Proactive and dynamic load balancing model for workload spike detection in cloud

Abstract

Dynamic Load Balancing (DLB) has been an ever-green research topic since the beginning of cloud computing. DLB model distributes the available cloud resources (i.e., CPU, RAM, Storage, BW, etc.) among the VMs using a systematic approach to ensure efficient resource utilization. Overloading, an abnormal condition, occurs when the host encounters a resource shortage due to the high workload of Federated Learning. VM migration and consolidation are reliable load-balancing techniques to fix dynamic overloading issues. As part of the migration, the oversubscribed host VMs are moved to the other hosts till the load is regulated. Besides the high workload, the sudden spike in workload also leads the hosts to instant overloading. The former DLB models are efficient in handling general overloading conditions. Still, they are now suffering from limitations in taking the spike overloading due to 1) a sudden spike in workloads, 2) instant overloading, 3) recurrent migrations, and 4) SLA violations. To address these limitations in handling spike overloading, in this paper, we proposed a proactive and dynamic load balancing model (DLBM), which is designed to handle spike overloading with optimal solutions. The offered dynamic load balancing model with a spike detection algorithm helps detect workload spikes in advance to prevent the hosts from overloading and SLA violations. Experimental conducted using the Cloudsim tool and the planet lab workload dataset prove that the proposed DLBM identified the spikes in workload and efficiently controlled the recurrent migrations.