Hybridization of ICBC and Genetic Algorithm for Optimizing Encryption Process in Cloud Computing Application Service

2018 ◽  
Vol 157 (1-2) ◽  
pp. 79-109
Author(s):  
Mahalakshmi Jeyabalu ◽  
Kuppusamy Krishnamoorthy
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Application Service

scholarly journals Exploration of Wireless Sensor Network Based on Cloud Computing

2018 ◽  
Vol 14 (11) ◽  
pp. 16
Author(s):  
Qing Wan ◽  
Ying Wang
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Energy State ◽  
Wireless Sensor ◽  
Cloud Environment ◽  
Application Service ◽  
Operation And Maintenance ◽  
Starting Point

To realize the exploration of wireless sensor network (WSN) based on cloud computing, the application service of WSN is taken as the starting point, the resource advantage of the cloud platform is used, and a WSN service framework based on cloud environment is proposed. Based on this framework, the problems of data management and reconstruction, network coverage optimization and monitoring, and edge recognition of holes are solved. In view of the node deployment of WSN and coverage problem of operation and maintenance optimization, the genetic algorithm is used to adjust the dormancy and energy state of nodes, and a parallel genetic algorithm for covering optimization in the cloud environment is proposed. For the operation and maintenance requirements of WSN, a parallel data statistics method for network monitoring is proposed. The experimental results show that the parallel algorithm is greatly improved in terms of the accuracy and time efficiency.

Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Ge Weiqing ◽  
Cui Yanru
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Fitness Functions ◽  
Task Scheduling Algorithm ◽  
Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

Optimal machine placement based on improved genetic algorithm in cloud computing

2021 ◽  
Author(s):  
Jiawei Lu ◽  
Wei Zhao ◽  
Haotian Zhu ◽  
Jie Li ◽  
Zhenbo Cheng ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Improved Genetic Algorithm ◽  
Optimal Machine

scholarly journals Experimental Study of SaaS, Its Categories and Contribution in Cloud Computing

2022 ◽  
Vol 71 (1) ◽  
Author(s):  
Dr Anand Sharma
Keyword(s):  
Experimental Study ◽  
Cloud Computing ◽  
Distributed System ◽  
Service Provider ◽  
Software As A Service ◽  
Application Service Provider ◽  
Client Server ◽  
Application Service

SaaS is software as a service. In previous days when the concept of providing the facility of distributed system from where information can be shared was introduced then the concept of Application service provider came into the picture. The idea was to provide application-based services to the companies using client server techniques. It became popular. SaaS is introduced to provide software also as a service which is upgraded version of the ASP. In this paper SaaS will be discussed in detail. There are two main types of SaaS. The challenges in implementation of SaaS will also be discussed.

scholarly journals An Optimal Solution in cloud computing for Load Balancing using Genetic Algorithm

2019 ◽  
Vol 8 (6) ◽  
pp. 2490-2494
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Load Balancing ◽  
Virtual Machines ◽  
Optimal Solution ◽  
Cloud Services ◽  
Research Trend ◽  
Schedule Length ◽  
Research Gap ◽  
Load Balancing Algorithm

Cloud computing is a research trend which bring various cloud services to the users. Cloud environment face various challenges and issues to provide efficient services. In this paper, a novel Genetic Algorithm based load balancing algorithm has been implemented to balance the load in the network. The literature review has been studied to understand the research gap. More specifically, load balancing technique authenticate the network by enabling Virtual Machines (VM). The proposed technique has been further evaluated using the Schedule Length Runtime (SLR) and Energy consumption (EC) parameters. Overall, the effective results has been obtained such as 46% improvement in consuming the energy and 12 % accuracy for the SLR measurement. In addition, results has been compared with the conventional approaches to validate the outcomes.

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