Benchmarks for Job Scheduling in Ultra-Distributed Systems.

As the number of edge devices rapidly multiplies into the millions in the post-5G era, there is a simultaneous surge in demand for job execution driven by the data generated by these devices. Ultra-distributed computing systems have emerged to support this notable proliferation of interconnected devices and in addressing the unprecedented data generated over low-latency networks. To ensure the high availability and reliability of these systems, an efficient job scheduling method is required to schedule jobs across available resources. However, there are currently no large-scale benchmarks available to evaluate job scheduling methods. This paper introduces large-scale benchmarks of up to one million devices for job shop scheduling problems. We evaluate the makespan reduction of widely used combinatorial optimizations on these benchmarks, including simulated annealing, ant colony, tree Parzen search, particle swarm, artificial bee colony, cuckoo search, whale, grey wolf, firefly, and bat optimizations. We investigate the impact of execution time on finding the optimal makespan of job shop scheduling for each method. The experimental results can be used to guide the selection of a job scheduling method for particular applications.