated datasets. The results demonstrate that our proposed approach can effectively and efficiently find optimum composite service solutions set with satisfactory convergence.

Towards the optimality of QoS-aware web service composition with uncertainty

Quality of service (QoS)-aware web service composition (QWSC) has recently become one of the most challenging research issues. Although much work has been investigated, they mainly focus on certain QoS of web services, while QoS with uncertainty exposes the most important characteristic in real and highly dynamic environment. In this paper, with the consideration of uncertain service QoS and user's preferences, we model the issue of uncertain QoS-aware WSC via interval number and translate it into a multi-objective optimisation problem with global QoS constraints of user's preferences. The encoded optimisation problem is solved by an non-deterministic multi-objective evolutionary algorithm, which exploits new genetic encoding schema, the strategy of crossover and uncertain interval Pareto comparison. To validate the feasibility, large-scale experiments have been conducted on simulated datasets. The results demonstrate that our proposed approach can effectively and efficiently find optimum composite service solutions set with satisfactory convergence.

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.

Integrating modified cuckoo algorithm and creditability evaluation for QoS-aware service composition

QoS-aware Web service composition is regarded as one of the fundamental issues in service computing. Given the open and dynamic internet environment, which lacks a central control of individual service providers, we propose in this paper a novel method that seamlessly considers Quality of Service (QoS) and credibility of service providers to achieve optimal service compositions. Instead of using creditability as one of the QoS attributes, we treat it as the overall capability of a service provider to deliver its promised QoS. We aggregate both user experience (i.e., user trust) and track record (i.e., service reputation) of a provider for accurate creditability evaluation. To facilitate user decision making when multiple (and sometimes conflicting) QoS attributes are involved, we develop an automatic weight calculation approach based on rough set theory and a fuzzy analytic hierarchy process, which assigns higher weights to the more discriminative attributes. Finally, to achieve an optimal service composition, a two phase optimization process is employed, where local optimization chooses services based on creditable QoS assessment and global optimization tackles a multi-objective problem using an effective cuckoo search algorithm. Extensive experimental results show that the proposed QoS-aware service composition approach achieves desirable QoS with credibility guarantees. The performance of our proposed approach also significantly outperforms other competitive methods.

GACRM: A dynamic multi-Attribute decision making approach to large-Scale Web service composition

The problem of QoS-aware Web service composition (QWSC), i.e., how to select from a pool of candidate services to construct a composite service with the best overall QoS performance, is an NP-hard problem. To address a large-scale QWSC problem, a novel method is proposed based on information theory, multi-attribute decision making (MADM) and genetic algorithm. To capture complex judgments, the QWSC problem is formulated into a MADM representation which aims to find acceptable solutions assessed by multiple QoS attributes with varying distributions. To solve the MADM problem for QWSC, each QoS attribute is weighted in both a priori, subjective perspective and a posteriori, information-based perspective based on the discriminative capability of QoS attributes for a dynamic pool of candidate services. Furthermore, to solve the large-scale QWSC problem that conventional MADM methods cannot navigate, we develop a GACRM algorithm by integrating genetic algorithm (GA) with Compromise Ratio Method (CRM). Experiments demonstrate that GACRM obtains nearly the same solution ranking by the CRM but scales much better in terms of computation time for large-scale QWSC problems.

Computational Intelligence Based QoS-Aware Web Service Composition: A Systematic Literature Review

Web service composition concerns the building of new value added services by integrating the sets of existing web services. Due to the seamless proliferation of web services, it becomes difficult to find a suitable web service that satisfies the requirements of users during web service composition. This paper systematically reviews existing research on QoS-aware web service composition using computational intelligence techniques (published between 2005 and 2015). This paper develops a classification of research approaches on computational intelligence based QoS-aware web service composition and describes future research directions in this area. In particular, the results of this study confirms that new meta-heuristic algorithms have not yet been applied for solving QoS-aware web services composition.