An Efficient Resource Selection Strategy Based on Load Forecasting in Computational Grid

2013 ◽  
Vol 10 (10) ◽  
pp. 2911-2922
Author(s):  
G. Kavitha
Keyword(s):  
Resource Selection ◽  
Load Forecasting ◽  
Computational Grid ◽  
Selection Strategy ◽  
Efficient Resource

Dynamic Pricing Strategy and Optimal Resource Selection Strategy Based on Credibility Model

2014 ◽  
Vol 668-669 ◽  
pp. 1615-1620
Author(s):  
Yu Yang ◽  
Hua Zhou ◽  
Jun Hui Liu ◽  
Yun Feng
Keyword(s):  
Cloud Computing ◽  
Dynamic Pricing ◽  
Resource Selection ◽  
Supply And Demand ◽  
Market Price ◽  
Pricing Strategy ◽  
Selection Strategy ◽  
Fixed Rate ◽  
Gradual Development ◽  
Optimal Resource

With the gradual development of Cloud Computing, the model of work and business will fundamentally change in the future. Current market trading mechanism under the cloud computing environment is lacking in flexibility and most of companies adopt a fixed-rate pricing model, which is difficult to meet the different needs of users. Based on cloud bank model, this paper introduces economic theory to provide a theoretical basis for the development of resource prices and propose a dynamic pricing strategy and maximize utility resource selection strategy based on market supply and demand and credit for cloud bank. In the last part of this paper, we use simulation platform to do a simple experiment to test this dynamic pricing strategy. Experiment result shows the pricing strategy could adjust computing resource prices automatically under the general market price rule conditions and maximize utility resource selection strategy could get the max utility for resource consumers.

scholarly journals AN EFFICIENT RESOURCE SELECTION AND ALLOCATION IN CLOUD COMPUTING USING ARTIFICIAL NUTRIENTS DISTRIBUTION MODEL

2020 ◽  
Vol 4 (3) ◽  
pp. 721-730
Author(s):  
Habila Mikailu ◽  
H. E. Bello ◽  
L. Mathias
Keyword(s):  
Cloud Computing ◽  
Resource Selection ◽  
Research Work ◽  
Distribution Model ◽  
Multiple Servers ◽  
Recent Emergence ◽  
Efficient Resource ◽  
Model Cloud ◽  
Intelligent Model ◽  
Cloud Users

The recent emergence of cloud computing and its rapid advancement in recent time indicates a promising technology. However, the increasing number of providers with different policies has induced a challenge for customers to select providers that can efficiently satisfy their requirements. This research work is regarding resource selection and allocation in cloud computing using artificial nutrients distribution model. Cloud computing makes it possible for system administrators to allocate resources whenever it is required. It provides multiple servers that are expandable and can meet future needs without buying any physical computer equipment. Because there are lots of providers available commercially selection of resources from reliable provider has become difficult for cloud users. This research proposed a new intelligent model using the idea of nutrients distribution in human body to optimally select and allocate resources in cloud. This model enables users to efficiently select resources from the integrated providers as a single unit of resource pool. The model intelligently evaluates the available resources from different providers and expeditiously selects a resource of highest value for the customer. This research has designed an intelligent architecture, algorithm and the UML model for Resource Selection, Evaluation and allocation. The simulation showed that the overhead cost of searching from one provider to another as opposed to the existing methods is minimized. This model is of good quality and could obtain solution with a worthy efficiency by only making a single selection attempts as providers’ resources are interwoven to a single resource pool.

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