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Proceedings ArticleDOI

Towards Service Evaluation and Ranking Model for Cloud Infrastructure Selection

01 Aug 2015-pp 1282-1287
TL;DR: This paper proposes Cloud Service Evaluation and Ranking Model (CSERM), which utilizes service measurement index, consistency checking and multiple criteria decision making techniques to evaluate service performance (cloud resource providers) based on user-defined requirements and generate ranked list from where the customer can select the top most ranked option for deploying their applications.
Abstract: Cloud computing is bringing revolution in the world of information technology by offering on-demand publicly available desired computing resources to the cloud customers. As, there are multiple cloud service providers available in the market and are increasing exponentially, it becomes very difficult for customer to select the most appropriate option from the web repository based on his personalized quality requirements. Also, the dynamic nature of cloud services makes it even difficult for customers to assess resource provider and its service quality ensured in Service Level Agreement. Currently no model exists that allow the customer to evaluate and rank services depending on performance consistency. Under this context, this paper propose Cloud Service Evaluation and Ranking Model (CSERM), which utilizes service measurement index, consistency checking and multiple criteria decision making techniques to evaluate service performance (cloud resource providers) based on user-defined requirements and generate ranked list from where the customer can select the top most ranked option for deploying their applications. Such a model can make significant impact on service performance and compel cloud providers to satisfy their promised quality of services. A case study in this paper describes the whole model implementation.
Citations
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Journal ArticleDOI
TL;DR: A systematic literature review based on Evaluation Theory, a theory that generalizes six evaluation components, target, criteria, yardstick, data gathering techniques, synthesis techniques, and evaluation process, provides the relative strengths and weaknesses of the different CSEMs and offers a basis for researchers and decision makers to develop improvedCSEMs.

52 citations

Journal ArticleDOI
TL;DR: Deep learning has an enormous impact on medical image analysis as discussed by the authors and many computer-aided diagnostic systems equipped with deep networks are rapidly reducing human intervention in healthcare, which has a significant contribution to healthcare and provides guided interventions, radiotherapy and improved radiological diagnostics.
Abstract: Deep learning has an enormous impact on medical image analysis. Many computer-aided diagnostic systems equipped with deep networks are rapidly reducing human intervention in healthcare. Among several applications, medical image semantic segmentation is one of the core areas of active research to delineate the anatomical structures and other regions of interest. It has a significant contribution to healthcare and provides guided interventions, radiotherapy, and improved radiological diagnostics. The underlying article provides a brief overview of deep convolutional neural architecture, the platforms and applications of deep neural networks, metrics used for empirical evaluation, state-of-the-art semantic segmentation architectures based on a foundational convolution concept, and a review of publicly available medical image datasets highlighting four distinct regions of interest. The article also analyzes the existing work and provides open-ended potential research directions in deep medical image semantic segmentation.

35 citations

Journal ArticleDOI
TL;DR: This paper presents a new decision-making framework called cloud vendor selector (CVS) for effective selection of cloud vendors by mitigating the challenge of unreasonable criteria weight assignment and improper management of uncertainty.
Abstract: This paper presents a new decision-making framework called cloud vendor selector (CVS) for effective selection of cloud vendors by mitigating the challenge of unreasonable criteria weight assignment and improper management of uncertainty. The CVS comprises of two stages where, in the first stage, decision-makers’ intuitionistic fuzzy-valued preferences are aggregated using newly proposed extended simple Atanassov’s intuitionistic weighted geometry operator. Further, in the second stage, criteria weights are estimated by using newly proposed intuitionistic fuzzy statistical variance method and finally, ranking of cloud vendor (CV) is done using newly proposed three-way VIKOR method under intuitionistic fuzzy environment which introduces neutral category along with cost and benefit for better understanding the nature of criteria. An illustrative example of CV selection is demonstrated to show the practicality and usefulness of the proposed framework. Finally, the strength and weakness of the proposal are realized from both theoretic and numeric context by comparison with other methods.

31 citations


Cites methods from "Towards Service Evaluation and Rank..."

  • ...[13] 2016 MCDM-based ranking CV selection QoS from service management index Criteria for CV were chosen from service management index....

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  • ...We further extend the investigation by comparing these two proposals with the framework taken from the literature [1, 12, 13, 34] for CV selection....

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Journal ArticleDOI
TL;DR: This paper presents a comparative analysis of the prominently used MCDM methods used in the geographical region selection problem for Amazon Web Service cloud in terms of time complexity and robustness.
Abstract: Incloud computing, the selection of an efficient multi-criteria decision-making (MCDM) method (with minimum time complexity and maximum robustness) is a challenging and interesting problem. The time complexity and robustness of a MCDM method depend upon the methodology of evaluating the best alternative (i.e., cloud service). Although numerous MCDM methods are proposed for the quality-of-service based service selection in the cloud, still the issue of selecting the most efficient method remains unresolved. This paper presents a comparative analysis of the prominently used MCDM methods in terms of time complexity and robustness. The MCDM methods are used in the geographical region selection problem for Amazon Web Service cloud, and a comparative analysis of the obtained ranking results is performed. Further, application-specific analysis and sensitivity analysis are performed to ascertain the robustness of ranking methods. Experimental analysis is performed on the large-scale synthetic dataset to get the ranking overhead, i.e., time complexity of different MCDM methods.

13 citations

Proceedings ArticleDOI
11 Mar 2021
TL;DR: In this article, the features learned from the general image classification are transferred to the time-series signal (ECG) classification using transfer learning, and the performance of these deep transfers on the classification of ECG time series data is then assessed.
Abstract: The state-of-the-art deep neural networks trained on a large amount of data can better diagnose cardiac arrhythmias than cardiologists. However, the requirement of the high-volume training data is not pragmatic. In this research, the identification and classification of three ECG patterns are analyzed from a transfer learning prospect. The features learned from the general image classification are transferred to the time-series signal (ECG) classification using transfer learning. In this research, various modern deep networks trained on the ImageNet database are re-utilized for classifying scalograms (2D representation) of ECG signals. The performance of these deep transfers on the classification of ECG time-series data is then assessed.

10 citations

References
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ReportDOI
28 Sep 2011
TL;DR: This cloud model promotes availability and is composed of five essential characteristics, three service models, and four deployment models.
Abstract: Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. This cloud model promotes availability and is composed of five essential characteristics, three service models, and four deployment models.

15,145 citations

Journal ArticleDOI
TL;DR: The Analytic Hierarchy Process (AHP) as discussed by the authors is a multicriteria decision-making approach in which factors are arranged in a hierarchic structure, and the principles and philosophy of the theory are summarized giving general background information of the type of measurement utilized, its properties and applications.

7,202 citations

Journal ArticleDOI
TL;DR: This paper defines Cloud computing and provides the architecture for creating Clouds with market-oriented resource allocation by leveraging technologies such as Virtual Machines (VMs), and provides insights on market-based resource management strategies that encompass both customer-driven service management and computational risk management to sustain Service Level Agreement (SLA) oriented resource allocation.

5,850 citations


"Towards Service Evaluation and Rank..." refers background in this paper

  • ...Cloud paradigm offer three main service depending upon the customer requirements, which include, Software as a Service, Platform as a Service and Infrastructure as a Service [2], [3]....

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01 Sep 2011

1,188 citations


"Towards Service Evaluation and Rank..." refers background in this paper

  • ...It describe cloud computing as,“ A model that enable ubiquitous, continent, pay-as-per-use network access to configurable computing resources which can be quickly allocated and removed with reasonable cost and reduced management effort ” [1]....

    [...]

Journal ArticleDOI
TL;DR: In this article, the authors extend TOPSIS to solve a multiple objective decision making problem, and obtain a single-objective programming problem by using the max-min operator for the second-order compromise operation.

862 citations