Cloud task scheduling using enhanced sunflower optimization algorithm
Hojjat Emami
Abstract
The objective of cloud task scheduling is to partition tasks on shared resources to minimize energy consumption and makespan. Recently, several meta-heuristics for task scheduling were proposed and achieved encouraging results. However, their performance is far from the ideal state and needs more improvement. This paper introduces an enhanced sunflower optimization (ESFO) algorithm for improving the performance of existing task scheduling. It finds optimal scheduling in a polynomial time. The experiments show that ESFO outperformed its counterparts. The amount of improvement in comparison with the best counterpart is 0.73% and 2.24% respectively in terms of makespan and energy consumption.
Topics & Concepts
Job shop schedulingComputer scienceHeuristicsRate-monotonic schedulingFair-share schedulingFixed-priority pre-emptive schedulingMathematical optimizationDistributed computingScheduling (production processes)Dynamic priority schedulingTwo-level schedulingCloud computingMathematicsEmbedded systemComputer networkOperating systemRouting (electronic design automation)Quality of serviceCloud Computing and Resource ManagementIoT and Edge/Fog ComputingDistributed and Parallel Computing Systems