scholarly journals Cost Optimization Using Hybrid Evolutionary Algorithm in Cloud Computing

2015 ◽  
Vol 10 (7) ◽  
pp. 758-769 ◽  
Author(s):  
B. Kavitha ◽  
P. Varalakshmi
Keyword(s):  
Cloud Computing ◽  
Evolutionary Algorithm ◽  
Cost Optimization ◽  
Hybrid Evolutionary Algorithm

scholarly journals A Two-Stage Heuristic Hybrid Evolutionary Algorithm for Virtual Machine Migration and Placement in Cloud Bursting

2019 ◽  
Vol 8 (4S2) ◽  
pp. 430-435
Keyword(s):  
Cloud Computing ◽  
Evolutionary Algorithm ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Research Work ◽  
Cloud Services ◽  
Virtual Machine Migration ◽  
Two Stage ◽  
Vm Migration ◽  
Hybrid Evolutionary Algorithm

The platform for cloud computing offers virtualization and a dynamic pool of resources to the consumers of the cloud. The acceptance and demand of cloud is growing on a regular basis. Cloud computing offers utility-based consumer services across the globe on a pay as you go strategy. Live Virtual Machine (VM) migration sets the basic foundation for cloud management. It hasakey role in reducing operating expenditure and revising quality of service without disruption of cloud services running in the VM. A lot of research has been done to yield better performance in live VM migration and has seen noteworthy development and accomplishment. However, some crucial problems require results and enhancement. With the growth of new cloud computing models like Mobile Edge Computing, certain problems related to optimization need to be addressed. The primary aim of this research work is to emphasize on optimum functioning of live migration. A migration algorithm to consolidate the computational resources, storage resources and network resources dynamically with a two-stage heuristic hybrid evolutionary algorithm is discussed. The resources are consolidated to cut down the energy and cost utilization depending upon the evolutionary Particle Swarm Optimization and the Ant Colony Optimization algorithms. These algorithms can rapidly identify the migrating virtual machines and locate their positions respectively.

scholarly journals A Survey of Cost Optimization in Serverless Cloud Computing

2021 ◽  
Vol 1802 (3) ◽  
pp. 032070
Author(s):  
Zhe Li ◽  
Yusong Tan ◽  
Bao Li ◽  
Jianfeng Zhang ◽  
Xiaochuan Wang
Keyword(s):  
Cloud Computing ◽  
Cost Optimization

scholarly journals Hybrid PSO-SA Type Algorithms for Multimodal Function Optimization and Reducing Energy Consumption in Embedded Systems

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Lhassane Idoumghar ◽  
Mahmoud Melkemi ◽  
René Schott ◽  
Maha Idrissi Aouad
Keyword(s):  
Energy Consumption ◽  
Embedded Systems ◽  
Evolutionary Algorithm ◽  
Optimal Solution ◽  
Function Optimization ◽  
Time Saving ◽  
Local Optima ◽  
Hybrid Evolutionary Algorithm ◽  
Multimodal Function Optimization ◽  
Multimodal Function

The paper presents a novel hybrid evolutionary algorithm that combines Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithms. When a local optimal solution is reached with PSO, all particles gather around it, and escaping from this local optima becomes difficult. To avoid premature convergence of PSO, we present a new hybrid evolutionary algorithm, called HPSO-SA, based on the idea that PSO ensures fast convergence, while SA brings the search out of local optima because of its strong local-search ability. The proposed HPSO-SA algorithm is validated on ten standard benchmark multimodal functions for which we obtained significant improvements. The results are compared with these obtained by existing hybrid PSO-SA algorithms. In this paper, we provide also two versions of HPSO-SA (sequential and distributed) for minimizing the energy consumption in embedded systems memories. The two versions, of HPSO-SA, reduce the energy consumption in memories from 76% up to 98% as compared to Tabu Search (TS). Moreover, the distributed version of HPSO-SA provides execution time saving of about 73% up to 84% on a cluster of 4 PCs.

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