Adaptive Distributed Load Balancing Algorithm Based on Live Migration of Virtual Machines in Cloud

Author(s):  
Yi Zhao ◽  
Wenlong Huang

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.


Author(s):  
Federico Cimorelli ◽  
Francesco Delli Priscoli ◽  
Antonio Pietrabissa ◽  
Lorenzo Ricciardi Celsi ◽  
Vincenzo Suraci ◽  
...  

Author(s):  
Archana Singh ◽  
Rakesh Kumar

Load balancing is the phenomenon of distributing workload over various computing resources efficiently. It offers enterprises to efficiently manage different application or workload demands by allocating available resources among different servers, computers, and networks. These services can be accessed and utilized either for home use or for business purposes. Due to the excessive load on the cloud, sometimes it is not feasible to offer all these services to different users efficiently. To solve this excessive load issue, an efficient load balancing technique is used to offer satisfactory services to users as per their expectations also leading to efficient utilization of resources and applications on the cloud platform. This paper presents an enhanced load balancing algorithm named as a two-phase load balancing algorithm. It uses a two-phase checking load balancing approach where the first phase is to divide all virtual machines into two different tables based on their state, that is, available or busy while in the second phase, it equally distributes the loads. The various parameters used to measure the performance of the proposed algorithm are cost, data center processing time, and response time. Cloud analyst simulation tool is used to simulate the algorithm. Simulation results demonstrate superiority of the algorithm with existing ones.


2019 ◽  
Vol 27 (5) ◽  
pp. 3994-4008
Author(s):  
Prabavathy BALASUNDARAM ◽  
Chitra BABU ◽  
Pradeep RENGASWAMY

Author(s):  
Raghavendra Achar ◽  
P. Santhi Thilagam ◽  
Nihal Soans ◽  
P. V. Vikyath ◽  
Sathvik Rao ◽  
...  

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