Optimal Kernel-based Extreme Learning and Multi-objective Function-aided Task Scheduling for Solving Load Balancing Problems in Cloud Environment

Workload balancing in cloud computing is not yet resolved, particularly considering Infrastructure as a Service (IaaS) in the cloud network. The problem of being underloaded or overloaded should not occur at the time of the server or host accessing the cloud which may lead to create system crash problem. Thus, to resolve these existing problems, an efficient task scheduling algorithm is required for distributing the tasks over the entire feasible resources, which is termed load balancing. The load balancing approach assures that the entire Virtual Machines (VMs) are utilized appropriately. So, it is highly essential to develop a load-balancing model in a cloud environment based on machine learning and optimization strategies. Here, the computing and networking data is utilized for the analysis to observe the traffic as well as performance patterns. The acquired data is offered to the machine learning decision to select the right server by predicting the performance effectively by employing an Optimal Kernel-based Extreme Learning Machine (OK-ELM) and their parameter is tuned by the developed hybrid approach Population Size-based Mud Ring Tunicate Swarm Algorithm (PS-MRTSA). Further, effective scheduling is performed to resolve the load balancing issues by employing the developed model MR-TSA. Here, the developed approach effectively resolves the multi-objective constraints such as Response time, Resource cost, and energy consumption. Thus, the recommended load balancing model securesan enhanced performance rate than the traditional approaches over several experimental analyses.