A Novel Resource Scheduler for Resource Allocation and Scheduling in Big Data Using Hybrid Optimization Algorithm at Cloud Environment

Abstract

Big Medical Data (BMD) is generated by cellular telephones, clinics, academics, suppliers, and organizations. Collecting, finding, analyzing, and managing the big data to make people's lives better, comprehending novel illnesses, and treatments, predicting results at initial phases, and making real-time choices are the actual issues in healthcare systems. Dealing with big medical data in resource scheduling is a major issue that aims to offer higher quality healthcare services. Hadoop MapReduce has been widely used for parallel processing of large data tasks and efficient job scheduling. The number of big data tasks is constantly growing; it is becoming more essential to minimize their energy usage to reduce the environmental effect and operating expenses. Hence to overcome these disadvantages, we propose a novel resource scheduler for big data using a Hybrid 2-GW Optimization Algorithm (H2-GWOA). We employ the Improved GlowWorm Swarm Optimization Algorithm (IGSOA) and Mean GreyWolf Optimization Algorithm (MGWOA) for optimizing the MapReduce framework in heterogeneous big data. The CloudSim platform was used for the simulations. The performance of the proposed scheduler is proved to be better than the conventional methods in terms of metrics like latency, makespan, resource utilization, skewness, and Central Processing Unit (CPU) consumption.