Service Selection Approach Research Articles

Dynamic QoS-Aware Cloud Service Selection Using Best-Worst Method and Timeslot Weighted Satisfaction Scores

Abstract The number of cloud services has dramatically increased over the past few years. Consequently, finding a service with the most suitable quality of service (QoS) criteria matching the user’s requirements is becoming a challenging task. Although various decision-making methods have been proposed to help users to find their required cloud services, some uncertainties such as dynamic QoS variations hamper the users from employing such methods. Additionally, the current approaches use either static or average QoS values for cloud service selection and do not consider dynamic QoS variations. In this paper, we overcome this drawback by developing a broker-based approach for cloud service selection. In this approach, we use recently monitored QoS values to find a timeslot weighted satisfaction score that represents how well a service satisfies the user’s QoS requirements. The timeslot weighted satisfaction score is then used in Best-Worst Method, which is a multi-criteria decision-making method, to rank the available cloud services. The proposed approach is validated using Amazon’s Elastic Compute Cloud (EC2) cloud services performance data. The results show that the proposed approach leads to the selection of more suitable cloud services and is also efficient in terms of performance compared to the existing analytic hierarchy process-based cloud service selection approaches.

A Heterogeneous QoS-Based Cloud Service Selection Approach Using Entropy Weight and GRA-ELECTRE III

With the development of cloud computing, more and more resources are provided in the form of cloud services. Then how to select suitable cloud service for users without professional knowledge has become an important issue. Existing cloud service selection models are usually considered as QoS-aware evaluation focused models. In practice, the QoS attributes have problems like subjectivity, vagueness, and uncertainty, and a range of formats are involved to describe QoS attributes. Therefore, it is necessary to consider the heterogeneous formats of QoS attributes in cloud service selection process. The aim of this paper is to develop a novel cloud service selection approach using entropy weight and GRA-ELECTRE III that can handle heterogeneous QoS attributes simultaneously. In the proposed approach, heterogeneous QoS attributes are handled simultaneously by being transformed into intuitionistic fuzzy numbers; the relative weights of QoS attributes are calculated objectively by the extended entropy measure method under intuitionistic fuzzy environment; and cloud services are evaluated by GRA-ELECTRE III integrated method under intuitionistic fuzzy environment. Experimental results show that the proposed approach has good stability and discrimination in dealing with heterogeneous data and can effectively avoid compensation between attributes.

An optimal service selection approach for service-oriented business collaboration using crowd-based cooperative computing

Crowd-based cooperative computing (CBCC) emerges as a new computing paradigm, the core issue of which is the effective management and the coordinated use of crowd resources, including Internet users, application services, and smart devices. The service-oriented architecture (SOA) provides interoperability among crowd resources to support service-oriented business collaboration (SOBC). To address such a common issue of the coordinated use of crowd resources for SOBC, this paper studies a collaborative service computing model by considering the competition and cooperation among crowd resources. Then, a multi-objective optimization mathematical model is established for optimal service selection (OSS). Specifically, the methodology is resorted to an improved particle swarm optimization (IPSO) algorithm to find suitable collaborative services that optimally balance the quality of service (QoS) and synergy effect (SE). Furthermore, a flexible rescheduling strategy is presented for faulty services. The experimental results show that the proposed methodology is effective and feasible to obtain better-quality solutions for fulfilling the SOBC.

A Hybrid User-Centric Approach for Efficient Web Service Selection

Over the last few years, e-commerce has exhibited explosive growth due to the ease of availability of the internet. E-commerce is rapidly changing the way in which businesses are interacting with each other as well as with their consumers. In each innovative e-commerce application, web services are being included as an important component. This leads to the availability of a huge number of web services that provide similar functionalities. The main challenge is to select the appropriate web service which fulfills the end user requirements. So, there is a need for a web service selection method that selects the web services not only based on their functionality, but also considers the nonfunctional requirements. This article proposes a method to preprocess web services using the J48 classification technique. After that, a hybrid weight evaluation mechanism is employed to obtain the weight values of each nonfunctional parameter. In the end, the web services that are near to user expectations are selected out using the ranking method.

Service selection and workflow composition in modern business processes

Abstract Enterprises heavily rely on the state-of-the-art information technologies user behaviors and social system to sustain business competitiveness. Service-Oriented Architecture (SoA) and Cloud allow enterprises to utilize dynamic distributed services across enterprise boundaries and even individuals. Based on the needs of society, enterprise can spin off a virtual enterprise for new businesses where the demanded manufacturing resources are obtained through service selection and workflow composition over the Internet. An increasing number of Internet of Things (IoT) services make enterprises facing the challenge to select preferable services for their business workflows. We improve workflow compositions methods at three major aspects: (1) we present an integrated approach for service selection and composition to meet the needs of users and social system. (2) We extend Service Workflow Specification (SWSpec) as a formal requirement specification to determine a design space of services. (3) We introduce the Fuzzy Analytic Hierarchy Process (F-AHP) to evaluate non-functional specifications quantitatively with less uncertainty. Appropriate rankings can be assigned to available services based on non-functional requirements to determine the best-suited services with less uncertainty.

An Integrated MCDM Approach for Cloud Service Selection Based on TOPSIS and BWM

Cloud Computing (CC) has become increasingly popular since it provides a wide variety of customized and reliable computational services. With the rapid growth of this technology, more and more IT services providers compete to offer high-quality and cost-effective cloud services that best fulfill their customers' needs. Given the vast diversity of these offers, the choice of the most appropriate Cloud Service Provider (CSP) became a dilemma that confuses most cloud customers. Many diverged criteria have to be considered to precisely evaluate services offered by several CSPs, some of these criteria cannot be quantified easily such as usability and security. The selection of the best CSP is thus a complex Multi-Criteria Decision Making (MCDM) problem that needs to be addressed efficiently. Previous studies of this problem employed MCDM methods that are either unfeasible when it is difficult or meaningless to quantify alternatives over criteria or computationally expensive and inconsistent when relative preferences of alternatives and criteria are used instead. In this paper, we propose a novel MCDM approach that is feasible, efficient and consistent using relative preferences of criteria and alternatives. The proposed approach incorporates Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and the Best Worst Method (BWM) to rank CSPs using evaluation criteria characterizing their services. The integrated approach has been tested and validated through a use-case scenario which demonstrates its effectiveness and correctness. We have also compared the proposed approach to the most commonly used MCDM approach, Analytical Hierarchical Process (AHP). The results clearly show that the proposed approach outperforms AHP in terms of computational complexity and consistency; hence, it is more efficient and reliable than AHP.

An approach for composite service selection based on the records of request/matching

As the emergence of more and more web services, it becomes more difficult to find the candidate atomic services and composing them to form a matched service process, where not only time is strictly limited, but also user demand must be satisfied. In order to solve this problem, this paper presents a service process selection method, which is driven by historical successful service request/matching solutions. Firstly, the system framework model is analysed and several concepts about service selection and composition are formalised. Then, the method model and algorithm are detailed. Finally, through a set of experiments, the effectiveness and robustness of our approach are evaluated.

An approach for composite service selection based on the records of request/matching

As the emergence of more and more web services, it becomes more difficult to find the candidate atomic services and composing them to form a matched service process, where not only time is strictly limited, but also user demand must be satisfied. In order to solve this problem, this paper presents a service process selection method, which is driven by historical successful service request/matching solutions. Firstly, the system framework model is analysed and several concepts about service selection and composition are formalised. Then, the method model and algorithm are detailed. Finally, through a set of experiments, the effectiveness and robustness of our approach are evaluated.

ПІДХІД ДО ВИБОРУ ВЕБСЕРВІСІВ НА ОСНОВІ ЯКОСТІ З УРАХУВАННЯМ ВПОДОБАНЬ КОРИСТУВАЧА

This paper is devoted to applying the LSP method for Web service selection based on quality characteristics and the user’s preferences. The purpose of this work is to develop an approach for the Web service selection, which, based on a reasonable choice of Web service quality attributes and the use of the LSP method, allows for a deeper consideration of consumer preferences. The efficiency the LSP method for QoS-based Web service selection is conformed. The novelty of this work is that the LSP method was fully used for QoS-based Web service selection in comparison to the publications authors are aware of. Keywords: Web service, quality attributes, LSP method, aggregation framework, multicriteria decision-making method.

Time-aware selection approach for service composition based on pruning and improvement techniques

In service-oriented environments, abstract business processes can be implemented by concrete services to build complex applications. Given global user constraints, service selection allows to identify the best combination of services with respect to the business constraints. Generally, the selection problem is challenging, but it is particularly complex when dealing with QoS (quality of service) values, which can change during the time associated with temporal constraints. Indeed, these constraints make the selection problem heavily constrained, which can present a barrier for enabling effective service selection. Unlike static QoS values which have been deeply studied in the existing service selection approaches, time-dependent QoS associated with temporal constraints are insufficiently taken into consideration. Moreover, existing approaches cannot handle heavily constrained problems and usually do not provide strategies to detect the source of failure in order to enhance the selection problem in case there is no solution. In this paper, we introduce a new service selection approach, while considering time-dependent QoS values associated with temporal constraints. First, pruning techniques are proposed. The aim of the pruning process is twofold: (1) it allows for reducing the search space and thus, enhancing the efficiency of the selection process; (2) it allows for improving the selection problem by detecting at earlier stages the possible causes of failure, even before the selection process. Second, based on the pruning phase, improvement techniques are proposed to identify possible actions for finding a solution. Third, an exact and an approximate service selection algorithms under several constraints are given. Finally, we demonstrate the effectiveness of our approach through experimental results.

Service selection approach in ACT@Scale project: methodology and results

The International Journal of Integrated Care (IJIC) is an online, open-access, peer-reviewed scientific journal that publishes original articles in the field of integrated care on a continuous basis.IJIC has an Impact Factor of 5.120 (2020 JCR, received in June 2021)

Dynamic Service Selection Based on Adaptive Global QoS Constraints Decomposition

As there are more and more available Web services with the same or similar functionalities but different Quality of Service (QoS), the challenge of QoS-aware service composition is to efficiently select appropriate component services to achieve maximum utility and meet the global QoS constraints with low time cost. In this paper, we propose a dynamic service selection approach based on adaptive global QoS constraints decomposition. Fuzzy logic technology and Cultural Genetic Algorithm are used to adaptively decompose global QoS constraints into near-optimal local constraints. According to the near-optimal local constraints, the optimal service is selected for each service class during the running time efficiently. Experimental results show that the proposed approach not only achieves the near-optimal solution, but also significantly reduces the computation time, and has good adaptability and scalability.

A QoS-based approach for cloud-service matchmaking, selection and composition using the Semantic Web

PurposeCloud computing provides a dynamic, heterogeneous and elastic environment by offering accessible ‘cloud services’ to end-users. The tasks involved in making cloud services available, such as matchmaking, selection and composition, are essential and closely related to each other. Integration of these tasks is critical for optimal composition and performance of the cloud service platform. More efficient solutions could be developed by considering cloud service tasks collectively, but the research and academic community have so far only considered these tasks individually. The purpose of this paper is to propose an integrated QoS-based approach for cloud service matchmaking, selection and composition using the Semantic Web.Design/methodology/approachIn this paper, the authors propose a new approach using the Semantic Web and quality of service (QoS) model to perform cloud service matchmaking, selection and composition, to fulfil the requirements of an end user. In the Semantic Web, the authors develop cloud ontologies to provide semantic descriptions to the service provider and requester, so as to automate the cloud service tasks. This paper considers QoS parameters, such as availability, throughput, response time and cost, for quality assurance and enhanced user satisfaction.FindingsThis paper focus on the development of an integrated framework and approach for cloud service life cycle phases, such as discovery, selection and composition using QoS, to enhance user satisfaction and the Semantic Web, to achieve automation. To evaluate performance and usefulness, this paper uses a scenario based on a Healthcare Decision-Making System (HDMS). Results derived through the experiment prove that the proposed prototype performs well for the defined set of cloud-services tasks.Originality/valueAs a novel concept, our proposed integrated framework and approach for cloud service matchmaking, selection and composition based on the Semantic Web and QoS characterisitcs (availability, response time, throughput and cost), as part of the service level agreement (SLA) will help the end user to match, select and filter cloud services and integrate cloud-service providers into a multi-cloud environment.

Web Service Selection Approach Based on Agent and Fuzzy Logic

Web Service Selection Approach Based on Agent and Fuzzy Logic

Migrating two legacy systems to SOA: a new approach for service selection based on data flow diagram

There are many service identification methods (SIMs) to simplify service identification in SOA lifecycle. These SIMs vary in terms of their features (e.g., input artefact, technique). Due to this diversity, few evaluation frameworks have been proposed to guide organisations in selecting a suitable SIM based on their available input artefacts (e.g., source code, business process). This research concerns with SIMs that consider data flow diagram (DFD) as an input artefact, in order to migrate two legacy systems, modelled with DFD, to SOA. Only two SIMs are found in the literature to identify services based on DFD. However, these SIMs do not provide a way to select among the services identified to be implemented as web services. Therefore, this paper aims to bridge this gap by proposing a new approach for service selection based on DFD to assist organisations in speeding up the process of migrating their legacy systems to SOA.

Quality-Aware Service Selection for Multi-Tenant Service Oriented Systems Based on Combinatorial Auction

Multi-tenant service-oriented systems (SOSs) have become a major software engineering paradigm in the cloud environment. Instead of serving a single end-user, a multi-tenant SOS provides multiple tenants with similar and yet customized functionalities and potentially different quality-of-service (QoS) values. Multiple tenants' differentiated multi-dimensional quality constraints for the SOS further complicates the NP-hard problem of quality-aware service selection. Existing quality aware service selection approaches suffer from poor success rates of finding a solution, especially in scenarios where tenants' quality constraints are stringent, due to the lack of systematic consideration of three critical issues: 1) the need to fulfil multiple tenants' differentiated quality constraints; 2) the competition among service providers; and 3) the complementarity between services. This paper proposes a novel approach called combinatorial auction-based service selection for multi-tenant SOSs (CASSMT) to support effective quality-aware service selection for multi-tenant SOSs. CASSMT allows service providers to bid for the services of an SOS expressively. Based on received bids (i.e., QoS offers), CASSMT attempts to find a solution that achieves the system developer's optimization goal while fulfilling all tenants' quality constraints for the SOS. When no solution can be found based on the current bids, service providers can improve their bids to increase their chances of winning, which in the meantime, increases the chances of finding a solution. The experimental results show that CASSMT outperforms representative approaches in the success rate of finding a solution and system optimality. Meanwhile, its efficiency, measured by the number of auction rounds and computation time, is demonstrated to be satisfactory in scenarios on different scales.

An improved robust heteroscedastic probabilistic neural network based trust prediction approach for cloud service selection

Trustworthiness is a comprehensive quality metric which is used to assess the quality of the services in service-oriented environments. However, trust prediction of cloud services based on the multi-faceted Quality of Service (QoS) attributes is a challenging task due to the complicated and non-linear relationships between the QoS values and the corresponding trust result. Recent research works reveal the significance of Artificial Neural Network (ANN) and its variants in providing a reasonable degree of success in trust prediction problems. However, the challenges with respect to weight assignment, training time and kernel functions make ANN and its variants under continuous advancements. Hence, this work presents a novel multi-level Hypergraph Coarsening based Robust Heteroscedastic Probabilistic Neural Network (HC-RHRPNN) to predict trustworthiness of cloud services to build high-quality service applications. HC-RHRPNN employs hypergraph coarsening to identify the informative samples, which were then used to train HRPNN to improve its prediction accuracy and minimize the runtime. The performance of HC-RHRPNN was evaluated using Quality of Web Service (QWS) dataset, a public QoS dataset in terms of classifier accuracy, precision, recall, and F-Score.

A cloud services recommendation system based on Fuzzy Formal Concept Analysis

Cloud computing is an attractive paradigm which offers variant services on demand. Many available cloud services offer the same or similar functionalities, which made it challenging for cloud users to choose a suitable service that meets with their preferences. Existing service selection approaches were not enough to solve this challenge. That's why researchers went for recommendation approaches trying to find a solution. Cloud service recommendation has become an important technique for cloud services. It helps users decide whether a service satisfies their requirements or not. However, two main recommendation problems remain unsolved yet, data sparsity and cold start. In addition, existing solutions mostly tried to adapt techniques inherited from Web service and e-commerce domains. This approach is not always adequate due to many reasons such as the cloud architecture, the various service models, etc. To address the problems stated above, we propose a Collaborative Filtering based recommendation system for cloud services using Fuzzy Formal Concept Analysis (Fuzzy FCA). Fuzzy FCA has a solid mathematical foundation and it's based on the lattice theory. The lattice representation will give an explicit description of our cloud environment (users, services, ratings, etc.) and, then, extract the pertinent information from it (similar users to an active user, ratings of each similar user, top services, etc.) which will make the recommendations more suitable. Experimental results confirmed our expectations and proved the efficiency of such an approach.

How to deal with QoS value constraints in MCDM based Web service selection

SummaryThe aim of the current research is to develop a more accurate Web service selection approach that can deal with value constraints on QoS criteria in the user request. The purpose is to be able to promote services even when all the value constraints are not satisfied and hence rank them according to their closeness to meet the latter. For this effect, we use MCDM (Multi Criteria Decision making) methods together with suitable normalization techniques at different stages of the process. First of all, we consider an extension of the AHP method to compute and normalize the weights associated with the QoS criteria considered in the Web service selection. Second, we introduce a more consistent normalization technique, called OMRI, to normalize data according to value constraints. Third, we propose to extend different ranking MCDM methods to the OMRI normalization so that it becomes possible for them to cope with value constraints. The considered methods are SAW, TOPSIS, VIKOR, and WPM. To compare the accuracy of the extended ranking methods, we use the Borda compromise solution together with different similarity ratios. To validate the solution, several experiments have been conducted on a real dataset as well as on an artificial one.

A trust centric optimal service ranking approach for cloud service selection

Cloud service selection, a promising research directive provides an intelligent solution via. service ranking based on the Quality of Service (QoS) attributes for the identification of trustworthy Cloud Service Providers (CSPs) among a wide range of functionally-equivalent CSPs. Further, the impact of objective and subjective assessment data on the accuracy of the service selection model makes the credibility of the assessment data, a major concern for the researchers in service-oriented environments. To address the challenges with respect to the identification of the user requirement compliant CSPs, data credibility, service ranking, etc. we present Hypergraph –Binary Fruit Fly Optimization based service ranking Algorithm (HBFFOA), a trust-centric approach for the identification of suitable and trustworthy cloud service providers. HBFFOA employs hypergraph partitioning, time-varying mapping function, helly property, and binary fruit fly optimization algorithm for the identification of similar service providers, credibility based trust assessment, selection of trustworthy service providers, and optimal service ranking respectively. Experiments using synthetic QoS dataset from WSDream#2 illustrates the effectiveness, practicability, scalability and computational attractiveness of HBFFOA over the existing service selection approaches in terms of precision, stability, statistical test, and time complexity analysis.