Test Resource Allocation for Uncertainty Quantification of Multi-level and Coupled Systems

2011 ◽  
Author(s):  
Shankar Sankararaman ◽  
Sankaran Mahadevan ◽  
Kyle McLemore ◽  
Samuel Bradford ◽  
Chen Liang ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Uncertainty Quantification ◽  
Coupled Systems ◽  
Multi Level

scholarly journals Uncertainty quantification in tsunami modeling using multi-level Monte Carlo finite volume method

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Carlos Sánchez-Linares ◽  
Marc de la Asunción ◽  
Manuel J Castro ◽  
José M González-Vida ◽  
Jorge Macías ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Uncertainty Quantification ◽  
Finite Volume Method ◽  
Finite Volume ◽  
Tsunami Modeling ◽  
Multi Level ◽  
Volume Method

An Efficient Multi-Core Resource Allocation using the Multi-Level Objective Functions in Cloud Environment

2020 ◽  
Vol 13 (5) ◽  
pp. 957-964
Author(s):  
Siva Rama Krishna ◽  
Mohammed Ali Hussain
Keyword(s):  
Resource Allocation ◽  
Real Time ◽  
Cloud Services ◽  
Management Technique ◽  
Cloud Environment ◽  
Allocation Model ◽  
Proposed Model ◽  
Cloud Server ◽  
Different Types ◽  
Multi Level

Background: In recent years, the computational memory and energy conservation have become a major problem in cloud computing environment due to the increase in data size and computing resources. Since, most of the different cloud providers offer different cloud services and resources use limited number of user’s applications. Objective: The main objective of this work is to design and implement a cloud resource allocation and resources scheduling model in the cloud environment. Methods: In the proposed model, a novel cloud server to resource management technique is proposed on real-time cloud environment to minimize the cost and time. In this model different types of cloud resources and its services are scheduled using multi-level objective constraint programming. Proposed cloud server-based resource allocation model is based on optimization functions to minimize the resource allocation time and cost. Results: Experimental results proved that the proposed model has high computational resource allocation time and cost compared to the existing resource allocation models. Conclusion: This cloud service and resource optimization model is efficiently implemented and tested in real-time cloud instances with different types of services and resource sets.

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