A Workload and Machine Categorization-Based Resource Allocation Framework for Load Balancing and Balanced Resource Utilization in the Cloud

This paper proposes a workload and machine categorization based resource allocation framework for balancing the load across active physical machines as well as utilizing their different resource capacities in a balanced manner. The workload, essentially independent and non-preemptive tasks are allocated resources on the physical machines whose resource availability complements the resource requirement of tasks. Simulation based experiments are performed using CloudSim simulator to execute three different set of tasks comprising 10000, 20000, and 30000 number of tasks. The metric of load imbalance across active physical machines and the metric of utilization imbalance among their considered resource capacities (i.e., CPU and RAM) are measured in different scheduling cycles of a simulation run. Simulation results show that the proposed resource allocation method outperforms the compared methods in terms of balancing the load across active physical machines and utilizing their different resource capacities in a balanced manner.