Optimal Service Selection Research Articles

Effective BigData-Space Service Selection over Trust and Heterogeneous QoS Preferences

As the number of Cloud services is growing at a tremendous speed, there is an increasing number of service providers offering similar functionalities. Selecting services with user desired non-functional properties (NFPs) becomes of significant importance but triggers a number of Big Data related research issues. First, the selection decision should deal with a large volume of service NFPs data. Second, service selection needs to reflect diverse user preferences, including both qualitative and quantitative ones. Third, the uncertainty of the network and service load leads to high variability in NFPs. Fourth, as the trust values of service NFPs are collected via historic user's feedbacks,it brings the veracity dimension to the NFPs of services. Fifth, multiple and sometimes conflicting decision objectives for optimal service selection should be balanced. An effective service selection mechanism is in demand that can tackle all the above Big Data challenges in an integrated way to handle the highly diverse QoS with significant variability along with the trust related issues giving rise to data veracity. Existing investigations focus on either users’ QoS preferences or their trust concerns but fail to provide a systematic solution to integrate both criteria in the selection process. In this paper, we tackle heterogeneous preference- and trust-based service selection by developing a novel multi-objective optimization approach to make trade-off decision between service's trust value and user's QoS preference to rank candidate Cloud services based on their match degrees with users’ requirements. We conduct extensive experiments to evaluate the effectiveness and efficiency of the proposed approach.

Service optimal selection and composition in cloud manufacturing: a comprehensive survey

With the rapid development of cloud manufacturing (CMfg) as a new service-oriented manufacturing paradigm, a considerable progress has been made in research of different aspects of it. One of the most challenging topics of interest has been service composition and optimal selection (SCOS) problem. Since CMfg is aiming towards sharing and collaborating among distributed manufacturing resources and capabilities, selecting and combining these services into a composite service to meet the user’s requirements while keeping up the optimal service performances is gaining higher emphasis. As a result, a comprehensive survey of research to date on this NP-hard problem becomes highly desirable. In this paper, first we summarize the recent advancements in CMfg and categorize them into six main areas in a brief but concise way. Then, after a short explanation of the SCOS problem, existing research work around it has been investigated and discussed in detail from the viewpoint of selection criteria, algorithms, optimization functions, correlation consideration, mapping approaches between subtasks and services, and dynamic composition. The goal of this article is to provide a comprehensive highlight for researchers who are inspired to explore work in the related areas and acquaint them with related research work done to date.

Optimal media service selection scheme for mobile users in mobile cloud

Media cloud environments can provide a large number of multimedia services to mobile clients due to its flexibility and agility. However, a number of challenges need to be addressed so that these services are efficiently provided in terms of resources’ usage and energy consumption whilst improving the quality of service (QoS) and the user’s service satisfaction. This paper proposes a new media cloud distributed scheduling scheme that addresses these challenges, suitable for resource-intensive mobile application such as mobile video streaming. The proposed scheduling policy includes media service provisioning and cloud resource scheduling within the cloud datacenter, being able to jointly improve the mobile user’s satisfaction and the media cloud supplier’s revenue. Its aims are to minimize the service time, power consumption and costs for the service provider, through a convenient tradeoff of multiple QoS parameters and, consequently, increase the user’s satisfaction by reducing waiting times, service failure rate and power consumption of the mobile device. The validity of the proposed scheme is demonstrated by running experiments based on a practical use case of video streaming for mobile clients. The experiments were defined to allow to study the effects of request rate, video length, number of video streams and job size on the performance of the proposed media cloud distributed scheduling algorithm and compare it with related algorithms. The results show that proposed algorithm has better performance in terms of request failure rate, amount of energy consumed and response time.

Application of multivariate Gaussian model for discovery of healthcare services in cloud

Cloud computing is a concept which has made computing to appear everywhere and anywhere. Recently, cloud computing is gaining research momentum due to its on-demand, multi-tenant, scalable and elastic nature. To better utilize the benefits of cloud, the commercial marketplace offers every requirement of the consumers as services. The present scenario of healthcare industry necessitates the provisioning of healthcare products and services, making it more viable to all the stakeholders. With the increasing adoption of such services on cloud, designing novel approaches for discovery and selection of service(s) has become a concern of paramount importance. The service expectations of the consumers are multiple and varied, which insists the need for appropriate reasoning of services. Hence, the multivariate Gaussian model is chosen to model the service providers for the optimal selection of services. The discovery of services is done based on the previous experience of the consumers of such services. This paper proposes an experience based reasoning system that uses the multivariate Gaussian model to significantly refine the discovery of healthcare services in the cloud.

RETRACTED ARTICLE: Maximizing quality of multimedia streaming services in heterogeneous wireless networks using optimal multi-server technique

User expectations pertaining to multimedia data is huge. Issues in streaming like slow start and buffering hinder the quality of received content. Many authors have addressed the buffering issues. Their solutions in order to avoid buffering focussed on reducing the received multimedia quality in an attempt to avoid buffering. In this proposed work, quality issues in multimedia streaming have been focussed upon in WLAN-4G networks. A modified cat swarm optimization algorithm for optimal server selection (MCSO-SS) has been proposed to maximize the multimedia quality in terms of quality of service (QoS) and quality of experience (QoE). The proposed technique has been tested in a heterogeneous wireless environment that includes WLAN and 4G cellular networks. Experimental results prove that our proposed MCSO-SS based streaming technique increases the received multimedia content quality compared to existing techniques in terms of QoS (delay, throughput, service failure rate), and QoE (PSNR, SSIM).

Modelling and simulation of logistics service selection in cloud manufacturing

Cloud manufacturing is a service-oriented and networked manufacturing model in which manufacturing resources are provided to demanders in the manner of manufacturing services through the cloud manufacturing platform. The optimal selection problem of manufacturing services plays an important role in searching the optimal manufacturing services for specific tasks. As a special type of manufacturing services in cloud manufacturing, logistics services provide transport services for different service providers and customers. The optimal selection of logistics services is important for guaranteeing just-in-time delivery of products to service demanders. In this paper, the characteristics of logistics service selection (LSS) problem are analyzed and a mathematical model is built in terms of optimization objective, an example and general solutions. Besides, some simulations are conducted to verify the proposed LSS model. Results show that the average task delivery time obtained by the proposed model is shorter than the fastest strategy and the nearest strategy in the cloud manufacturing environment.

Approaches to Addressing Service Selection Ties in Ad Hoc Mobile Cloud Computing

The ad hoc mobile cloud (AMC) allows mobile devices to connect together through a wireless connection or any other means and send a request for web services from one to another within the mobile cloud. However, one of the major challenges in the AMC is the occurrence of dissatisfaction experienced by the users. This is because there are many services with similar functionalities but varying nonfunctional properties. Moreover, another resultant cause of user dissatisfaction being coupled with runtime redundancy is the attainment of similar quality computations during service selection, often referred to as “service selection ties.” In an attempt to address this challenge, service selection mechanisms for the AMC were developed in this work. This includes the use of selected quality of service properties coupled with user feedback data to determine the most suitable service. These mechanisms were evaluated using the experimental method. The evaluation of the mechanisms mainly focused on the metrics that evaluate the satisfaction of users' interest via the quantitative evaluation. The experiments affirmed that the use of the shortest distance can help to break selection ties between potential servicing nodes. Also, a continuous use of updated and unlimited range of users' assessments enhances an optimal service selection.

Survey on QoS metrics and ranking in cloud services

The Cloud Computing uses high speed broadband for good Quality of Service (QoS) so that Cloud based application can be used with high speed which entails the minimum response time, less latency rate and reduced amount of loss of packets. Because of the ample range within the delivered Cloud solutions, from the customer’s aspect, it's emerged as irksome to decide whose providers they need to utilize and then what's the thought of his or her option. Bestowing suitable metrics is vital in assessing practices. QoS metrics are playing an important role in selecting Cloud providers and also revamping resource utilization efficiency. To guarantee a specialized product is published, describing metrics for assessing the QoS might be an essential requirement. To obtain high quality Cloud applications, Optimal Service Selection is needed. With the increasing number of Cloud services, QoS is usually selected for describing non-functional characteristics of Cloud services. In this paper, a widespread survey on QoS metrics for service vendors and QoS Ranking in Cloud Computing is presented.

Dynamic Service Provisioning and Selection for Satisfying Cloud Applications and Cloud Providers in Hybrid Cloud

The paper presents a hybrid cloud service provisioning and selection optimization scheme, and proposes a hybrid cloud model which consists of hybrid cloud users, private cloud and public cloud. This scheme aims to effectively provide cloud service and allocate cloud resources, such that the system utility can be maximized subject to public cloud resource constraints and hybrid cloud users constraints. The paper makes use of a utility-driven approach to solve interaction among private cloud user, hybrid cloud service provider and public cloud provider in hybrid cloud environment. The paper presents hybrid cloud service provisioning and selection algorithm in hybrid cloud. The hybrid cloud market consists of hybrid cloud user agent, hybrid cloud service agent and hybrid cloud agent, which represent the interests of different roles. The experiments are designed to compare the performance of proposed algorithm with the other related work.

Cloud manufacturing service selection optimization and scheduling with transportation considerations: mixed-integer programming models

Cloud manufacturing is an emerging service-oriented manufacturing paradigm that integrates and manages distributed manufacturing resources through which complex manufacturing demands with a high degree of customization can be fulfilled. The process of service selection optimization and scheduling (SSOS) is an important issue for practical implementation of cloud manufacturing. In this paper, we propose new mixed-integer programming (MIP) models for solving the SSOS problem with basic composition structures (i.e., sequential, parallel, loop, and selective). Through incorporation of the proposed MIP models, the SSOS with a mixed composition structure can be tackled. As transportation is indispensable in cloud manufacturing environment, the models also optimize routing decisions within a given hybrid hub-and-spoke transportation network in which the central decision is to optimally determine whether a shipment between a pair of distributed manufacturing resources is routed directly or using hub facilities. Unlike the majority of previous research undertaken in cloud manufacturing, it is assumed that manufacturing resources are not continuously available for processing but the start time and end time of their occupancy interval are known in advance. The performance of the proposed models is evaluated through solving different scenarios in the SSOS. Moreover, in order to examine the robustness of the results, a series of sensitivity analysis are conducted on key parameters. The outcomes of this study demonstrate that the consideration of transportation and availability not only can change the results of the SSOS significantly, but also is necessary for obtaining more realistic solutions. The results also show that routing within a hybrid hub-and-spoke transportation network, compared with a pure hub-and-spoke network or a pure direct network, leads to more flexibility and has advantage of cost and time saving. The level of saving depends on the value of discount factor for decreasing transportation cost between hub facilities.

Autonomous Context-Based Service Optimization in Mobile Cloud Computing

As the concept of merging the capabilities of mobile devices and cloud computing is becoming increasingly popular, an important question arises: how to optimally schedule services/tasks between the device and the cloud. The main objective of this paper is to investigate the possibilities for using a decision module on mobile devices in order to autonomously optimize the execution of services within the framework of Mobile Cloud Computing while taking context into account. A novel model of the decision module with learning capabilities, service-oriented architecture, and service selection optimization algorithm are proposed to solve this problem. To achieve autonomous, online learning on mobile devices, we apply supervised learning. Information about the context, task description, the decision made and its results such as calculation time or power consumption are stored and form training data for a supervised learning algorithm, which updates the knowledge used by the decision module to determine the optimal place for the execution of a given type of task. To verify the solution proposed, service-oriented mobile processing systems for multimedia file conversion have been developed and series of experiments have been executed. Results show that the decision module has become more efficient in assigning the task to either the mobile device or cloud resources.

A Constrained Learning Approach to the Prediction of Reliability Ranking for WSN Services

Wireless Sensor Network Service Applications (WSAs) are playing an important role in Wireless Sensor Network (WSN), which bridge the gap between WSN and existing widely deployed Service-Oriented Architecture (SOA) technologies. Function properties of WSN services are important, which assure correct functionality of WSA. Meanwhile, nonfunctional properties such as reliability might significantly influence the client-perceived quality of WSA. Thus, building high-reliability WSA is a critical research problem. Reliability rankings provide valuable information for making optimal WSN service selection from functionally equivalent service candidates. There existed several methods that can conduct reliability ranking prediction of WSN services. However, it is difficult to evaluate which one is better than another, because those acquire different rankings with different preference functions. This paper proposes a constrained learning prediction of reliability ranking approach for WSN services on past service usage experiences of other WSAs, which can achieve higher accuracy and improve the performance by pruning candidate services. To validate the authors' approach, large-scale experiments are conducted based on a real-world WSN service dataset. The results show that their proposed approach achieves higher prediction accuracy than other approaches.

Online Reliability Prediction via Motifs-Based Dynamic Bayesian Networks for Service-Oriented Systems

A service-oriented System of Systems (SoS) considers a system as a service and constructs a robust and value-added SoS by outsourcing external component systems through service composition techniques. Online reliability prediction for the component systems for the purpose of assuring the overall Quality of Service (QoS) is often a major challenge in coping with a loosely coupled SoS operating under dynamic and uncertain running environments. It is also a prerequisite for guaranteeing runtime QoS of a SoS through optimal service selection for reliable system construction. We propose a novel online reliability time series prediction approach for the component systems in a service-oriented SoS. We utilize Probabilistic Graphical Models (PGMs) to yield near-future, time series predictions. We assess the approach via invocation records collected from widely used real Web services. Experimental results have confirmed the effectiveness of the approach.

Dynamic service selection with QoS constraints and inter-service correlations using cooperative coevolution

Building business processes by Web services in cloud computing has become the hotspot of service applications. Due to the complexity and uncertainty of business environment, QoS violations of service processes often take place at run-time. To rapidly recover from failures and minimize their impacts on the original execution plan of service processes, dynamic service selection is urgently needed once potential QoS violations are detected. However, existing research works do not fully investigate QoS constraints and inter-service correlations, as well as the breach penalty caused by service adjustment. In this paper, we present a new cooperative coevolutionary approach for dynamic service selection with QoS constraints and inter-service correlations. First, a novel formal model for the dynamic service selection problem with QoS constraints and inter-service correlations is presented. Second, a Double Information based Cooperative Coevolutionary algorithm (DICC) is proposed which uses Potter’s cooperative coevolutionary framework and provides both local and global knowledge for the dynamic service selection optimization. Finally, we develop a prototype system to apply our approach and adopt different test cases to show that our DICC approach performs more effectively and efficiently than existing algorithms.

A clustering network-based approach to service composition in cloud manufacturing

Cloud manufacturing (CMfg) is a new business paradigm that aims to provide manufacturing resources as services over the Internet in a convenient pay-as-you-go mode. Service composition is a critical means for achieving value adding and synergy of resources in CMfg. There are a vast number of services in a CMfg platform, which makes traditional service composition methods incapable and low efficiency in solving the problem of service composition. Most of existing research on CMfg was devoted to the optimal selection of services under a specific composition flow and a group of given candidate pools. Hence, there is a need to explore effective approaches to obtaining service composition flow and candidate sets. The network-based web service composition approach has recently drawn much attention and has been proved effective in coping with the challenge of large-scale services. Inspired by this, a novel approach called service clustering network-based service composition is proposed in this paper. In this method, services are first clustered into abstract services, and then a clustering network of the abstract services is established. In this way, service composition paths and corresponding candidate sets for fulfilling manufacturing tasks can be quickly obtained without requiring task decomposition, which decreases the difficulty and improves efficiency of service composition. Effectiveness and efficiency of the approach are verified through simulation experiments.

Multi-population parallel self-adaptive differential artificial bee colony algorithm with application in large-scale service composition for cloud manufacturing

As a new service-oriented smart manufacturing paradigm, cloud manufacturing (CMfg) aims at fully sharing and circulation of manufacturing capabilities towards socialization, in which composite CMfg service optimal selection (CCSOS) involves selecting appropriate services to be combined as a composite complex service to fulfill a customer need or a business requirement. Such composition is one of the most difficult combination optimization problems with NP-hard complexity. For such an NP-hard CCSOS problem, this study proposes a new approach, called multi-population parallel self-adaptive differential artificial bee colony (MPsaDABC) algorithm. The proposed algorithm adopts multiple parallel subpopulations, each of which evolves according to different mutation strategies borrowed from the differential evolution (DE) to generate perturbed food sources for foraging bees, and the control parameters of each mutation strategy are adapted independently. Moreover, the size of each subpopulation is dynamically adjusted based on the information derived from the search process. Different scales of the CCSOS problems are conducted to validate the effectiveness of the proposed algorithm, and the experimental results show that the proposed algorithm has superior performance over other hybrid and single population algorithms, especially for complex CCSOS problems.

A systematic mapping study on soft computing techniques to cloud environment

Cloud computing plays an essential role in storage and transfer of big capacity data due to a rapid increase in size and the number of organizational activities. There exist numerous studies in which diverse soft computing techniques are applied to the cloud environment. The relevant extant literature that were clustered into five main categories with respect to precedence are; task optimization, power optimization, security, service selection and cost optimization. Yet, it was discovered that there is a dearth of systematic review/mapping studies particularly on soft computing techniques in cloud environment so as to obtain exclusive insight, to identify existing gaps and future research directions. Therefore the aim of this paper is to conduct a systematic mapping study of recent literature on soft computing techniques in cloud environment. For this purpose, 163 articles were chosen as primary sources that were published within the last decade, which were classified based on study focus area, type of research, contribution facet and particularly the type of soft computing technique used. Findings revealed that task optimization takes part as the highly preferred research focus area. Secondly, most of the articles found are of validation studies. The contributions of most of the studies are concerned about methods and finally the top three soft computing techniques were detected as particle swarm optimization (PSO), genetic algorithm (GA) and hybrid systems. The results of this study confirm that applying soft computing techniques in cloud computing has gained more and more significant attention recently but there still remain challenges and gaps which calls for further investigation especially in the area of cost optimization and also artificial bee colony.

MIFIM—Middleware solution for service centric anomaly in future internet models

Internet is shifting at a rapid pace and evolving into a trend called as “Future Internet (FI)”. It can be defined as the union and cooperation of paradigms such as Internet of Things (IoT), Internet of Services (IoS) and Internet of Content (IoC). In these paradigms, the role of Service oriented Computing (SoC) deserves special attention. FI can be defined as an association of web services encompassing innovative services such as converged services, intelligent services and related smart services for overcoming the structural limitations of the current internet. Among many key concerns in the services environment, service discovery and optimal service selection are considered to be vital. Service discovery enables the client to get access to the right service at the right time to complete the requested tasks while service selection determines the feasible service composition that fulfils a set of conditions while maintaining a rich Quality of User Experience (QoUE) and good Quality of Service (QoS). This paper proposes the FI middleware named MIFIM (MIddleware for Future Internet Models) incorporated with Aspect Oriented Module (AOM) for addressing the challenges in particular related to the unknown topology and missing data estimation present in IoT service discovery and optimal service selection routine named Composite Service Selection Module (CSSM) for deriving the best service composition in IoS paradigm. The AOM is evaluated in accordance with MUSIC pervasive computing middleware while CSSM is compared with the other optimality approaches. Experimental results were found encouraging and the proposed components were performing reasonably well when compared to the similar solutions.

Smart Cloud Storage Service Selection Based on Fuzzy Logic, Theory of Evidence and Game Theory

Cloud platforms encompass a large number of storage services that can be used to manage the needs of customers. Each of these services, offered by a different provider, is characterized by specific features, limitations and prices. In presence of multiple options, it is crucial to select the best solution fitting the customer requirements in terms of quality of service and costs. Most of the available approaches are not able to handle uncertainty in the expression of subjective preferences from customers, and can result in wrong (or sub-optimal) service selections in presence of rational/selfish providers, exposing untrustworthy indications concerning the quality of service levels and prices associated to their offers. In addition, due to its multi-objective nature, the optimal service selection process results in a very complex task to be managed, when possible, in a distributed way, for well-known scalability reasons. In this work, we aim at facing the above challenges by proposing three novel contributions. The fuzzy sets theory is used to express vagueness in the subjective preferences of the customers. The service selection is resolved with the distributed application of fuzzy inference or Dempster-Shafer theory of evidence. The selection strategy is also complemented by the adoption of a game theoretic approach for promoting truth-telling ones among service providers. We present empirical evidence of the proposed solution effectiveness through properly crafted simulation experiments.

A hybrid artificial bee colony algorithm for optimal selection of QoS-based cloud manufacturing service composition

With the advent of cloud manufacturing (CMfg), more and more services in CMfg platforms may provide the same functionality but differ in performance. In order to insure the manufacturing cloud to match the complicated task requirements, composited CMfg service optimal selection (CCSOS) is becoming increasingly important. This study proposes a new approach for such CCSOS problems, the so-called hybrid artificial bee colony (HABC) algorithm, which employs both the probabilistic model of Archimedean copula estimation of distribution algorithm (ACEDA) and the chaos operators of global best-guided artificial bee colony to generate the offspring individuals with consideration of quality of service (QoS) and CMfg environment. Different-scale CCSOS problems are adopted to evaluate the performance of the proposed HABC. Experimental results have shown that the HABC can find better solutions compared with such algorithms as genetic algorithm, particle swarm optimization, and basic artificial bee colony algorithm.