Virtual machine placement in energy aware load balancer using fog classifier

• Published in: Journal of cloud computing : advances, systems and applications Vol. 12; no. 1; pp. 180 - 10
• Main Authors: Selvaganapathy, S., Chinnadurai, M.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2023; Springer Nature B.V; SpringerOpen
• Subjects: Algorithms; Belief networks; Classifiers; Cloud computing; Computer Communication Networks; Computer Science; Computer System Implementation; Computer Systems Organization and Communication Networks; Energy efficiency; Fog classifier; Information Systems Applications (incl.Internet); Load balancer; Load balancing; Placement; Software Engineering/Programming and Operating Systems; Special Purpose and Application-Based Systems; Virtual environments; Virtual machine; Workstations; Energy efficiency; Placement; Virtual machine; Fog classifier; Load balancer
• ISSN: 2192-113X; 2192-113X
• DOI: 10.1186/s13677-023-00559-8

Cloud datacenter carries huge volume of data and tasks which is allocating resources to multiple workstations. Most of the cloud services are operating service level agreement (SLA) placements. During execution datacenter emits carbon and makes the energy. So operation cost always consideration fact. We need to address this challenge by using energy aware load balancer. This load balancer can be fixed in Virtual Machines (VM) and Classifier is required for selecting VMs. Employing the VMs is very important factor so fog enabled services is required for distributed geo physical load balancer with energy efficiency. In this paper we propose offloading VM services and Fog classifier for load balancing the cloud services. Placing the VM from one host to another we use Host Load Balancer with Energy Aware placement algorithm. In this case dynamical cloud environment can be tested and compare the host results. This is empirical approach for place the VMs without compromising the users. The simulations are done by using CloudSim and TensorFlow is used of generating deep belief network model for preparing VM placement. Our proposed method achieves 96% energy efficiency with minimum migration cost. The results are compared with existing placement methods based on active host availability.