Resource Management Strategies in Heterogeneous Distributed Systems
Anila Gogineni · Journal of Artificial Intelligence Machine Learning and Data Science · 2024
This research targets workload partitioning for GPUs, TPUs and CPUs in heterogeneous distributed systems.This paper focuses on the issues connected with the heterogeneity of the hardware, balanced loads, latency of data transfer and energy consumption.It highlights the characteristics of proactive scheduling algorithms for resources, which enhance the system's efficiency and capability.These applications are selected in the areas of machine learning and artificial intelligence, big data and cloud computing and scientific computing and modelling to demonstrate how systems with heterogeneity can improve certain computational workloads.Finally, the study provides information on fault tolerance and cost-effectiveness as a way of finding efficient and effective ways of managing available resources for mixed-distributed environments.