##
Using Bandwidth Data to Make Computation Offloading Decisions

Rich Wolski, Selim Gurun, Chandra Krintz, and Dan Nurmi

High-Performance Grid Computing Workshop (at IPDPS)
April 14-18, 2008

### Abstract:

We present a framework for making computation offloading
decisions in computational grid settings in which schedulers determine when to
move parts of a computation to more capable resources to improve performance.
Such schedulers must predict when an offloaded computation will outperform
one that is local by forecasting the local cost (execution time for computing
locally) and remote cost (execution time for computing remotely and transmission
time for the input/output of the computation to/from the remote system).
Typically, this decision amounts to predicting the bandwidth between
the local and remote systems to estimate these costs.
Our framework unifies such decision models
by formulating the problem as a statistical decision problem that can either
be
treated ``classically'' or using a Bayesian approach.
Using an implementation of this framework,
we evaluate the efficacy of a number of different decision strategies
(several of which have been employed by previous systems). Our results
indicate that a Bayesian approach employing automatic change-point detection
when estimating the *prior* distribution is the best-performing approach.