Existing Service Selection Approaches Research Articles

Toward the efficient service selection approaches in cloud computing

PurposeThis paper follows a systematic literature review (SLR) method covering the published studies until March 2021. The authors have extracted the related studies from different online databases utilizing quality-assessment-criteria. In order to review high-quality studies, 32 papers have been chosen through the paper selection process. The selected papers have been categorized into three main groups, decision-making methods (17 papers), meta-heuristic methods (8 papers) and fuzzy-based methods (7 papers). The existing methods in each group have been examined based on important qualitative parameters, namely, time, cost, scalability, efficiency, availability and reliability.Design/methodology/approachCloud computing is known as one of the superior technologies to perform large-scale and complex computing. With the growing tendency of network service users to utilize cloud computing, web service providers are encouraged to provide services with various functional and non-functional features and supply them in a service pool. In this regard, choosing the most appropriate services to fulfill users' requirements becomes a challenging problem. Since the problem of service selection in a cloud environment is known as a nondeterministic polynomial time (NP)-hard problem, many efforts have been made in recent years. Therefore, this paper aims to study and assess the existing service selection approaches in cloud computing.FindingsThe obtained results indicate that in decision-making methods, the assignment of proper weights to the criteria has a high impact on service ranking accuracy. Also, since service selection in cloud computing is known as an NP-hard problem, utilizing meta-heuristic algorithms to solve this problem offers interesting advantages compared to other approaches in discovering better solutions with less computational effort and moving quickly toward very good solutions. On the other hand, since fuzzy-based service selection approaches offer search results visually and cover quality of service (QoS) requirements of users, this kind of method is able to facilitate enhanced user experience.Research limitations/implicationsAlthough the current paper aimed to provide a comprehensive study, there were some limitations. Since the authors have applied some filters to select the studies, some effective works may have been ignored. Generally, this paper has focused on journal papers and some effective works published in conferences. Moreover, the works published in non-English formats have been excluded. To discover relevant studies, the authors have chosen Google Scholar as a popular electronic database. Although Google Scholar can offer the most valid approaches, some suitable papers may not be observed during the process of article selection.Practical implicationsThe outcome of the current paper will be useful and valuable for scholars, and it can be a roadmap to help future researchers enrich and improve their innovations. By assessing the recent efforts in service selection in cloud computing and offering an up-to-date comparison of the discussed works, this paper can be a solid foundation for understanding the different aspects of service selection.Originality/valueAlthough service selection approaches have essential impacts on cloud computing, there is still a lack of a detailed and comprehensive study about reviewing and assessing existing mechanisms in this field. Therefore, the current paper adopts a systematic method to cover this gap. The obtained results in this paper can help the researchers interested in the field of service selection. Generally, the authors have aimed to specify existing challenges, characterize the efficient efforts and suggest some directions for upcoming studies.

SaaSRec+: a new context-aware recommendation method for SaaS services.

Cloud computing is an attractive model that provides users with a variety of services. Thus, the number of cloud services on the market is growing rapidly. Therefore, choosing the proper cloud service is an important challenge. Another major challenge is the availability of diverse cloud services with similar performance, which makes it difficult for users to choose the cloud service that suits their needs. Therefore, the existing service selection approaches is not able to solve the problem, and cloud service recommendation has become an essential and important need. In this paper, we present a new way for context-aware cloud service recommendation. Our proposed method seeks to solve the weakness in user clustering, which itself is due to reasons such as 1) lack of full use of contextual information such as cloud service placement, and 2) inaccurate method of determining the similarity of two vectors. The evaluation conducted by the WSDream dataset indicates a reduction in the cloud service recommendation process error rate. The volume of data used in the evaluation of this paper is 5 times that of the basic method. Also, according to the T-test, the service recommendation performance in the proposed method is significant.

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.

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.

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.

An influence diagram based cloud service selection approach in dynamic cloud marketplaces

Cloud service selection is one of the key issues for service users. An important concern for this issue is to make an optimal policy that adapt to a dynamic cloud marketplace that different types of changes may happen during a service consumption process, for example, the change of the cloud service providers and the provisioned cloud services in this marketplace, the change of the requirements of service users on service performance, e.g. Quality of services (QoSs) and service functions, and the change of the termination time points of a consumed service. These changes require a dynamic modelling method for cloud service selection, while existing service selection approaches rarely consider such dynamicity. Therefore, we propose a novel Cloud service selection framework based on Markov decision processes (MDPs), which can help Cloud service users to select a set of services meeting the QoS requirements and the economic constraints of service users. The MDP, as one of the primary decision theoretic planning tools, is capable of formalizing the uncertainties in the dynamic marketplace, and making service selection policies to achieve the best trade-off between costs and benefits. We use the causal-mapping approach to construct the structure of influence diagrams, and a Gauss kernel estimation method to estimate the marginal distributions of QoSs. Our experiments are based on simulated scenarios and real datasets. The experimental results show that the proposed framework is capable of accurately capturing the features of QoS values, predicting the QoS performance, and efficiently adapting to the changes in a long-term service consumption to facilitate policy determinations in a dynamic marketplace.

A genetic algorithm based approach to transactional and QoS-aware service selection

As there are various risks of failure when Web services are deployed in unreliable environment, the execution of a composite Web service (CWS) requires the transaction mechanism to guarantee its reliable execution. However, the existing service selection approaches consider QoS and transaction separately and have not considered that transactional properties may affect the QoS such as the execution time of a CWS. This work addresses the importance of considering transactional properties and how the transactional properties affect the QoS simultaneously in the process of service selection by a QoS-aware and transactional-aware selection approach. First, a performance evaluation method is proposed to calculate the execution time of a transactional CWS. Then, a genetic algorithm based approach, which takes into account the execution time, price, transactional property and successful execution rate of CWS, is presented to achieve global optimisation service selection. Finally, experimental results show the efficiency and effectiveness of the approach.

Multi-attribute optimization in service selection

As multiple service providers may compete to offer the same functionality with different quality of service (e.g., latency, fee, and reputation), a key issue in service computing is selecting service providers with the best user desired quality. Existing service selection approaches mostly rely on computing a predefined objective function. When multiple quality criteria are considered, users are required to express their preference over different (and sometimes conflicting) quality attributes as numeric weights. This is a rather demanding task and an imprecise specification of the weights could miss user desired services. We propose a multi-attribute optimization approach to tackle this issue. In particular, we develop a novel concept, called service skyline, and a set of service skyline computation techniques that return a set of most interesting service providers. These providers are non-dominant in all user interested quality attributes. Thus, the service skyline ensures that the user desired providers will be included. Analytical and experimental studies justify the performance of the proposed techniques. The relative small sizes of the service skylines also make it practical for service users to make selections from them.