Abstract
To efficiently handle a large volume of data, scheduling algorithms in stream processing systems need to minimise the data movement between communicating tasks to improve system throughput. However, finding an optimal scheduling algorithm for these systems is NP-hard. In this paper, we propose a heuristic scheduling algorithm - T3-Scheduler- for a heterogeneous fog or cloud cluster that can efficiently identify the tasks that communicate with each other and assign them to the same node, up to a specified level of utilisation for that node. Using three common micro-benchmarks and an evaluation using two real world applications, we demonstrate that T3-Scheduler outperforms current state-of-the-art scheduling algorithms, such as Aniello et al.'s popular 'Online scheduler' and R-Storm, improving throughput by up to 32% for the two real-world applications. (C) 2018 Elsevier B.V. All rights reserved.