Dynamic matching and scheduling of a class of independent tasks onto heterogeneous computing systems

TLDR

Dynamic mapping (matching and scheduling) heuristics for independent tasks on heterogeneous distributed computing systems are studied. The study introduces three new heuristics—one batch and two online—for this mapping problem. The authors evaluate five online and three batch heuristics, including the new ones, via simulation, considering task affinity, machine ready times, and task aging. Simulation results show that the optimal heuristic depends on task‑machine heterogeneity, optimization goals, and task arrival rate.

Abstract

Dynamic mapping (matching and scheduling) heuristics for a class of independent tasks using heterogeneous distributed computing systems are studied. Two types of mapping heuristics are considered: on-line and batch mode heuristics. Three new heuristics, one for batch and two for on-line, are introduced as part of this research. Simulation studies are performed to compare these heuristics with some existing ones. In total, five on-line heuristics and three batch heuristics are examined. The on-line heuristics consider; to varying degrees and in different ways, task affinity for different machines and machine ready times. The batch heuristics consider these factors, as well as aging of tasks waiting to execute. The simulation results reveal that the choice of mapping heuristic depends on parameters such as: (a) the structure of the heterogeneity among tasks and machines, (b) the optimization requirements, and (c) the arrival rate of the tasks.