Load balancing algorithm in a virtualized cloud environment

In this work, an approach to the design of load balancing algorithm that dynamically allocates and reallocates resources using the Shortest Time Path in order to reduce CPU response time was proposed. To exemplify the approach, a system is presented which dynamically monitors the state of the VM, their utilisation capacities and eliminates idle VM and those that contribute too little to CPU utilisation and reallocate processes to VM to leverage on their maximum capacity. The African adage that says ‘too many cook spoils the soup’ was utilised to eliminate non-contributing VM to CPU utilisation. The performance of this algorithm was assessed by setting up two data centers using VMware v2.0.2 and VMware ESX v50.05. WebSphere V6 was used as the application server. Three PMs each having three VMs and 2GB of memory. Each VM had one core with 512MB RAM. ESX server had three PMs with four VMs. Each PM had 4GB of memory and each one core assigned 1GB RAM. The experimental results reveal that it helped to slightly reduce the CPU response time when servicing and reallocating resources as evident in the dynamic nature of VM activation and de-activation.

[1]  Zhen Xiao,et al.  Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Environment , 2013, IEEE Transactions on Parallel and Distributed Systems.

[2]  Gregor von Laszewski,et al.  Efficient resource management for Cloud computing environments , 2010, International Conference on Green Computing.

[3]  P. Mell,et al.  The NIST Definition of Cloud Computing , 2011 .

[4]  Rajkumar Buyya,et al.  Workflow Engine for Clouds , 2011, CloudCom 2011.

[5]  .K Dhanya,et al.  A Virtual Cloud Computing Provider for Mobile Devices , 2017 .

[6]  Anne M. Holler,et al.  Cloud Scale Resource Management: Challenges and Techniques , 2011, HotCloud.

[7]  Calton Pu,et al.  Intelligent management of virtualized resources for database systems in cloud environment , 2011, 2011 IEEE 27th International Conference on Data Engineering.

[8]  Rajkumar Buyya,et al.  Virtual Machine Provisioning Based on Analytical Performance and QoS in Cloud Computing Environments , 2011, 2011 International Conference on Parallel Processing.

[9]  Yahya Slimani,et al.  Load Balancing Approach for QoS Management of Multi-instance Applications in Clouds , 2013, 2013 International Conference on Cloud Computing and Big Data.

[10]  Yu Shyang Tan,et al.  'Time' for Cloud? Design and Implementation of a Time-Based Cloud Resource Management System , 2014, 2014 IEEE 7th International Conference on Cloud Computing.

[11]  Ji Li,et al.  An Greedy-Based Job Scheduling Algorithm in Cloud Computing , 2014, J. Softw..

[12]  Jianhua Gu,et al.  A New Resource Scheduling Strategy Based on Genetic Algorithm in Cloud Computing Environment , 2012, J. Comput..

[13]  Yongzhao Zhan,et al.  Virtualization and Cloud Computing , 2019, CompTIA® A+® Complete Practice Tests.

[14]  Robert L. Grossman,et al.  The Case for Cloud Computing , 2009, IT Professional.

[15]  Calton Pu,et al.  Dynamic monitoring, modeling and management of performance and resources for applications in the cloud , 2012 .

[16]  Baochun Li,et al.  Anchor: A Versatile and Efficient Framework for Resource Management in the Cloud , 2013, IEEE Transactions on Parallel and Distributed Systems.

[17]  Rajkumar Buyya,et al.  Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing , 2012, Future Gener. Comput. Syst..

[18]  N. P. Gopalan,et al.  Task Assignment for Heterogeneous Computing Problems using Improved Iterated Greedy Algorithm , 2014 .