SC17 Denver, CO

Budget-Aware Scheduling Algorithms for Scientific Workflows on IaaS Cloud Platforms

Workshop: WORKS 2017 (12th Workshop on Workflows in Support of Large-Scale Science)
Authors: Yves Robert (ENS Lyon, University of Tennessee)

Abstract: This paper introduces several budget-aware algorithms to deploy scientific workflows on IaaS cloud platforms, where users can request Virtual Machines (VMs) of different types, each with specific cost and speed parameters. We use a realistic application/platform model with stochastic task weights, and VMs communicating through a datacenter. We extend two well-known algorithms, HEFT and MinMin, and make scheduling decisions based upon machine availability and available budget. During the mapping process, the budget-aware algorithms make conservative assumptions to avoid exceeding the initial budget; we further improve our results with refined versions that aim at re-scheduling some tasks onto faster VMs, thereby spending any budget fraction leftover by the first allocation. These refined variants are much more time-consuming than the former algorithms, so there is a trade-off to find in terms of scalability. We report an extensive set of simulations with workflows from the Pegasus benchmark suite. Budget-aware algorithms generally succeed in achieving efficient makespans while enforcing the given budget, and despite the uncertainty in task weights.

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