A Fuzzy Based Trust Evaluation Model for Service Selection in Cloud Environment

2019 ◽  
Vol 11 (4) ◽  
pp. 13-27
Author(s):  
Priya G. ◽  
Jaisankar N.
Keyword(s):  
Fuzzy Inference ◽  
Service Providers ◽  
Evaluation Model ◽  
Service Selection ◽  
Cloud Service ◽  
Fuzzy Evaluation ◽  
Trust Evaluation ◽  
Trust Value ◽  
Computing Paradigm ◽  
Fuzzy Input

Cloud computing is a popular computing paradigm among several computing environments, but a deficit in trust among the users and the service providers prevents the large adoption of the cloud in most of the businesses. Cloud service providers should give assurances for providing the reliable services to the cloud consumers. The proposed work explains about the architecture of the trust evaluation model and considered four service measurement indexes (SMI) namely: availability, success rate, turnaround efficiency and feedback about a resource. The trust value for each resource is estimated by the fuzzy evaluation engine in which a fuzzy input set is derived from the SMI parameters. By applying a fuzzy inference rule on fuzzy input sets will yield a fuzzy output set and finally, the most trusted resource value is calculated by defuzzification process called center of gravity. The proposed work is done the implementation by using cloudsim with jfuzzycloud.

Research on Behavior Trust Evaluation Method of Cloud Services Based on Membership Theory

2013 ◽  
Vol 427-429 ◽  
pp. 2377-2382
Author(s):  
Ying Liu ◽  
Yan Wang ◽  
Xian You Sun
Keyword(s):  
Evaluation System ◽  
Evaluation Method ◽  
Service Providers ◽  
Evaluation Model ◽  
Degree Theory ◽  
Cloud Service ◽  
Cloud Services ◽  
Trust Evaluation ◽  
Wide Range ◽  
Cloud Users

Among the wide range of cloud service providers with different performance characteristics, in order to let the cloud users find cloud services which satisfy its performance preferences and specific trust levels,it needs to establish a reasonable and scientific cloud service trust evaluation system. This paper introduces a membership degree theory into trust evaluation model. First, it designs the trust evaluation system framework of cloud services, and establishes a trust evaluation model of cloud services. Next, it calculates the trust level of cloud services with the comprehensive trust cloud center of gravity evaluation method (CCGE). Finally, the experiment results show that this model can build precise trust relationship between cloud users and cloud services based on users performance demands.

User-centric framework to facilitate trust worthy cloud service provider selection based on fuzzy inference system

2021 ◽  
pp. 1-9
Author(s):  
M. Sujatha ◽  
K. Geetha ◽  
P. Balakrishnan
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Service Providers ◽  
Selection Process ◽  
Cost Model ◽  
Service Selection ◽  
Cloud Service ◽  
Second Phase ◽  
Inference System ◽  
The Cost

The widespread adoption of cloud computing by several companies across diverse verticals of different sizes has led to an exponential growth of Cloud Service Providers (CSP). Multiple CSPs offer homogeneous services with a vast array of options and different pricing policies, making the suitable service selection process complex. Our proposed model simplifies the IaaS selection process that can be used by all users including clients from the non-IT background. In the first phase, requirements are gathered using a simple questionnaire and are mapped with the compute services among different alternatives.In the second phase, we have implemented the Sugeno Fuzzy inference system to rank the service providers based on the QoS attributes to ascertain the appropriate selection. In the third phase, we have applied the cost model to identify the optimal CSP. This framework is validated by applying it for a gaming application use case and it has outperformed the online tools thus making it an exemplary model.

Trust Management in Cloud Computing

2019 ◽  
pp. 1686-1711
Author(s):  
Vijay L. Hallappanavar ◽  
Mahantesh N. Birje
Keyword(s):  
Cloud Computing ◽  
Service Provider ◽  
Management System ◽  
Trust Management ◽  
Service Providers ◽  
Cloud Service ◽  
Network Access ◽  
Trust Value ◽  
Trust Management System ◽  
Computing Services

Cloud computing is a model for enabling everywhere, suitable, on-demand network access. There are a number of challenges to provide cloud computing services and to accomplish this, it is necessary to establish trust across the cloud, between the user and the service provider. It is becoming increasingly complex for cloud users to make distinction among service providers offering similar kinds of services. There must be some mechanisms in the hands of users to determine trustworthiness of service providers so that they can select service providers with confidence and with some degree of assurance that service provider will not behave unpredictably or maliciously. An effective trust management system helps cloud service providers and consumers reap the benefits brought about by cloud computing technologies. Hence the objective of this chapter is to describe existing mechanisms that are used to determine a trust worthiness of a cloud service, various models that are used for calculating a trust value and method to establish trust management system.

Trusted Cloud Computing

2019 ◽  
pp. 12-27
Keyword(s):  
Service Providers ◽  
Trusted Computing ◽  
Cloud Service ◽  
Sensitive Data ◽  
Remote Attestation ◽  
Research Directions ◽  
Computing Paradigm ◽  
Secure Computing ◽  
Cloud Service Providers ◽  
The Given

This chapter introduces various ideas to deal with insider attacks using the research directions, which are discussed in earlier chapters such as remote attestation, sealed storage, and integrity measurement. Trusted computing dependent on hardware root of trust has been produced by industry to secure computing frameworks and billions of end points. Remote attestation provides a facility to attestation the required platforms using platform configuration registers (PCR), and sealed storage is used to encrypt the consumer sensitive data using cryptographic operations. Integrity measurements are used to measure the given computing components in respective register. Here, the authors concentrated on a trusted computing paradigm to enable cloud service providers to solve the potential insider attacks at cloud premises.

Client-Centric Cloud Service Composition

2017 ◽  
pp. 144-166
Author(s):  
Vivek Gaur ◽  
Praveen Dhyani ◽  
Om Prakash Rishi
Keyword(s):  
Service Composition ◽  
Large Scale ◽  
Business Processes ◽  
Service Providers ◽  
Service Selection ◽  
Cloud Service ◽  
Quality Parameters ◽  
End User ◽  
Cloud Service Composition ◽  
Tabu Search Methods

Recent computing world has seen rapid growth of the number of middle and large scale enterprises that deploy business processes sharing variety of services available over cloud environment. Due to the advantage of reduced cost and increased availability, the cloud technology has been gaining unbound popularity. However, because of existence of multiple cloud service providers on one hand and varying user requirements on the other hand, the task of appropriate service composition becomes challenging. The conception of this chapter is to consider the fact that different quality parameters related to various services might bear varied importance for different user. This chapter introduces a framework for QoS-based Cloud service selection to satisfy the end user needs. A hybrid algorithm based on genetic algorithm (GA) and Tabu Search methods has been developed, and its efficacy is analysed. Finally, this chapter includes the experimental analysis to present the performance of the algorithm.

A Cloud Trusting Mechanism Based on Resource Ranking

2020 ◽  
pp. 130-155
Author(s):  
Ajai K. Daniel
Keyword(s):  
Cloud Computing ◽  
Resource Management ◽  
Service Selection ◽  
Cloud Service ◽  
Cloud Services ◽  
Storage And Retrieval ◽  
Computing Paradigm ◽  
Efficient Resource ◽  
Software And Hardware ◽  
Cloud User

The cloud-based computing paradigm helps organizations grow exponentially through means of employing an efficient resource management under the budgetary constraints. As an emerging field, cloud computing has a concept of amalgamation of database techniques, programming, network, and internet. The revolutionary advantages over conventional data computing, storage, and retrieval infrastructures result in an increase in the number of organizational services. Cloud services are feasible in all aspects such as cost, operation, infrastructure (software and hardware) and processing. The efficient resource management with cloud computing has great importance of higher scalability, significant energy saving, and cost reduction. Trustworthiness of the provider significantly influences the possible cloud user in his selection of cloud services. This chapter proposes a cloud service selection model (CSSM) for analyzing any cloud service in detail with multidimensional perspectives.

A Robust and Efficient MCDM-Based Framework for Cloud Service Selection Using Modified TOPSIS

2021 ◽  
Vol 11 (1) ◽  
pp. 21-51
Author(s):  
Rohit Kumar Tiwari ◽  
Rakesh Kumar
Keyword(s):  
Service Provider ◽  
Service Providers ◽  
Service Selection ◽  
Cloud Service ◽  
Reversal Problem ◽  
Rank Reversal ◽  
Reversal Phenomenon ◽  
Optimal Service ◽  
Selection Framework ◽  
Cloud Users

Cloud computing has become a business model and organizations like Google, Amazon, etc. are investing huge capital on it. The availability of many organizations in the cloud has posed a challenge for cloud users to choose a best cloud service. To assist the cloud users, we have proposed a MCDM-based cloud service selection framework to choose a best service provider based on QoS requirement. The cloud service selection methods based on TOPSIS suffers from rank reversal problem as it ranks optimal service provider to non-optimal on addition or removal of a service provider and deludes the cloud user. Therefore, a robust and efficient TOPSIS (RE-TOPSIS)-based novel framework has been proposed to rank the cloud service providers using QoS provided by them and cloud user's priority for each QoS. The proposed framework is robust to rank reversal problem and its effectiveness has been demonstrated through a case study performed on a real dataset. Sensitivity analysis has also been performed to show the robustness against the rank reversal phenomenon.

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