Cloud service selection: State-of-the-art and future research directions

Sun, Le; Dong, Hai; Hussain, Farookh Khadeer; Hussain, Omar Khadeer; Chang, Elizabeth

doi:10.1016/j.jnca.2014.07.019

Cited by 238 publications

(104 citation statements)

References 58 publications

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“…This includes: 1) data integrity (data accuracy and recovery), 2) level of audibility and 3) access control [28], [32], [33].…”

Section: The Selection Factors Of Cloud Computing Service Modelsmentioning

confidence: 99%

A Group Decision-Making Method for Selecting Cloud Computing Service Model

Al‐Jabri¹,

Eid²,

Sohail³

2018

ijacsa

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Abstract-Cloud computing is a new technology that has great potential for the business world. Many business firms have implemented, are implementing, or planning to implement cloud computing technology. The cloud computing resources are delivered in various forms of service models which make it challenging for business customers to select the model that suits their business needs. This paper proposes a novel group-based decision-making method where a group of decision makers is involved in the decision process. Each decision maker provides weights for the cloud selection criteria. Based on weight aggregations and deviations, decision makers would select the alternative which has the highest ratio of deviation to mean is selected. The method is illustrated with an example on the selection of cloud service models. This method is useful for IT managers in selecting the appropriate cloud service model for their organizations.

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“…This includes: 1) data integrity (data accuracy and recovery), 2) level of audibility and 3) access control [28], [32], [33].…”

Section: The Selection Factors Of Cloud Computing Service Modelsmentioning

confidence: 99%

A Group Decision-Making Method for Selecting Cloud Computing Service Model

Al‐Jabri¹,

Eid²,

Sohail³

2018

ijacsa

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“…It analyses fault tolerance according to every stage of failure.Elasticity plays a significant role in cloud computing. It is defined as flexible, smooth, general and straightforward to measure [20].Resilience is used to authenticate integrity, user's response and needs of the business applicant. Testing is recreating the actual program of the user details to accept the program valuefor system establishment.…”

Section: Fig 1: Measures Used For Best Cloud Selectionmentioning

confidence: 99%

“…[17].This paper tells failures effect on consumer applications and analyses the fault tolerance regarding each stage of failures. [19].This paper defines elasticity as the degree of the system enable to accept the workload variations by allocating and DE allocation of resource in an autonomic way, in such a way at each time the allocating support from available resources should have a close match with the present demand as near as possible [20]. This paper helps to determine the shared process at the design time, and it used for local needs, the selected method gives a global view, and its execution is done by various mash up engines of all working consumers.The resilience of the process is surveyed correspondingly [21].…”

Section: Literature Survey For Measures Based Cloud Selectionmentioning

confidence: 99%

“…Selection [7][8][9][10][11][12][13][14][15][16][17] made based on metrics like availability, throughput, scalability, fault tolerance, resilience, and elasticity. But the choice is not only based on these metrics but also on reliability [19][20][21].Though the process depends on theparameters presented in many papers, reliability based selection is not performed in any of the work. Our implementation basedon this new strategy enhances adaptive behaviour mechanism.…”

Section: Introductionmentioning

confidence: 99%

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Enhanced Adaptive Learning Mechanism for Cloud Selection

Maruthi¹,

Shanthi²,

Umamakeswari³

2018

IJET

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Estimation of cloud services in a distributed computing environment is taking more interests. There is a wealth of developing cloud benefit assets that makes it difficult for the user to select the best administration related to own applications in an evolving multiple cloud environment, particularly for online processing applications. To make clients to choose their interested cloud adequately, we need a model which holds the cloud profits, and hence dynamic cloud benefit determination procedure named Dynamic Cloud Selection (DCS) is adapted. In this procedure of selected services, every cloud benefit business deals with some group of cloud administrations, and executes the DCS method. This paper studies the cloud selection and proposed a way to improve the cloud selection based on related measures. The measures are reliability, response time, throughput, availability, utilization, resilience, scalability, and elasticity. The system is contrived to enhance the cloud benefit choice powerfully and to restore the best administration result to the client. These measures are used to form best selection strategy. User memory requirement is also considered to improve the preferred task. Experimental results proved that using this new strategy, best cloud selection is made efficiently.

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“…Also, when judgment matrix is not consistent, the adjustment is often with blindness and extensive calculation. Authors often combine this and other methods in order to overcome the stated deficiencies [9][10][11][12] and more can be seen in review paper of Le Sun [13]. In realistic environment of supplier selection and evaluation, a decision-maker must take into account different uncertain factors.…”

Section: Introductionmentioning

confidence: 99%

Two-Stage Fuzzy Logic Model for Cloud Service Supplier Selection and Evaluation

Paunović

Ralević

Gajović

et al. 2018

Mathematical Problems in Engineering

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Dynamicity of the cloud computing system, complexity of real-world decision-making problems, and nontransparent market imply numerous difficulties in finding the corresponding solution for cloud supplier evaluation and selection. Due to that reason, there is a need to develop a model that can contribute to increase of reliability of evaluation and reduction of subjectivity of the decisionmaker. Having in mind the aforementioned, the authors proposed a new concept to model a cloud supplier evaluation based on technology perspective. Here, we developed a model that combined fuzzy techniques with implemented simulation of model parameters. The proposed method for cloud supplier evaluation is flexible with an objective evaluation and ensures more precise numerical value. Numerical examples are presented to demonstrate the effectiveness and pragmatism of the proposed approach.

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Cloud service selection: State-of-the-art and future research directions

Cited by 238 publications

References 58 publications

A Group Decision-Making Method for Selecting Cloud Computing Service Model

A Group Decision-Making Method for Selecting Cloud Computing Service Model

Enhanced Adaptive Learning Mechanism for Cloud Selection

Two-Stage Fuzzy Logic Model for Cloud Service Supplier Selection and Evaluation

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