Service Selection Problem Research Articles

SLA-constrained service selection for minimizing costs of providing composite cloud services under stochastic runtime performance.

Composite cloud services based on the methodologies of Software as a Service and Service-Oriented Architecture are transforming how people develop and use software. Cloud service providers are confronting the service selection problem when composing composite cloud services. This paper deals with an important type of service selection problem, minimizing the total cost of providing a composite cloud service with respect to the constraints of service level agreement (SLA). Two types of SLA are considered in the study: per-request-based SLA and ratio-based SLA. We present three service selection approaches for dynamic cloud environments where services’ performance might vary with time. The first two are iterative compound approaches for per-request-based SLA and the third approach is a one-step method for ratio-based SLA based on the Chebyshev’s theorem and nonlinear programming. Experimental results show that our approaches outperform the previous method significantly in terms of total cost reduction.

BAT and Hybrid BAT Meta-Heuristic for Quality of Service-Based Web Service Selection

AbstractEfficient QoS-based service selection from a pool of functionally substitutable web services (WS) for constructing composite WS is important for an efficient business process. Service composition based on diverse QoS requirements is a multi-objective optimization problem. Meta-heuristic techniques such as genetic algorithm (GA), particle swarm optimization (PSO), and variants of PSO have been extensively used for solving multi-objective optimization problems. The efficiency of any such meta-heuristic techniques lies with their rate of convergence and execution time. This article evaluates the efficiency of BAT and Hybrid BAT algorithms against the existing GA and Discrete PSO techniques in the context of service selection problems. The proposed algorithms are tested on the QWS data set to select the best fit services in terms of maximum aggregated end-to-end QoS parameters. Hybrid BAT is found to be efficient for service composition.

Two-stage approach for reliable dynamic Web service composition

Web service composition is a key technology for creating value-added services by integrating available services. With the rapid development of Service Computing, Cloud Computing, Big Data, and the Internet of Things, mass services with the same functionalities and different quality of service (QoS) values are available on the Internet. Moreover, due to the uncertainties of services’ application environment, a service’s QoS is highly dynamic; these two factors cause reliable dynamic Web service composition to be a challenge. To address this issue, this paper proposes a two-stage approach for reliable dynamic Web service composition. In the first stage, the top K Web service composition schemes based on each service’s historical QoS values are selected with the proposed algorithm named Culture Genetic Algorithm (CGA). Then, component services in the top K schemes are filtered out and employed as the candidate services for dynamic service composition. This operation can greatly reduce the number of available services and filter out better candidate services for dynamic service composition. Next, the global QoS constraints are decomposed into local QoS constraints with the CGA algorithm, and the global optimal problem of service composition is transformed into a local optimal service selection problem; this conversion increases the flexibility of dynamic service composition and provides a chance to predict QoS values of services before service selection. In the second stage, before selecting the best service for each task during the running of the service composition workflow, QoS values of each candidate service are predicted based on the improved case-based reasoning, and the best service is selected according to the predicted QoS values. Through QoS prediction, the reliability of the composite Web service can be greatly enhanced. Finally, experimental results show that the proposed method is feasible and effective.

QoS-Aware Mobile Service Selection Algorithm

For the problem of mobile service selection, this paper gives a context-aware service selection algorithm based on Genetic Algorithm. In this algorithm, a tree encoding method, a fitness function, and a fitness-better strategy were proposed. The tree encoding mode made Genetic Algorithm support selection of various types of service combinations, for example, sequence composition, concurrence composition, probability composition, and loop composition. According to the encoding method, a fitness function was designed specially. The fitness-better strategy gives the direction of population evolution and avoids the degradation of population fitness. Some experiments analyses show that the provided service selection algorithm can get better service composition.

Using Service Clustering and Self-Adaptive MOPSO-CD for QoS-Aware Cloud Service Selection

A promising way to effectively manage the composition of services in a heterogeneous and dynamic environment is to make workflow management able to self-adapt at runtime to react to changes in its environment by autonomously reconfiguring itself. Most of the proposed methodologies address this issue as a QoS-aware service selection problem in which a web service broker can dynamically select the “right” service that takes part in the composition, and adaptively change the bound service when the delivered QoS has changed.In this paper, we propose a self-organizing framework that uses a service clustering based discovery approach to effectively and efficiently support the selection of services in which runtime changes in the QoS of the services are taken into account. Two bio-inspired algorithms are designed to support a QoS-aware dynamic service selection mechanism. An ant-based clustering algorithm enhanced with a template mechanism that guides the artificial ants to move data items to construct and maintain a specific topology is adopted as a method for efficient service discovery. As a consequence, services can dynamically be discovered in a shorter time and with lower network traffic. To select the actual concrete services that best meet the user QoS requirements a Self-Adaptive Multi-Objective Particle Swarm Optimization Algorithm using crowding distance technique (MOPSO-CD) is executed using the topological map generated by the ant algorithm. In the end, simulation results show the effectiveness of the method proposed.

A Cloud Service Selection Model based on Analytic Network Process

A cloud computing service is the next-generation core service of green ICT for operational management of ICT resources and maximization of energy consumption efficiency. Cloud service is expected to rapidly increase in its market size in the future through its convenience in usage, efficiency of operational management and economic effects and so on. In users’ perspectives, the choice of the appropriative cloud service is very important to meet their business goals and strategies. A cloud service selection problem is a multiple-criteria decision analysis with feedback. An Analytical Network Process (ANP) is a popular decision analysis technique. This study proposes a cloud service selection model based on ANP. According to the proposed model, the criteria and sub-factors for cloud service selection are identified and then determined their weights. In this study, ANP based the multiple criteria decision-making model is applied to choose the best cloud services.

QoS-based service selection with lightweight description for large-scale service-oriented internet of things

Quality of Service (QoS)-based service selection is the key to large-scale service-oriented Internet of Things (IOT), due to the increasing emergence of massive services with various QoS. Current methods either have low selection accuracy or are highly time-consuming (e.g., exponential time complexity), neither of which are desirable in large-scale IOT applications. We investigate a QoS-based service selection method to solve this problem. The main challenges are that we need to not only improve the selection accuracy but also decrease the time complexity to make them suitable for large-scale IOT applications. We address these challenges with the following three basic ideas. First, we present a lightweight description method to describe the QoS, dramatically decreasing the time complexity of service selection. Further more, based on this QoS description, we decompose the complex problem of QoS-based service selection into a simple and basic sub-problem. Finally, based on this problem decomposition, we present a QoS-based service matching algorithm, which greatly improves selection accuracy by considering the whole meaning of the predicates. The traces-driven simulations show that our method can increase the matching precision by 69% and the recall rate by 20% in comparison with current methods. Moreover, theoretical analysis illustrates that our method has polynomial time complexity, i.e., O(m 2 × n), where m and n denote the number of predicates and services, respectively.

A Hybrid Multi-Criteria Decision-Making Model for a Cloud Service Selection Problem Using BSC, Fuzzy Delphi Method and Fuzzy AHP

In rapid changing the global business environment, information communication technology (ICT) is essential for the survival of a firm, and the functions of ICT is becoming increasingly important. The emergence of cloud computing represents a fundamental change of ICT services and cloud services continue to grow rapidly with increasing functionality and more users. As a result of this growth, it is a critical issue to select a suitable cloud service which meets all the business strategies and the objectives of firms. This paper proposes a hybrid multi-criteria decision-making model for a cloud service selection problem using balanced scorecard (BSC), fuzzy Delphi method (FDM) and fuzzy analytical hierarchy process (FAHP). We focus on selecting an IaaS among cloud services for firms' users. The BSC concept is applied to define the hierarchy with four major perspectives (i.e. financial, customer, internal business process, and learning and growth), and to derive decision-making criteria and decision-making factors are selected for each BSC perspective. FDM is used to select the list of important decision-making factors within each BSC perspective based on the decision makers' opinion. A FAHP approach is then proposed in order to compares the decision-making criteria and factors and determine the importance of them. It is also used to select the best cloud service among the cloud service alternatives based on the predetermined weights of decision-making criteria and factors. In this study, the BSC and FAHP as the hybrid multi-criteria decision-making technique are used to select the best cloud service. Our findings can be utilized as bases to apply systematic decision-making processes for the best cloud service selection and for providing guidance to IT department managers or CTO regarding performance evaluation and strategies to improve companies' performance and capability.

A Hierarchical Multilayer Service Composition Model for Global Virtual Organizations

A major benefit of service composition is the ability to support agile global collaborative virtual organizations. However, being global in nature, collaborative virtual organizations can have several virtual industry clusters (VIC), where each VIC has hundreds to thousands of virtual enterprises that provide functionally similar services exposed as web services. These web services can be differentiated on a high dimensionality of quality of service attributes. The dilemma the virtual enterprise broker is faced with is how to dynamically select the best combination of component services to fulfill a complex consumer need within the shortest time possible. This composite service selection problem remains a Multi-Criteria Decision Making (MCDM) NP hard problem. Although existing MCDM methods based on local planning are linearly scalable for large problems, they lack capabilities to express critical intertask constraints that are practically relevant to service consumers. MCDM global planning methods on the other hand suffer exponential state space explosion making them severely limited for large problems of industrial relevance. This paper proposes HMSCM: Hierarchical Multi-Layer Service Composition Model. HMSCM is based on the theory of Layering as Optimization Decomposition (28-31). We view the service selection process as a layer network where each layer is a subproblem to be solved. The objective of one of the layers is to maximize a local utility function over a subset of web service QoS attributes from a service consumer perspective. The objective of the other layer is to maximize a local utility function over another subset of web service QoS attributes from the perspective of the Virtual enterprise broker. We develop the algorithm: Service Layered Utility Maximization (SLUM) that extends the Mixed Integer programming model in (9). We then formulate the problem at each layer in form of SLUM. Together, the two layers attempt to achieve the global optimization objective of the network. We show analytically how HMSCM overcomes the shortcomings of existing local planning and global planning service selection methods while retaining the strengths from each. i.e HMSCM is able to scale linearly with increasing number of QoS variables and number of web services while being able to enforce global intertask constraints.

Service Selection for Web Services with Probabilistic QoS

Web services can be specified from two perspectives, namely functional and non-functional properties. Multiple services may possess the same function while vary in their non-functional properties, or called quality-of-service (QoS). QoS values are important criteria for service selection or recommendation. Most of the former works in web service selection and recommendation treat the QoS values as constants. However, QoS values of a service as perceived by a given user are intrinsically random variables because QoS value prediction can never be precise and there are always some unobserved random effects. In this work, we address the service selection problem by representing services' QoS values as discrete random variables with probability mass functions. The goal is to select a set of atomic services for composing a composite service such that the probability of satisfying constraints imposed on the composite service is high and the execution time is reasonable. Our proposed method starts with an initial web service assignment and incrementally adjusts it using simulated annealing. We conduct several experiments and the results show that our approach generally performs better than previous works, such as the integer programming method and the cost-driven method.

A Service Selection Algorithm Based on Heterogeneous QoS Model and Synthetic Weights

Quality of Service (QoS) aware-based service selection problem is a multi-attribute decision making problem. In order to solve service selection problem with QoS indicators describe by different types of data, a service selection algorithm based on heterogeneous QoS model and synthetic weight (SSAoHS) is proposed. SSAoHS introduces real number, interval number and linguistic data to describe different QoS attributes, considers the subjective and objective weights wholly, and makes the final decision referring to the expectation and variance of QoS attributes after computing the synthetic scores. SSAoHS expands the traditional service selection and it is efficient and effective.

Discovering admissible Web services with uncertain QoS

Open and dynamic environments lead to inherent uncertainty of Web service QoS (Quality of Service), and the QoS-aware service selection problem can be looked upon as a decision problem under uncertainty. We use an empirical distribution function to describe the uncertainty of scores obtained from historical transactions. We then propose an approach to discovering the admissible set of services including alternative services that are not dominated by any other alternatives according to the expected utility criterion. Stochastic dominance (SD) rules are used to compare two services with uncertain scores regardless of the distribution form of their uncertain scores. By using the properties of SD rules, an algorithm is developed to reduce the number of SD tests, by which the admissible services can be reported progressively. We prove that the proposed algorithm can be run on partitioned or incremental alternative services. Moreover, we achieve some useful theoretical conclusions for correct pruning of unnecessary calculations and comparisons in each SD test, by which the efficiency of the SD tests can be improved. We make a comprehensive experimental study using real datasets to evaluate the effectiveness, efficiency, and scalability of the proposed algorithm.

云计算环境下基于用户偏好的服务选择策略

云计算环境下，服务是指各种抽象的虚拟化的网络资源，伴随着环境的动态化、服务的多样化和用户需求的个性化，服务选择问题越来越重要。把这些服务经过进一步的组合后，以服务组合的方式提供给终端用户，能保证用户能高效地选取服务。本文从用户体验质量的角度，分析云计算环境下用户访问服务的行为特征，利用其访问服务的偏好性，提出基于用户行为偏好的服务选择策略。理论分析和实验结果表明，该策略能保证较高的性能及用户满意度，并能减少时间开销，为用户提供更有效的服务选择方案。 Service of cloud computing refers to all kinds of abstract and virtualized network resources, along with the environmental dynamism, the diversification of the services and personalized user re-quirements. Service selection problem is becoming more and more important. Further combining these services and providing them to end users in the form of service composition can ensure that the users can select service efficiently. The paper analyzes the behavior characteristics of the user to access the service under the cloud computing environment from the perspective of the user ex-perience and puts forward the service selection strategy based on behavior preferences of users by using the access service preferences. Theoretical analysis and experimental results show that the strategy can guarantee high performance and user satisfaction, reduce the time overhead and provide users with more effective service options.

A Selection Method Based on MAGDM with Interval-Valued Intuitionistic Fuzzy Sets

As the cloud computing develops rapidly, more and more cloud services appear. Many enterprises tend to utilize cloud service to achieve better flexibility and react faster to market demands. In the cloud service selection, several experts may be invited and many attributes (indicators or goals) should be considered. Therefore, the cloud service selection can be regarded as a kind of Multiattribute Group Decision Making (MAGDM) problems. This paper develops a new method for solving such MAGDM problems. In this method, the ratings of the alternatives on attributes in individual decision matrices given by each expert are in the form of interval-valued intuitionistic fuzzy sets (IVIFSs) which can flexibly describe the preferences of experts on qualitative attributes. First, the weights of experts on each attribute are determined by extending the classical gray relational analysis (GRA) into IVIF environment. Then, based on the collective decision matrix obtained by aggregating the individual matrices, the score (profit) matrix, accuracy matrix, and uncertainty (risk) matrix are derived. A multiobjective programming model is constructed to determine the attribute weights. Subsequently, the alternatives are ranked by employing the overall scores and uncertainties of alternatives. Finally, a cloud service selection problem is provided to illustrate the feasibility and effectiveness of the proposed methods.

A multi-dimensional trust-aware cloud service selection mechanism based on evidential reasoning approach

In the last few years, cloud computing as a new computing paradigm has gone through significant development, but it is also facing many problems. One of them is the cloud service selection problem. As increasingly boosting cloud services are offered through the internet and some of them may be not reliable or even malicious, how to select trustworthy cloud services for cloud users is a big challenge. In this paper, we propose a multi-dimensional trust-aware cloud service selection mechanism based on evidential reasoning (ER) approach that integrates both perception-based trust value and reputation based trust value, which are derived from direct and indirect trust evidence respectively, to identify trustworthy services. Here, multi-dimensional trust evidence, which reflects the trustworthiness of cloud services from different aspects, is elicited in the form of historical users’ feedback ratings. Then, the ER approach is applied to aggregate the multi-dimensional trust ratings to obtain the real-time trust value and select the most trustworthy cloud service of certain type for the active users. Finally, the fresh feedback from the active users will update the trust evidence for other service users in the future.

Distributed joint optimization for large-scale video-on-demand

We study the provisioning of large-scale video-on-demand (VoD) services to distributed users. In order to achieve scalability in user capacity overcoming the limitation in core network bandwidth, servers are deployed close to user pools. They replicate movie segments cooperatively under the constraint of their storages. Considering the realistic scenario that access delay is a function of the total traffic in the underlay link (including cross-traffic), we address the following optimization issues in the server overlay: (1) Which segments should each server replicate to achieve network-wide good locality effect? This is the so-called content replication (CR) problem; (2) Given a segment miss at a server and a number of remote servers storing the segment, which of them should serve the local server to conserve network bandwidth? This is the so-called server selection (SS) problem; and (3) Given a certain total storage budget in the VoD network, what should be the capacity allocated to each server to achieve low access delay? This is so-called storage planning (SP) problem. Clearly the decisions of CR, SS and SP are inter-dependent, and hence need to be jointly optimized.We first formulate the joint optimization problem and prove that it is NP-hard. We then propose a simple and distributed algorithm called CR–SS–SP to address it. CR–SS–SP achieves good storage allocation, replicates segments collaboratively and adaptively to achieve high locality, and selects servers efficiently with a simple lookup. Simulation results on both Internet-like and real ISP topologies show that CR–SS–SP significantly outperforms existing and state-of-the-art approaches by a wide margin (often by multiple times).

A Hybrid Particle Swarm Optimization Algorithm for Service Selection Problem in the Cloud

With the growing number of alternative services in the cloud environment, users have put forward new requirements to solve the service dynamic selection problem quickly and efficiently. In this paper, an evaluation model of service process which considers concurrent requests and service association is proposed. This model evaluates the service process from three dimensions which are functional quality dimension, non-functional quality dimension and transactional dimension. To solve the service selection problem efficiently, we first design a novel coding strategy of particle, and then propose an approach based on hybrid particle swarm optimization algorithm which combines the crossover and mutation operators of genetic algorithm. The experimental results show that our proposed approach is feasible and effective.

An approach for Web service selection based on confidence level of decision maker.

Web services today are among the most widely used groups for Service Oriented Architecture (SOA). Service selection is one of the most significant current discussions in SOA, which evaluates discovered services and chooses the best candidate from them. Although a majority of service selection techniques apply Quality of Service (QoS), the behaviour of QoS-based service selection leads to service selection problems in Multi-Criteria Decision Making (MCDM). In the existing works, the confidence level of decision makers is neglected and does not consider their expertise in assessing Web services. In this paper, we employ the VIKOR (VIšekriterijumskoKOmpromisnoRangiranje) method, which is absent in the literature for service selection, but is well-known in other research. We propose a QoS-based approach that deals with service selection by applying VIKOR with improvement of features. This research determines the weights of criteria based on user preference and accounts for the confidence level of decision makers. The proposed approach is illustrated by an example in order to demonstrate and validate the model. The results of this research may facilitate service consumers to attain a more efficient decision when selecting the appropriate service.

Optimization of Multiple-Objective Web Service Selection Using Fractional Programming

The Service Oriented Approach (SOA) is based on the idea that distributed applications can be flexibly composed by integrating independently developed component services. Due to the fact that a large number of available services offer similar functionality, when choosing actual services to be included in the composition their non-functional (Quality of Service – QoS) properties must also be taken into account. On the other hand certain constraints regarding the required performances (i.e. the required QoS levels) may also be given. Therefore, web service selection presents a multiple-objective multiple constraint problem and in this paper we model it as a discrete multiple-objective linear fractional programming (MOLFP) problem. In order to solve this problem we use a previously published technique for generating strongly efficient solutions to a continuous MOLFP problem to the discrete case. We present a complete methodology for solving the multiple criteria web service selection problem and report the experimental results obtained by testing it on 50 random instances generated by varying the input parameters.

A QoS Constraints Location-based Services Selection Model and Algorithm under Mobile Internet Environment

With the rapid development of communication technology and mobile Internet, selecting from mass Location-based Services to meet the needs for different users becomes the research hot spot under mobile Internet environment. First of all, it starts from QoS constraints as well as user preferences in this paper, and then describes a mobile service selection framework based on QoS constraints and user preferences. Furthermore, a User Preferences Database is built in modeling user preferences by Similarity Formula. Users’ feedback is adopted to update User Preferences Database, which makes the combination between QoS constraints and user preferences more compact. Then, a LBS selection model and algorithm are proposed separately, which considers the QoS attributes and user preferences. Finally, it takes Online Learning Resources Platform (OLRP) based on mobile terminals as a sample, and designs a mobile course resources selection system on campus. The results show that the proposed algorithm is precision and effective in solving the problem of service selection to support Location-based Services personalization.