An Enhanced Genetic Virtual Machine Load Balancing Algorithm for Data Center

2019 ◽  
pp. 244-253
Author(s):  
Mala Yadav ◽  
Jay Shankar Prasad
Keyword(s):  
Load Balancing ◽  
Virtual Machine ◽  
Data Center ◽  
Machine Load ◽  
Load Balancing Algorithm

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.

A Two-Phase Load Balancing Algorithm for Cloud Environment

2021 ◽  
Vol 13 (1) ◽  
pp. 38-55
Author(s):  
Archana Singh ◽  
Rakesh Kumar
Keyword(s):  
Load Balancing ◽  
Data Center ◽  
Virtual Machines ◽  
Second Phase ◽  
Cloud Environment ◽  
Cloud Platform ◽  
Efficient Utilization ◽  
Two Phase ◽  
Excessive Load ◽  
Load Balancing Algorithm

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.

OpenFlow-Based Global Load Balancing in Fat-Tree Networks

2014 ◽  
Vol 989-994 ◽  
pp. 4794-4798 ◽  
Author(s):  
Yu Wen Wu ◽  
Wei Zhang
Keyword(s):  
Load Balancing ◽  
Data Center ◽  
Essential Role ◽  
Data Centers ◽  
Cloud Services ◽  
Tree Topology ◽  
The Self ◽  
Internal Load ◽  
Tree Networks ◽  
Load Balancing Algorithm

Cloud services have been explosively popular over the last decade. And data centers play an essential role in providing cloud services. Inside a data center, any server instance has the chance to inject traffic of various applications into the network. Yet how to balance the enormous internal load to make the best of data center network is a highly prioritized problem to be solved. To provide balanced traffic in data centers, this paper proposes an OpenFlow-based GLB load balancing algorithm in data center fat-tree networks. GLB uses a path-related weight to select path. This weight indicates how balanced of a path. We implement GLB algorithm as a module in an openflow controller platform, POX. On the self-defined modified mininet emulation platform, we conduct experiments in a fat-tree topology environment running random traffic to generate performance data. Experiment results demonstrate that our proposed GLB algorithm outperforms DLB algorithm in terms of load balancing.

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