Cost-Effective Resource Provisioning in Cloud Using Cooperative Coevolutionary Genetic Algorithm

Author(s):  
Monika Kumari ◽  
Gadadhar Sahoo
2012 ◽  
Vol 23 (4) ◽  
pp. 765-775 ◽  
Author(s):  
Quan LIU ◽  
Xiao-Yan WANG ◽  
Qi-Ming FU ◽  
Yong-Gang ZHANG ◽  
Xiao-Fang ZHANG

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Monika Kumari ◽  
G. Sahoo

Cloud is a widely used platform for intensive computing, bulk storage, and networking. In the world of cloud computing, scaling is a preferred tool for resource management and performance determination. Scaling is generally of two types: horizontal and vertical. The horizontal scale connects users’ agreement with the hardware and software entities and is implemented physically as per the requirement and demand of the datacenter for its further expansion. Vertical scaling can essentially resize server without any change in code and can increase the capacity of existing hardware or software by adding resources. The present study aims at describing two approaches for scaling, one is a predator-prey method and second is genetic algorithm (GA) along with differential evolution (DE). The predator-prey method is a mathematical model used to implement vertical scaling of task for optimal resource provisioning and genetic algorithm (GA) along with differential evolution(DE) based metaheuristic approach that is used for resource scaling. In this respect, the predator-prey model introduces two algorithms, namely, sustainable and seasonal scaling algorithm (SSSA) and maximum profit scaling algorithm (MPSA). The SSSA tries to find the approximation of resource scaling and the mechanism for maximizing sustainable as well as seasonal scaling. On the other hand, the MPSA calculates the optimal cost per reservation and maximum sustainable profit. The experimental results reflect that the proposed logistic scaling-based predator-prey method (SSSA-MPSA) provides a comparable result with GA-DE algorithm in terms of execution time, average completion time, and cost of expenses incurred by the datacenter.


2019 ◽  
Vol 145 (5) ◽  
pp. 04019016 ◽  
Author(s):  
Weiling Li ◽  
Kewen Liao ◽  
Qiang He ◽  
Yunni Xia

Author(s):  
Michael J. Perry ◽  
John E. Halkyard ◽  
C. G. Koh

Preliminary design of floating offshore structures involves determining structural dimensions able to provide sufficient buoyancy to carry the required topside, at the lowest possible cost, while satisfying various stability, strength, installation, and response requirements. A novel optimization strategy, capable of carrying out the preliminary design of floating offshore structures, is presented in this paper. The genetic algorithm based strategy searches within prescribed parameter limits for the most cost effective design, while ensuring the design conforms to the constraints given. The design of a truss spar is used to illustrate how the strategy can be applied. The topside weight, design wind speed, maximum wave height, etc are input along with constraints such as, maximum draft at floatoff, maximum heel angle, allowable stress in the truss and limits on pitch and heave period and response. Using empirical estimates for hull weights and simplified response calculations, the strategy is then able to rapidly determine parameters such as hull diameter, hard tank depth, length of keel tank, total length and truss leg diameter such that the total cost of the structure is minimized. The strategy allows for the preliminary design phase to be completed in only a few seconds, while providing initial weight and cost estimates.


2012 ◽  
Vol 39 (9) ◽  
pp. 978-992 ◽  
Author(s):  
Ahmed Atef ◽  
Hesham Osman ◽  
Osama Moselhi

This paper presents a framework for optimizing condition assessment policies by balancing the revealed value of information with the cost of obtaining such information. The computational platform is based on augmenting the asset condition state with an expected level of accuracy. Inaccuracies due to condition assessment reliability are evaluated using the partially observable Markov decision process. The single objective genetic algorithm is used to select the most cost-effective assets to assess considering information inaccuracy under a fixed budget. The model is extended using multiobjective genetic algorithms and fuzzy set theory to include minimizing the risk exposure based on asset consequence of failure. This methodology takes into consideration direct and indirect costs of sudden infrastructure failure and reduced level of service costs. A case study is presented using the City of Hamilton, Canada, water network to demonstrate the capabilities of the model.


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