An Energy-Efficient Load Balancing Algorithm to Perform Computation Type Application Processes for Virtual Machine Environments

2015 ◽  
Author(s):  
Tomoya Enokido ◽  
Makoto Takizawa
Keyword(s):  
Load Balancing ◽  
Virtual Machine ◽  
Energy Efficient ◽  
Load Balancing Algorithm

Energy-Efficient Virtualized Scheduling and Load Balancing Algorithm in Cloud Data Centers

2021 ◽  
Vol 11 (3) ◽  
pp. 34-48
Author(s):  
J. K. Jeevitha ◽  
Athisha G.
Keyword(s):  
Quality Of Service ◽  
Load Balancing ◽  
Energy Efficient ◽  
Data Centers ◽  
Scheduling Algorithm ◽  
Cloud Data ◽  
The Third ◽  
Cloud Data Centers ◽  
Load Balancing Algorithm

To scale back the energy consumption, this paper proposed three algorithms: The first one is identifying the load balancing factors and redistribute the load. The second one is finding out the most suitable server to assigning the task to the server, achieved by most efficient first fit algorithm (MEFFA), and the third algorithm is processing the task in the server in an efficient way by energy efficient virtual round robin (EEVRR) scheduling algorithm with FAT tree topology architecture. This EEVRR algorithm improves the quality of service via sending the task scheduling performance and cutting the delay in cloud data centers. It increases the energy efficiency by achieving the quality of service (QOS).

scholarly journals Proposing a load balancing algorithm with an integrative approach to reduce response time and service process time in data centers

2019 ◽  
Vol 16 (4) ◽  
pp. 627-637
Author(s):  
Sanaz Hosseinzadeh Sabeti ◽  
Maryam Mollabgher
Keyword(s):  
Load Balancing ◽  
Data Processing ◽  
Response Time ◽  
Virtual Machine ◽  
Data Center ◽  
Processing Time ◽  
Data Centers ◽  
Virtual Machines ◽  
Overall Response ◽  
Load Balancing Algorithm

Goal: Load balancing policies often map workloads on virtual machines, and are being sought to achieve their goals by creating an almost equal level of workload on any virtual machine. In this research, a hybrid load balancing algorithm is proposed with the aim of reducing response time and processing time. Design / Methodology / Approach: The proposed algorithm performs load balancing using a table including the status indicators of virtual machines and the task list allocated to each virtual machine. The evaluation results of response time and processing time in data centers from four algorithms, ESCE, Throttled, Round Robin and the proposed algorithm is done. Results: The overall response time and data processing time in the proposed algorithm data center are shorter than other algorithms and improve the response time and data processing time in the data center. The results of the overall response time for all algorithms show that the response time of the proposed algorithm is 12.28%, compared to the Round Robin algorithm, 9.1% compared to the Throttled algorithm, and 4.86% of the ESCE algorithm. Limitations of the investigation: Due to time and technical limitations, load balancing has not been achieved with more goals, such as lowering costs and increasing productivity. Practical implications: The implementation of a hybrid load factor policy can improve the response time and processing time. The use of load balancing will cause the traffic load between virtual machines to be properly distributed and prevent bottlenecks. This will be effective in increasing customer responsiveness. And finally, improving response time increases the satisfaction of cloud users and increases the productivity of computing resources. Originality/Value: This research can be effective in optimizing the existing algorithms and will take a step towards further research in this regard.

scholarly journals A Detailed Analysis on Several Load Balancing Algorithm in Cloud Computing

10.29007/rnvj ◽  
2018 ◽  
Author(s):  
Shubhra Saxena ◽  
Navneet Sharma ◽  
Akash Saxena ◽  
Jayanti Goyal
Keyword(s):  
Cloud Computing ◽  
Distributed Computing ◽  
Load Balancing ◽  
Detailed Analysis ◽  
Virtual Machine ◽  
Bumble Bee ◽  
Cloud Framework ◽  
Execution Unit ◽  
Broad Variety ◽  
Load Balancing Algorithm

Cloud computing (CC) is rising rapidly; an expansive number of clients are pulled in towards cloud administrations for more fulfillments. Distributed computing is most recent developing innovation for expansive scale dispersed processing and parallel registering. CC gives vast pool of shared assets, program bundle, data, stockpile and a broad variety of uses according to client requests at any example of time. Adjusting the heap has turned out to be all the more intriguing examination zone in this field. Better load adjusting calculation in cloud framework builds the execution and assets use by progressively dispersing work stack among different hubs in the framework. Virtual machine (VM) is an execution unit that goes about as an establishment for distributed computing innovation. Bumble bee conduct propelled stack adjusting enhances the general throughput of handling and need construct adjusting centers with respect to decreasing the measure of time an errand needs to look out for a line of the VM.

scholarly journals Optimization Load Balancing Over an Imbalance Datacenter Topologies

2019 ◽  
Vol 8 (3) ◽  
pp. 5676-5680
Keyword(s):  
Load Balancing ◽  
Virtual Machine ◽  
Efficient Algorithm ◽  
Virtual Machines ◽  
End User ◽  
Huge Number ◽  
Number Of Clusters ◽  
Main Challenge ◽  
Cpu Utilization ◽  
Load Balancing Algorithm

The quick demand of cloud resources, responsible for design a highly dynamic and flexible Cloud, has become a main challenge in datacenter deployment.A huge number of virtual machines will be available in Datacenter. Further Datacenter will be divided into a greater number of clusters. Each cluster is grouped to same type of Virtual machines. The virtual machines inside the cluster is homogeneous and heterogeneous to other cluster. Any virtual machine can be allocated to end user. If an unhealthy and less energy virtual machine is allocated to user, it will completely degrade the performance of the machine. To overcome this issue, we use an efficient load-balancing algorithm to allocate virtual machine to end user. The Fuzzy Optimized load-balancing algorithm uses the bandwidth, memory, CPU utilization are the key metrics. An efficient algorithm increases the number of hosts allocated to each end user

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