Cost optimized Hybrid Genetic-Gravitational Search Algorithm for load scheduling in Cloud Computing

2019 ◽  
Vol 83 ◽  
pp. 105627 ◽  
Author(s):  
Divya Chaudhary ◽  
Bijendra Kumar
Keyword(s):  
Cloud Computing ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Load Scheduling ◽  
Gravitational Search

Exponential gravitational search algorithm-based VM migration strategy for load balancing in cloud computing

2018 ◽  
Vol 09 (01) ◽  
pp. 1850002 ◽  
Author(s):  
Vijayakumar Polepally ◽  
K. Shahu Chatrapati
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Resource Utilization ◽  
Virtual Machines ◽  
Search Algorithm ◽  
Moving Average ◽  
Gravitational Search Algorithm ◽  
Migration Strategy ◽  
Vm Migration ◽  
Gravitational Search

With the advancement in the science and technology, cloud computing has become a recent trend in environment with immense requirement of infrastructure and resources. Load balancing of cloud computing environments is an important matter of concern. The migration of the overloaded virtual machines (VMs) to the underloaded VM with optimized resource utilization is the effective way of the load balancing. In this paper, a new VM migration algorithm for the load balancing in the cloud is proposed. The migration algorithm proposed (EGSA-VMM) is based on exponential gravitational search algorithm which is the integration of gravitational search algorithm and exponential weighted moving average theory. In our approach, the migration is done based on the migration cost and QoS. The experimentation of proposed EGSA-based VM migration algorithm is compared with ACO and GSA. The simulation of experiments shows that the proposed EGSA-VMM algorithm achieves load balancing and reasonable resource utilization, which outperforms existing migration strategies in terms of number of VM migrations and number of SLA violations.

Analysis of Optimal Power Flow Using Gravitational Search Algorithm

2016 ◽  
Vol 3 (4) ◽  
pp. 1-11
Author(s):  
M. Lakshmikantha Reddy ◽  
◽  
M. Ramprasad Reddy ◽  
V.C. Veera Reddy ◽  
◽  
...  
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Optimal Power ◽  
Gravitational Search

Automatic Mining of Quantitative Association Rules with Gravitational Search Algorithm

2017 ◽  
Vol 27 (03) ◽  
pp. 343-372 ◽  
Author(s):  
Umit Can ◽  
Bilal Alatas
Keyword(s):  
Association Rules ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Fitness Function ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Search Space ◽  
Search Method ◽  
Quantitative Association Rules ◽  
Gravitational Search

The classical optimization algorithms are not efficient in solving complex search and optimization problems. Thus, some heuristic optimization algorithms have been proposed. In this paper, exploration of association rules within numerical databases with Gravitational Search Algorithm (GSA) has been firstly performed. GSA has been designed as search method for quantitative association rules from the databases which can be regarded as search space. Furthermore, determining the minimum values of confidence and support for every database which is a hard job has been eliminated by GSA. Apart from this, the fitness function used for GSA is very flexible. According to the interested problem, some parameters can be removed from or added to the fitness function. The range values of the attributes have been automatically adjusted during the time of mining of the rules. That is why there is not any requirements for the pre-processing of the data. Attributes interaction problem has also been eliminated with the designed GSA. GSA has been tested with four real databases and promising results have been obtained. GSA seems an effective search method for complex numerical sequential patterns mining, numerical classification rules mining, and clustering rules mining tasks of data mining.

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