Comparing Task Graph Scheduling Algorithms: An Adversarial Approach
arxiv(2024)
摘要
Scheduling a task graph representing an application over a heterogeneous
network of computers is a fundamental problem in distributed computing. It is
known to be not only NP-hard but also not polynomial-time approximable within a
constant factor. As a result, many heuristic algorithms have been proposed over
the past few decades. Yet it remains largely unclear how these algorithms
compare to each other in terms of the quality of schedules they produce. We
identify gaps in the traditional benchmarking approach to comparing task
scheduling algorithms and propose a simulated annealing-based adversarial
analysis approach called PISA to help address them. We also introduce SAGA, a
new open-source library for comparing task scheduling algorithms. We use SAGA
to benchmark 15 algorithms on 16 datasets and PISA to compare the algorithms in
a pairwise manner. Algorithms that appear to perform similarly on benchmarking
datasets are shown to perform very differently on adversarially chosen problem
instances. Interestingly, the results indicate that this is true even when the
adversarial search is constrained to selecting among well-structured,
application-specific problem instances. This work represents an important step
towards a more general understanding of the performance boundaries between task
scheduling algorithms on different families of problem instances.
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