Optimal Service Selection Research Articles

Efficient service selection approach for mobile devices in mobile cloud

The paper proposes optimal efficient cloud service selection scheme for mobile device through local and public cloud in hybrid cloud. The proposed model consists of non-dedicated public cloud resources and local cloud resources. At different locations, local cloud service providers maintain finite dedicated resources that can be used by mobile device users. In the proposed model, the system middleware first examines if any of the local cloud servers have sufficient idle capacity to satisfy the desired requirement of mobile application. If the mobile users' requirement cannot be handled locally, the proposed architecture must choose mobile users' jobs to burst to the remote public cloud. The decision of when to trigger a cloud burst involves monitoring the system context and workload. A cloud-based service selection optimization is proposed, which can optimize the resource utilization, cloud supplier's benefit and mobile user's QoS. The proposed optimization problem is partitioned by local cloud service selection and public cloud resource scheduling, which is implemented by the mobile device, mobile cloud service broker and cloud supplier. A cloud service selection algorithm for mobile device is proposed, which is composed of two sub-algorithms: cloud service selection optimization and cloud resource scheduling optimization. The efficiency of the cloud service selection algorithm for mobile device is tested by experiments.

Scheduled Based Optimal Web Service Selection

Scheduled Based Optimal Web Service Selection

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.

Distributed Approach for QoS Service Selection in Web of Objects

In the Internet of Things (IoT), web objects and services can be seen as distributed and cooperative agents that need to collaborate in order to reach advanced functionalities and also to optimize the overall quality offered to the end users. For this purpose, we propose in this paper a distributed and optimal QoS services selection approach based on Multi-Agents paradigm and Distributed Constraints Optimization Formalism (DCOP). Hence, we revisited a DCoP technique for developing a new complete algorithm for service selection: the’Synch4QoS’ implemented under a multi-agent architecture using a real-time Web protocol for communi- cation. The proposed algorithm takes into accounts the specificities of the service composition context and the satisfaction of the global user's constraints.

A New Ant Algorithm for Optimal Service Selection with End-to-End QoS Constraints

In this paper, we study the dynamic service composition becomes a decision problem on which component services should be selected under the E2E-to- End Quality of Service constraints. This problem is modeled is a nonlinear optimization problem under several constraints. We have two approaches to solve this problem. The first approach is local selection which is an efficient strategy with polynomial time complexity but can not handle global constraints. The second approach is the traditional global selection approach based on integer linear programming can handle global constraints, but its poor performance renders it inappropriate for practical applications with dynamic requirements. To overcome this disadvantage, in this study we proposed a novel Min-Max Ant System algorithm with the new utilities heuristic information to search the global approximate optimal. Our goal focus on its efficiency in terms of execution time and the quality in terms of the obtained best solution fitness value. The experiment results show that our algorithm is efficiency and feasibility more than the recently proposed related algorithms.

Personalized QOS based Ranking Approach for Cloud Service Selection

Cloud computing is an Internet-based computing model. This model enables access to resources and services on demand. Cloud computing users have applications with different Quality of Service requirements. On the other hand, there are different cloud service providers offering services with different qualitative characteristics. Determining the best cloud computing service for a specific application is a significant research problem. Ranking of cloud service providers compares different services offered by different providers based on quality of service, in order to select the most suitable cloud service provider. QoS parameters provide valuable information for making optimal cloud service selection from a set of functionally equivalent service candidates. To obtain QoS values, realworld invocations on the service candidates are usually required. This project proposes a QoS ranking prediction framework for cloud services that eliminates delay and expenses involved in real-world service invocations. It makes use of the past service usage experiences of other users. This framework does not require any additional invocations of cloud services while making QoS ranking prediction. The algorithm is implemented by considering both cost and benefit parameters such as Response time and throughput respectively using a database containing response time and throughput values of 300 users for 10 different cloud providers. Also, Sensitivity analysis is done by varying weights of individual QoS parameters to verify the correctness of the algorithm. It is observed from the results that the proposed cloud service selection algorithm is able to appropriately choose the best cloud service provider depending on the weights of the respective QoS parameters. General Terms Algorithms

Multivariate process capability evaluation of cloud manufacturing resource based on intuitionistic fuzzy set

In the manufacturing process on cloud system, there exist a lot of resource services which have similar functional characteristics according to functional requirements of a manufacturing task. How to select the optimal resources chain or resources combination to accomplish the certain manufacturing task with distributed subtasks is a key issue in the study of cloud manufacturing (CMfg). In this study, a multivariate process capability indicator is put forward to evaluate the manufacturing process with the reliability of data on the cloud decaying when time passing. According to the fuzzy quality theory, the dominance degree of intuitionistic fuzzy value is taken into account in decision making when selecting the optimal resources chain. A new method is proposed for resource service optimal selection based on multivariate process indicator and dominance degree of intuitionistic fuzzy value. A practice case study is used to illustrate the proposed method and procedure. The performance and advantage of the proposed method are discussed at the end.

A Transaction and QoS-Aware Service Selection Approach Based on Genetic Algorithm

As there are various risks of failure in its execution, a composite web service (CWS) requires a transactional mechanism to guarantee its reliable execution. Though the existing service selection methods have considered that its transactional properties may affect its quality of service (QoS) such as its execution time, some of these methods can just give the locally optimal transactional CWS while others can give globally optimal CWS only under a given fixed transactional workflow. This paper addresses the issue of selecting and composing web services via a genetic algorithm (GA) and gives a transaction and QoS-aware selection approach. First, it introduces transactional properties of a single web service and CWS and the transactional rules used to compose them. Next, it conducts the performance analysis of basic workflow patterns such as sequential, parallel, selectable, and loop patterns and develops an algorithm to compute the execution time of a complex CWS. Then, it presents a GA-based approach, which takes into account the execution time, price, transactional property, stability, and penalty-factor, to achieve globally optimal service selection. Finally, this paper reports experimental results that compare the proposed approach with the exhaustive search algorithm, transactional-QoS-driven selection algorithm, and transactional service selection algorithm. The experimental results show that the proposed algorithm is efficient and effective and can give a globally optimal transactional CWS.

Hybrid cloud service selection strategy: Model and application of campus

ABSTRACTThe paper proposes hybrid cloud service selection optimization scheme. Hybrid cloud service selection optimization is distributed to and performed at two levels: hybrid cloud user agent‐ hybrid cloud service agent at hybrid cloud service layer and hybrid cloud service agent‐ hybrid cloud agent at hybrid cloud resource layer. Interactions among hybrid cloud user agent, hybrid cloud service agent, public cloud agents and private cloud agents are mediated by means of market mechanisms. Two level hybrid cloud service selection optimization maximizes the interests of hybrid cloud user agents, hybrid cloud service agent, public cloud agents and private cloud agents. Hybrid cloud service selection process includes two parts: hybrid cloud service provisioning and cloud resource allocation. The paper presents a two‐level hybrid cloud service selection algorithm used to perform service provisioning and resource allocation. In the simulations, compared with other related algorithm, our proposed hybrid cloud service selection algorithms achieve the better performance with acceptable overhead. © 2015 Wiley Periodicals, Inc. Comput. Appl. Eng. Educ. Comput Appl Eng Educ 23:645–657, 2015; View this article online at wileyonlinelibrary.com/journal/cae; DOI 10.1002/cae.21634

Correlation-aware QoS modeling and manufacturing cloud service composition

Recently, cloud manufacturing has attracted much attention from both academic and industry communities. Manufacturing cloud service composition and optimization is critical to the optimal resources allocation in cloud manufacturing. Since there are many manufacturing cloud services available with similar functions but different quality of service (QoS), and with potential quality correlations among them, such correlations must to be considered for manufacturing cloud service composition. In this paper, a correlation-aware manufacturing cloud service description model is presented to characterize the QoS dependence of an individual service on other related services. Based on such a model, a service correlation mapping model is proposed for getting correlation QoS values among services automatically. In addition, an effective approach for the correlation-aware optimal service selection is proposed based on a genetic algorithm. A case study indicates that services composition of higher quality can be obtained when such correlations are considered. And the effectiveness and efficiency of the proposed approach are demonstrated via simulation studies.

Optimizing Service Selection in Combinatorial Auction

Optimizing Service Selection in Combinatorial Auction

A QoS-aware optimal selection scheme for web services with a trusted environment

Optimal web service selection among number of services for diversified user’s functional requirements brings a challenging issue. Currently available service selection schemes consider the quality of service (QoS) parameters of web services. These schemes produce less significant results when there are multiple service providers with their multiple functionally identical services. Present paper describes a QoS-aware optimal selection scheme. The paper considers an additional QoS parameter called trust rate for each service provider. A trust rate depends on the reliability of the service provider. The paper describes a binary search algorithm, which selects the trustworthy services. This study discusses results of computational time complexity for searching optimal services. The QoS-aware selection is optimized based on trust rates. Advanced Java development environment Eclipse is used for the implementation. Axis2 server is used to create the web services. A comparative study of results with the complexity computed for the earlier adopted schemes shows that complexity decreases exponentially. The proposed scheme is experimented on e-supply chain management system. Along with trust rate, some extended parameters can be evaluated to achieve optimization.

Conformance Checking and QoS Selection Based on CPN for Web Service Composition

The development of new services by composition of existing ones has gained considerable momentum as a means of integrating heterogeneous applications and realizing business collaborations. More and more Web services with similar function attributes but different QoS are available. The performance of the composed service is determined by the involved Web services. Therefore, QoS properties are crucial for selecting the Web services to take part in the composition, which can identify the best candidate Web services from a set of functionally-equivalent services. Web service composition enables seamless and dynamic integration of business applications on the web. Due to the inherent autonomy and heterogeneity of component Web services, it is difficult to predict the behavior of the overall composite service. Conformance checking identifies failure and conflict of execution of composite Web service that ensures reliable execution. In this paper we use skyline computation to select services for composition efficiently, reducing the number of candidate services to be considered. Then a novel color Petri net model of Web service composition is presented that combines QoS-based optimal service selection and consistence verification. In the model we define aggregation functions, and use a Multiple Attribute Decision Making approach for the utility function to achieve optimal services selection of QoS properties. We also propose a consistence verification approach to identify potential logical inconsistence of the semantic Web service process before the deployment. Proofs are also presented. We evaluate our approach experimentally using both real and synthetically generated datasets.

A GA-Tabu Based User Centric Approach for Discovering Optimal Qos Composition

Cloud computing is an emerging internet- based paradigm of rendering services on pay- as -per -use basis. Increasing growth of cloud service providers and services creates the need to provide a tool for retrieval of the high-quality optimal cloud services composition with relevance to the user priorities. Quality of Service rank- ings provides valuable information for making optimal cloud service selection from a set of functionally equiva- lent service candidates. To obtain weighted user-centric Quality of Service Composition, real-world invocations on the service candidates are usually required. To avoid the time-consuming and expensive real-world service invocations, this paper proposes framework for prediction of optimal composition of services requested by the user. Taking advantage of the past service usage experiences of the consumers more cost effective results are achieved. Our proposed framework enables the end user to deter- mine the optimal service composition based on the input weight for individual service Quality of Service. The Ge- netic algorithm and basic Tabu search is applied for the user-centric Quality of Service ranking prediction and the optimal service composition. The experimental results proves that our approaches outperform other competing approaches.

Optimal Server Selection in a Queueing Loss Model with Heterogeneous Exponential Servers, Discriminating Arrivals, and Arbitrary Arrival Times

We consider a multiple server queueing loss system where the service times of server i are exponential with rate μ i , where μ i decreases in i. Arrivals have associated vectors (X 1, …, X n ) of binary variables, with X i = 1 indicating that server i is eligible to serve that arrival. Arrivals finding no idle eligible servers are lost. Letting I j be the indicator variable for the event that the jth arrival enters service, we show that, for any arrival process, the policy that assigns arrivals to the smallest numbered idle eligible server stochastically maximizes the vector (I 1, …, I r ) for every r if the eligibility vector of arrivals is either (a) exchangeable, or (b) a vector of independent variables for which P(X i = 1) increases in i.

Optimal Server Selection in a Queueing Loss Model with Heterogeneous Exponential Servers, Discriminating Arrivals, and Arbitrary Arrival Times

We consider a multiple server queueing loss system where the service times of server i are exponential with rate μi, where μi decreases in i. Arrivals have associated vectors (X1, …, Xn) of binary variables, with Xi = 1 indicating that server i is eligible to serve that arrival. Arrivals finding no idle eligible servers are lost. Letting Ij be the indicator variable for the event that the jth arrival enters service, we show that, for any arrival process, the policy that assigns arrivals to the smallest numbered idle eligible server stochastically maximizes the vector (I1, …, Ir) for every r if the eligibility vector of arrivals is either (a) exchangeable, or (b) a vector of independent variables for which P(Xi = 1) increases in i.

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.

Ranking Prediction of Cloud Services based on BPR

Cloud is an advancing technology where the the concept of service oriented architecture, distributed, autonomic, and utility computing is being utilized by various service providers. The current changing world is highly challengeable to build high-quality cloud application. Since QoS ranking that provide beneficial information for optimal cloud service selection is time consuming and expensive, this paper focuses on QoS ranking prediction framework for selecting optimal cloud services based on Bayesian Personalized Ranking. Our proposed framework is able to perform well with no additional invocations of cloud services while predicting appropriate services. QoS Ranking Prediction can be done by two approaches. The simulation results show that the approaches are outperforming other competing approaches.

Modelling and exploring historical records to facilitate service composition

Along with a continuously growing number of Web services, how to locate appropriate Web services to complete the task of service composition is becoming more critical. Differing from most recent studies which mainly focus on functional and non-functional properties, we mine nuggets from the Historical Service-composition Dataset HSD, which carries related users' past experiences. In this paper, a graph mining based recommendation approach is presented to facilitate the process of service composition. In particular, we first extend the graph mining approach gSpan to recognise Frequently Used Web Services FUWS with their connecting structures from HSD. Then, according to the records in HSD, which share the same FUWSs with user's partially composed service, a bunch of Web services with higher probability is recommended automatically as candidates. Finally, the skyline approach is adopted for optimal composite service selection with consideration of overall quality of services QoS. Furthermore, experiments based on 1,530 real Web services demonstrate the effectiveness and efficiency of our approach.

Ranking of Cloud Services Using Dynamic Qos

Cloud computing is an internet based computing and is becoming popular. QoS rankings provide valuable information for making optimal cloud service selection from a set of functionally equivalent service candidates. To obtain QoS values, real-world invocations on the service candidates are usually required. To avoid the time-consuming and expensive real-world service invocation QoS ranking prediction framework is used. This framework requires no additional invocations of cloud services when making QoS ranking prediction. Two personalized QoS ranking prediction approaches are used to predict the QoS rankings directly. Comprehensive experiments are conducted employing real-world QoS data, including 300 distributed users and 500 real-world web services all over the world. The proposed uses modernized ranking approach which uses different QoS parameters to predict the ranking more accurately. Different QoS parameters like latency, availability, failure probability can be used to improve the ranking. Keyword: Quality-of-service, cloud service, ranking prediction, personalization