Arezoo Jahani

Bio: Arezoo Jahani is an academic researcher. The author has contributed to research in topics: Cloud testing & Cloud computing security. The author has an hindex of 1, co-authored 1 publications receiving 8 citations.

Ranking Approaches for Cloud Computing Services Based on Quality of Service: A Review

Abstract: Cloud computing is an Internet-based computing model. This model enables accessing to information resources in request time. Cloud computing users always have applications with different requirements. On the other hand, there are different cloud Service providers which present services with different qualitative characteristics. Determining the best cloud computing service for a specific application is a serious problem for users. Ranking compares the different services offered by different providers based on quality of services, in order to select the most appropriate service. In this paper, the existing approaches for ranking cloud computing services are analyzed. The overall performance of each method is presented by reviewing and comparing of them. Finally, the essential features of an efficient rating system are indicated.

A trust centric optimal service ranking approach for cloud service selection

Abstract: Cloud service selection, a promising research directive provides an intelligent solution via. service ranking based on the Quality of Service (QoS) attributes for the identification of trustworthy Cloud Service Providers (CSPs) among a wide range of functionally-equivalent CSPs. Further, the impact of objective and subjective assessment data on the accuracy of the service selection model makes the credibility of the assessment data, a major concern for the researchers in service-oriented environments. To address the challenges with respect to the identification of the user requirement compliant CSPs, data credibility, service ranking, etc. we present Hypergraph –Binary Fruit Fly Optimization based service ranking Algorithm (HBFFOA), a trust-centric approach for the identification of suitable and trustworthy cloud service providers. HBFFOA employs hypergraph partitioning, time-varying mapping function, helly property, and binary fruit fly optimization algorithm for the identification of similar service providers, credibility based trust assessment, selection of trustworthy service providers, and optimal service ranking respectively. Experiments using synthetic QoS dataset from WSDream#2 illustrates the effectiveness, practicability, scalability and computational attractiveness of HBFFOA over the existing service selection approaches in terms of precision, stability, statistical test, and time complexity analysis.

Trust management in cloud computing: A survey

Abstract: Cloud computing is a new computing model that involves outsourcing of computer technologies due to the lack of their availability in certain locations. However, when there is no previous experience between cloud service providers and their consumers, consumers often hold a degree of uncertainty about the reliability, quality and performance of the services being offered. This paper presents a survey about the current trust management techniques regarding to the performance of cloud service providers taking into consideration other aspects like privacy, security, credibility, user feedback, etc.

Fuzzy Cloud Ranking Model based on QoS and Trust

Abstract: Cloud computing is one of the emerging domains of information technology as most of the applications are moving towards Cloud because of its features like availability, performance, security, cost, and maintenance. In recent advancement due to the rapid growth of Cloud computing technology, a vast number of Cloud service providers are available to fulfill the needs of Cloud customers. So, it is quite difficult for a customer to choose a Cloud service provider that will satisfy his needs. This paper has reviewed popular Cloud ranking models to prioritize the ranking of Cloud services based on different parameters of Cloud and proposed a fuzzy trust model for the ranking of different cloud service providers using three basic parameters capacity, cost and performance.

Enhanced Cloud Service Provisioning for Social Networks

Abstract: In the era of cloud computing and social networks prosperity, new requirements rise up for cloud services that meet social network’s needs. Several cloud service providers deliver social network services for cloud services users. The aim of this paper is to develop a framework to pick the best cloud service providers from group of available providers based on many Quality-of-Service (“QoS”) criteria attributes in order to enhance efficiency, accuracy and service provisioning. This paper also aims to provide a classification for QoS criteria attributes, which is divided into five main attributes and sub-attributes, helping in enhancing ranking process in the provided framework.

A Multi-Criteria Dual Membership Cloud Selection Model based on Fuzzy Logic for QoS

Abstract: : The use of cloud computing in various data-centric applications such as wireless sensor networks (WSN) has attracted a large number of users because the cloud integrates various features in the applications such as scalability, availability, security, etc. The adoption of on-demand services of the cloud has raised competition among various cloud service providers (CSPs).The various CSPs charge di ff erent subscription charges for their services, such as storage space and virtual processors. Hence, the selection of the most suitable cloud is a must. In this paper, a multi-criteria dual-membership-based fuzzy technique (MC-DMFT) is proposed to improve cloud users’ QoS experience and address the hurdle of choosing an appropriate cloud service. We have used the MC-DMFT method to define the phases of the overall process involved in the cloud service selection and calculated the rank values for di ff erent users for QoS. Existing approaches are not proficient enough, and they require a very complex computation process. The proposed approach uses the concept of fuzzy technique to rank various cloud service providers based on capacity, pricing, security, performance, and maintenance as key parameters. The comparative analysis shows the e ff ectiveness and potential of the proposed method.