Service Selection Algorithm Research Articles

QoS ontology for service selection and reuse

Web service technologies become popular in software development in all sectors. Variety services related standards are defined but they still have limitations to represent the services such as the service registry does not support the Quality of Service (QoS) properties, web service description language does not allow specifying the QoS properties and there is no common ontology’s structure to store services. Our research aims to enhance the representation of services to assist the service selection and composition process in order to reduce the development costs. The existing resources are analyzed to define a web service ontology (WSOnto). Furthermore, a service selection algorithm is proposed to validate the proposed WSOnto. It considers the multiple criteria of inputs such as: context, functional and non-functional properties of services. The WSOnto and a service selection algorithm are studied to assist the re-engineering of business processes from users’ designed business processes.

An Efficient Discrete Invasive Weed Optimization Algorithm for Web Services Selection

Efficient algorithm is required to select component services with end-to-end QoS constraints in dynamic composition of Web Services while optimizing the QoS of the composite service. In this paper, the services selection probelm is modeled as a nonlinear optimization with constraints, then a novel discrete invasive weed optimization web services selection algorithm is proposed. The proposed solution consists of two steps:first, a set of randomly generated feasible solutions are transformed into decimal code. Second, We utilize Gaussian diffusion to guide the population to spread in the solution space. The mutation probability and mutation step size of individual is dynamically adjusted by changing the stantard deviation of Gaussian distribution. Accordingly, the population diversity is ensured in the early stage to expand the search space, while the local search nearby excellent individuals is focused in the latter stage to ensure the global convergence. Theoretical analysis and experiment results indicate the efficiency, robustness and feasibility of our approach.

QoS-Aware Multiobjective Optimization Algorithm for Web Services Selection with Deadline and Budget Constraints

The problem of QoS-aware multiobjective optimization is an important issue for Web services selection in distributed computing environment. In this paper, a novel algorithm called MOASS (multiobjective optimization algorithm for web service selection) is proposed through analyzing the genetic operators such as constraint handling, the initial population generation, fitness assignment, and diversity preservation. Compared with MOEAWP (Yu et al., 2007), simulation results show that the feasible objective region can be filled uniformly with the optimal solutions obtained by MOASS under different test applications. In the case of higher constraints especially, MOASS can obtain more high-quality and evenly distributed nondominated solutions than MOEAWP.

Service Provisioning through Opportunistic Computing in Mobile Clouds

Mobile clouds are a new paradigm enabling mobile users to access the heterogeneous services present in a pervasive mobile environment together with the rich service offers of the cloud infrastructures. In mobile computing environments mobile devices can also act as service providers, using approaches conceptually similar to service-oriented models. Many approaches implement service provisioning between mobile devices with the intervention of cloud-based handlers, with mobility playing a disruptive role to the functionality offered by of the system. In our approach, we exploit the opportunistic computing model, whereby mobile devices exploit direct contacts to provide services to each other, without necessarily go through conventional cloud services residing in the Internet. Conventional cloud services are therefore complemented by a mobile cloud formed directly by the mobile devices. This paper exploits an algorithm for service selection and composition in this type of mobile cloud environments able to estimate the execution time of a service composition. The model enables the system to produce an estimate of the execution time of the alternative compositions that can be exploited to solve a user's request and then choose the best one among them. We compare the performance of our algorithm with alternative strategies, showing its superior performance from a number of standpoints. In particular, we show how our algorithm can manage a higher load of requests without causing instability in the system conversely to the other strategies. When the load of requests is manageable for all strategies, our algorithm can achieve up to 75% less time spent in average to solve requests.

A Hybrid Artificial Bee Colony Algorithm for the Service Selection Problem

To tackle the QoS-based service selection problem, a hybrid artificial bee colony algorithm calledh-ABC is proposed, which incorporates the ant colony optimization mechanism into the artificial bee colony optimization process. In this algorithm, a skyline query process is used to filter the candidates related to each service class, which can greatly shrink the search space in case of not losing good candidates, and a flexible self-adaptive varying construct graph is designed to model the search space based on a clustering process. Then, based on this construct graph, different foraging strategies are designed for different groups of bees in the swarm. Finally, this approach is evaluated experimentally using different standard real datasets and synthetically generated datasets and compared with some recently proposed related service selection algorithms. It reveals very encouraging results in terms of the quality of solutions.

A Dynamic Composite Web Services Selection Method With QoS-Aware Based on AND/OR Graph

AbstractService selection problem refers to the selection of appropriate Web services from a large number of candidates in order to create complex composite services that can satisfy users’ quality-of-service (QoS) requirements. However, the existing services selection methods seldom consider the parallel relations between services as well as the dynamic changing of QoS. To combat these defects, a QoS-aware composite services selection model is presented based on an AND/OR graph model in this paper. The weight of edges is defined by considering QoS attributes, and one shorter path in the model is corresponding to the better service composition that satisfies the service request. Besides, the model is not only capable of dealing with sequence relations and fork relations between services, but also capable of dealing with parallel relations between services. And then a heuristic services selection algorithm is developed based on the framework of the ant colony optimization (ACO) to guide finding the path, a...

Web Services Selection Algorithm Based on the Quality of Service

Web Services Selection Algorithm Based on the Quality of Service

Refining Reputation to Truly Select High-QoS Servers in Peer-to-Peer Networks

Peer-to-peer networks (P2Ps) use reputation systems to provide incentives for nodes to offer high quality of service (QoS) and thwart the intentions of dishonest or selfish nodes. Existing reputation systems have two problems. First, they directly regard node reputation as trust. Rather, reputation represents the opinions formed by others about a node's QoS behavior, while trust represents a node's honesty and willingness to cooperate. In addition to trust, factors such as node capacity and lifetime also influence reputation. Due to these factors' heterogeneity and variance over time, reputation cannot directly reflect a node's trust or current QoS. Second, existing reputation systems guide a node to select the server with the highest reputation, which may not actually select the highest QoS server and would overload the highest reputed nodes. This work aims to accurately reflect node trust and provide guidance for high-QoS server selection. Through experimental study, we find that node trust, available capacity, and lifetime positively affect node reputation. Based on this observation, we first propose a manual trust model and an automatic trust model that remove the influence of additional factors on reputation to truly reflect node trust. We then propose a high-QoS server selection algorithm that separately considers node trust, current available capacity, and lifetime. Extensive simulation results demonstrate the effectiveness of the trust models in accurate node trust reflection compared with an existing reputation system. Moreover, the server selection algorithm dramatically increases the success rate of service requests and avoids overloading nodes.

Optimizing DNS Server Selection

In DNS zone delegation, the NS resource records of the delegated domain appear in both the parent and child zones, which complicates the domain name resolution procedure especially for server selection. In this paper, we investigate the recursive resolution mechanism for the delegated domain and the necessary operations. As there are no specifications for such resolution, we provide some recommendations such as NS record substitution mechanism. Furthermore, we analyze the delegation impacts on server selection as the NS records are obtained from both parent and child zone. We propose an integrated sever selection procedure to handle the lame delegation and enhance server selection efficiency. It makes use of th e two stages of server probing information and can help save the initial server probing in the server selection algorithm. Several proposa ls as to the disposal of NS resource records and the maintenance of the rtt information of servers are also presented.

A hybrid anycast network architecture and intelligent server selection strategy for multimedia services

ABSTRACTMultimedia services, which are experiencing an enormous popularity nowadays, have been regarded as one of the main applications of Future Internet. A key supporting technology behind these services is a scalable, intelligent and effective anycast service. Present anycast solutions are either based on route‐by‐name or lookup‐by‐name, which cannot fulfill the requirements of today's services. In this paper, we first propose an anycast network architecture that combines the merits of both the route‐by‐name and lookup‐by‐name schemes, which achieves good balance between routing scalability and forwarding efficiency, allows flexible server selection, quickly adapts to network changes and maintains flow affinity. Based on this architecture, practical anycast‐based server selection algorithms are presented with joint considerations of both network condition and server load condition. The proposed algorithms are evaluated through the discrete event network simulations using OMNET++ and the INET framework. Experiment results show that efficient server selection can be achieved. Copyright © 2013 John Wiley & Sons, Ltd.

WS-SIM: A Toolkit for Modeling and Simulation of Service Composition

Web service has been rapidly developed in recent years. Web service selection is an important issue in web service composition and lots of service selection algorithms have been presented. As lots of them select an atomic service during runtime, it is not an easy task to evaluate the quality of the composited service which composed of several atomic services. In this work we introduce WS-SIM, a simulation toolkit, to solve this problem. WS-SIM supports modeling and simulating composite services and atomic services in the real world. This system also provides many common service selection algorithms and researchers can custom their own service selection algorithm for a simulation experiment. The quality of composite services can also be generated by our system. Furthermore, to demonstrate suitability of the WS-SIM, in this paper, functionalities of our system are illustrated by a case study. This confirms the usability and the applicability of WS-SIM.

An Ant Colony Optimization Algorithm for Selection Problem

To tackle the QoS-based service selection problem, an efficient ant colony service selection algorithm called CASS is proposed in this paper. In this algorithm, a skyline query process is used to filtering the candidates related each service class and a clustering based shrinking process is used to guide the ant search directions. We evaluate our approach experimentally using standard real datasets and synthetically generated datasets, and compared with the recently proposed related service selection algorithms. It reveals very encouraging results in terms of the quality of solution, and the processing time required.

An adaptive service selection method for cross‐cloud service composition

SUMMARYCloud computing aims at providing elastic applications that can be scaled at runtime. In practice, traditional service composition methods are not flexible enough to changes in cross‐cloud environment. In view of this challenge, an adaptive service selection method for cross‐cloud service composition is proposed in this paper, by dynamically selecting proper services with near‐optimal performance for adapting to changes in time. First, concretely speaking, the service selection and execution are modeled with Markov decision process to ensure flexibility. Second, service pair is defined, and the way to build service pairs set is proposed to predict the performance of candidate services. Third, the adaptive service selection algorithm is designed to select proper cloud services for changing cross‐cloud environment. Finally, a case study for cross‐cloud service composition and experiments are presented for validating the feasibility of our proposal. Concurrency and Computation: Practice and Experience, 2013.© 2013 Wiley Periodicals, Inc.

Research on dynamic business composition based on web service proxies

Web services-based business composition brings a number of advantages to the enterprise application development. How to select and compose the web services based on their functionality and QoS (Quality of Service) dynamically prove to be more and more important. In this paper we develop a proxy-based framework to compose Web services dynamically. The framework is featured with a QoS model, an effective service discovery and selection algorithms to facilitate the dynamic integration of Web services and management of abnormalities. Furthermore, a business process constructing method based on service slice is put forward to satisfy the users’ personalized requirements more effectively and flexibly. Our study concerns both functionality and QoS characteristics of Web services to identify the optimal business process solutions. A Complete case study is also included in this paper and the performance demonstrated that the framework and algorithms can provide a tangible and reliable solution to dynamic Web service composition and adaptation.

GOS: a global optimal selection strategies for QoS-aware web services composition

Services composition technology provides a promising way to create new services in services-oriented architecture. However, some challenges are hindering the application of services composition. One of the major challenges encountered by composite services developer is how to effectively select a set of services across different autonomous regions (e.g., organization or business) to instantiate a composite service which can satisfy user's QoS constraints. To solve QoS-aware Web service composition problem, this paper proposes a global optimization selection (GOS) approach based on prediction mechanism for QoS values of local services. The GOS includes two parts. First, the local preprocessing service selection algorithm can be used to increase composite services performance in run-time by predicting the change of service quality parameters. Second, GOS aims at enhancing the run-time performance of global selection by reducing QoS aggregation operations. The simulation results show that the GOS has excellent selection and lower execution cost than existing approaches.

Research on Transaction Web Service Selection Algorithm in WSC

Research on Transaction Web Service Selection Algorithm in WSC

Web service selection algorithm based on principal component analysis

Existing Web service selection approaches usually assume that preferences of users have been provided in a quantitative form by users. However, due to the subjectivity and vagueness of preferences, it may be impractical for users to specify quantitative and exact preferences. Moreover, due to that Quality of Service (QoS) attributes are often interrelated, existing Web service selection approaches which employ weighted summation of QoS attribute values to compute the overall QoS of Web services may produce inaccurate results, since they do not take correlations among QoS attributes into account. To resolve these problems, a Web service selection framework considering user’s preference priority is proposed, which incorporates a searching mechanism with QoS range setting to identify services satisfying the user’s QoS constraints. With the identified service candidates, based on the idea of Principal Component Analysis (PCA), an algorithm of Web service selection named PCA-WSS (Web Service Selection based on PCA) is proposed, which can eliminate the correlations among QoS attributes and compute the overall QoS of Web services accurately. After computing the overall QoS for each service, the algorithm ranks the Web service candidates based on their overall QoS and recommends services with top QoS values to users. Finally, the effectiveness and feasibility of our approach are validated by experiments, i.e. the selected Web service by our approach is given high average evaluation than other ones by users and the time cost of PCA-WSS algorithm is not affected acutely by the number of service candidates.

Service Selection Algorithm Based on Constraint for Cloud Workflow System

It is possible to utilize cloud computing to address the problems of resource extendibility and flexibility for managing large scale workflow applications. Therefore, the investigation of workflow systems based on cloud computing, namely cloud workflow systems is a timely issue and worthwhile for increasing efforts. The technology and application of workflow in cloud computing environment also becomes a research focus. The paper firstly proposes a brief introduction to the cloud workflow system. Next, based on cloud architecture, the paper presents the architecture of cloud workflow system. Then, as the key content of the paper, considering the service selection strategy based on constraint, the paper presents a new service selection algorithm–SSAC. The simulation results show that SSAC algorithm further decreases the total execution cost and total execution time in comparison with other algorithms.

Selecting skyline services for QoS-aware composition by upgrading MapReduce paradigm

With the development of web technologies and cloud computing, more and more services which provide similar functionality but differ in QoS are deployed on the Internet via cloud platforms. Recently, skyline analysis is adopted to select candidate services with better QoS to facilitate the process of QoS-aware service composition. However, the fast increasing number of services, multiple QoS attributes to be considered, and dynamic service environment pose a big challenge to skyline service selection. In this paper, we present a parallel skyline service selection method to improve the efficiency by upgrading the MapReduce paradigm. An angle-based dataspace partitioning approach is employed in our MapReduce based skyline service selection. In particular, we explore the dominance power of local skyline services to improve the efficiency of selection, and present two detailed algorithms. To handle the dynamic nature of service environment, we employ Paper-Tape (PT) model which is used to rapidly locate varying services, and present a dynamic skyline service selection algorithm based on PT model. By experimenting over both real and synthetical datasets, we demonstrate the efficiency of our proposed methods.

A Novel Ant Colony Optimization Algorithm for Large Scale QoS-Based Service Selection Problem

To tackle the large scale QoS-based service selection problem, a novel efficient clustering guided ant colony service selection algorithm called CASS is proposed in this paper. In this algorithm, a skyline query process is used to filter the candidates related to each service class, and a clustering based shrinking process is used to guide the ant to the search directions. We evaluate our approach experimentally using standard real datasets and synthetically generated datasets and compared it with the recently proposed related service selection algorithms. It reveals very encouraging results in terms of the quality of solution and the processing time required.