Cloud service ranking as a multi objective optimization problem

Jahani, Arezoo; Khanli, Leyli Mohammad

doi:10.1007/s11227-016-1690-2

Cited by 22 publications

(17 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lack of the proper information and past experience of the CSPs leads the weak decision by the CSCs. Hence, many researchers [3,27,[34][35][36] have conducted a research to compare and evaluate the level of the services that user gain by the CSPs. For example, in a typical distributed environment [37], an agent (trustier) is acting in a domain where he needs to trust other agents or objects, whose ability and reliability are unknown.…”

Section: Trust Framework and Past Experiencementioning

confidence: 99%

Evaluation Theory for Characteristics of Cloud Identity Trust Framework

Ghazizadeh¹,

Cusack²

2019

Cloud Computing - Technology and Practices

View full text Add to dashboard Cite

Trust management is a prominent area of security in cloud computing because insufficient trust management hinders cloud growth. Trust management systems can help cloud users to make the best decision regarding the security, privacy, Quality of Protection (QoP), and Quality of Service (QoS). A Trust model acts as a security strength evaluator and ranking service for the cloud and cloud identity applications and services. It might be used as a benchmark to setup the cloud identity service security and to find the inadequacies and enhancements in cloud infrastructure. This chapter addresses the concerns of evaluating cloud trust management systems, data gathering, and synthesis of theory and data. The conclusion is that the relationship between cloud identity providers and Cloud identity users can greatly benefit from the evaluation and critical review of current trust models.

show abstract

Section: Trust Framework and Past Experiencementioning

confidence: 99%

Evaluation Theory for Characteristics of Cloud Identity Trust Framework

Ghazizadeh¹,

Cusack²

2019

Cloud Computing - Technology and Practices

View full text Add to dashboard Cite

show abstract

“…Jahani and Khanli [15] have proposed a NSGA_SR method which models the cloud service ranking as a multi‐objective optimisation problem. The NSGA_SR method uses objective and subjective assessments.…”

Section: Related Workmentioning

confidence: 99%

Efficient multi‐objective particle swarm optimisation based ranking system for cloud service selection

Kunhambu

Balan

2019

IET Communications

View full text Add to dashboard Cite

“…Jahani and Mohammad Khanli [29] (our earlier work) proposed a ranking system (NSGA SR) that can select some candidate services and rank them based on multi-objective optimization problem. This method is so useful and need less time than other methods in literature.…”

Section: A Jahani F Derakhshan L Mohammad Khanlimentioning

confidence: 99%

“…It can be seen that, except the approach offered by Rajkumar [6] and Jahani [29], other proposed methods do ranking based on limited attributes and they do not consider SMI attributes, in their ranking approach. In this way, user often searches for a service with special attributes which the ranking process do not perform ranking based on those attributes.…”

Section: A Jahani F Derakhshan L Mohammad Khanlimentioning

confidence: 99%

ARank: A Multi-agent Based Approach for Ranking of Cloud Computing Services

Jahani¹,

Derakhshan²,

Khanli³

2017

SCPE

Self Cite

View full text Add to dashboard Cite

Cloud computing enables access to computing, processing and storage resources as a service. These services offered to users through internet and based on payment obligations. There are various service providers and services for users, so users have the challenge of choosing appropriate service, matching their needs. Therefore, having a system which helps users to choose the best service based on their need is very important. In this paper, we propose a new multi-agent based method named ARank, which is applied for ranking algorithm to reduce the waiting time of users. ARank method uses intelligent agents which they choose some candidate services and rank these services based on the quality of service values. Furthermore, the agents of ARank include the satisfaction rate of the earlier users in ranking process. The results of our evaluation show reduction in the waiting time of the users using ARank method, compared to the existing related work, Analytic Hierarchical Process (AHP) and Singular Value Decomposition (SVD).

show abstract

Cloud service ranking as a multi objective optimization problem

Cited by 22 publications

References 32 publications

Evaluation Theory for Characteristics of Cloud Identity Trust Framework

Evaluation Theory for Characteristics of Cloud Identity Trust Framework

Efficient multi‐objective particle swarm optimisation based ranking system for cloud service selection

ARank: A Multi-agent Based Approach for Ranking of Cloud Computing Services

Contact Info

Product

Resources

About