Budget-Aware Scheduling Algorithms for Scientific
Workflows on IaaS Cloud Platforms
Author/Presenter
Event Type
Workshop
TimeMonday, November 13th3:40pm -
3:50pm
Location501
DescriptionThis 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.
Author/Presenter
