Task stalling for a batch of task makespan minimisation in heterogeneous multigrid computing

Abstract

This paper presents a new algorithm for a batch of task makespan minimisation in heterogeneous multigrid computing. Heterogeneous grids are known to cause straggling task problem that increases task execution makespan. Existing task distribution algorithms solve this problem by using information about the compute node capacities or task sizes. However, such information may not always be available. Task stalling solves both problems. However, this method is described for queuing systems consisting of only two heterogeneous servers or grids. Our proposed algorithm is based on an improved task stalling method, allowing it to distribute tasks in systems consisting of two or more grids. Experiment results show reduced task execution makespan by up to 19,92% compared to FIFO. This allows us to conclude that the new algorithm is suitable for a batch of task makespan minimisation in heterogeneous multigrid computing.

Application of a Task Stalling Buffer in Distributed Hybrid Cloud Computing

“…However, existing well-known hierarchical and non-hierarchical task scheduling algorithms, reviewed in Section IV, cannot balance the workload without any additional information about the tasks (such as task size, quantity, and incoming task rate). As reviewed in [5], existing hybrid distributed computing platforms ( [6]- [18]) require preliminary data on the number of tasks to be performed, the execution time for each task, or the number of computing resources available. A task execution schedule is then created using these data.…”