Evaluation of Quality of Service Parameters for Mobile Cloud Service Using Principal Component Analysis
01 Jan 2019-pp 461-469
TL;DR: This paper’s objective is to obtain effective services with composition of services based on specific QoS parameters using principal component factor analysis method to find the best mobile cloud services.
Abstract: The cloud service is communicating between mobile devices through the Internet. They are prepended as platform independent and loosely conjoined. This paper’s objective is to obtain effective services with composition of services based on specific QoS parameters using principal component factor analysis method. The Factor analysis, analyzing the interrelationships among variables by characterizing highly correlated known as factors. In factor analysis, particularly statistical principal component analysis is used to consider the interrelationships among variables with a minimal loss of information. This analysis is made from the data collected from the cloud service providers. This result is based on components of cloud service availability, response time, throughput, reliability, successability, compliance, best practices, and web services resource framework. These are few parameters considered among the QoS parameters to find the best mobile cloud services. In this factor analysis based on principal component analysis considers these eight parameters which can be further improved to select the cloud services of QoS.
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TL;DR: In this paper, the multivariate normal distribution is used for principal component analysis and multivariate analysis of covariance and related topics, as well as multi-dimensional scaling and cluster analysis.
Abstract: Part One. Multivariate distributions. Preliminary data analysis. Part Two: Finding new underlying variables. Principal component analysis. Factor analysis. Part Three: Procedures based on the multivariate normal distribution. The multivariate normal distribution. Procedures based on normal distribution theory. The multivariate analysis of variance. The multivariate analysis of covariance and related topics. Part Four: Multi-dimensional scaling and cluster analysis. Multi-dimensional scaling. Cluster analysis.
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01 Jan 1980
TL;DR: In this article, the multivariate normal distribution is used for principal component analysis and multivariate analysis of covariance and related topics, as well as multi-dimensional scaling and cluster analysis.
Abstract: Part One. Multivariate distributions. Preliminary data analysis. Part Two: Finding new underlying variables. Principal component analysis. Factor analysis. Part Three: Procedures based on the multivariate normal distribution. The multivariate normal distribution. Procedures based on normal distribution theory. The multivariate analysis of variance. The multivariate analysis of covariance and related topics. Part Four: Multi-dimensional scaling and cluster analysis. Multi-dimensional scaling. Cluster analysis.
1,856 citations
20 May 2003
TL;DR: This paper proposes a global planning approach to optimally select component services during the execution of a composite service, and experimental results show that thisglobal planning approach outperforms approaches in which the component services are selected individually for each task in a Composite service.
Abstract: The process-driven composition of Web services is emerging as a promising approach to integrate business applications within and across organizational boundaries. In this approach, individual Web services are federated into composite Web services whose business logic is expressed as a process model. The tasks of this process model are essentially invocations to functionalities offered by the underlying component services. Usually, several component services are able to execute a given task, although with different levels of pricing and quality. In this paper, we advocate that the selection of component services should be carried out during the execution of a composite service, rather than at design-time. In addition, this selection should consider multiple criteria (e.g., price, duration, reliability), and it should take into account global constraints and preferences set by the user (e.g., budget constraints). Accordingly, the paper proposes a global planning approach to optimally select component services during the execution of a composite service. Service selection is formulated as an optimization problem which can be solved using efficient linear programming methods. Experimental results show that this global planning approach outperforms approaches in which the component services are selected individually for each task in a composite service.
1,229 citations
TL;DR: A new Web services discovery model is proposed in which the functional and non-functional requirements are taken into account for the service discovery and should give Web services consumers some confidence about the quality of service of the discovered Web services.
Abstract: Web services technology has generated a lot interest, but its adoption rate has been slow. This paper discusses issues related to this slow take up and argues that quality of services is one of the contributing factors. The paper proposes a new Web services discovery model in which the functional and non-functional requirements (i.e. quality of services) are taken into account for the service discovery. The proposed model should give Web services consumers some confidence about the quality of service of the discovered Web services.
1,081 citations
25 Jun 2005
TL;DR: Genetic Algorithms, while being slower than integer programming, represent a more scalable choice, and are more suitable to handle generic QoS attributes.
Abstract: Web services are rapidly changing the landscape of software engineering. One of the most interesting challenges introduced by web services is represented by Quality Of Service (QoS)--aware composition and late--binding. This allows to bind, at run--time, a service--oriented system with a set of services that, among those providing the required features, meet some non--functional constraints, and optimize criteria such as the overall cost or response time. In other words, QoS--aware composition can be modeled as an optimization problem.We propose to adopt Genetic Algorithms to this aim. Genetic Algorithms, while being slower than integer programming, represent a more scalable choice, and are more suitable to handle generic QoS attributes. The paper describes our approach and its applicability, advantages and weaknesses, discussing results of some numerical simulations.
953 citations
24 Sep 2007
TL;DR: The Web service relevancy function (WsRF) used for measuring the relevancies ranking of a particular Web service based on client's preferences, and QoS metrics is introduced and presented.
Abstract: Discovering Web services using keyword-based search techniques offered by existing UDDI APIs (i.e. Inquiry API) may not yield results that are tailored to clients' needs. When discovering Web services, clients look for those that meet their requirements, primarily the overall functionality and quality of service (QoS). Standards such as UDDI, WSDL, and SOAP have the potential of providing QoS-aware discovery, however, there are technical challenges associated with existing standards such as the client's ability to control and manage discovery of Web services across accessible service registries. This paper proposes a solution to this problem and introduces the Web service relevancy function (WsRF) used for measuring the relevancy ranking of a particular Web service based on client's preferences, and QoS metrics. We present experimental validation, results, and analysis of the presented ideas.
519 citations