This talk is a dry run talk. The speaker, Trilce Estrada, will present the paper at the ACM CF-2008 in Italy on May 7. More information about the paper:
Title: A Distributed Evolutionary Method to Design Scheduling Policies for Volunteer Computing
Auhtors: Trilce Estrada (1), Olac Fuentes (2), and Michela Taufer (1)
Affiliations:
(1)Department of Computer & Information Sciences, University of Delaware
(2)Department of Computer of Science, The University of Texas at El Paso
Abstract:
Volunteer Computing (VC) is a paradigm that uses idle cycles from
computing resources donated by volunteers and connected through the
Internet to compute large-scale, loosely-coupled simulations. A big
challenge in VC projects is the scheduling of work-units across
heterogeneous, volatile, and error-prone computers. The design of
effective scheduling policies for VC projects involves subjective and
time-demanding tuning that is driven by the knowledge of the project
designer. VC projects are in need of a faster and project-independent
method to automate the scheduling design.
To automatically generate a scheduling policy, we must explore the
extremely large space of syntactically valid policies. Given the size
of this search space, exhaustive search is not feasible. Thus in this
paper we propose to solve the problem using an evolutionary method to
automatically generate a set of scheduling policies that are
project-independent, minimize errors, and maximize throughput in VC
projects. Our method includes a genetic algorithm where the
representation of individuals, the fitness function, and the genetic
operators are specifically tailored to get effective policies in a
short time. The effectiveness of our method is evaluated with SimBA,
a Simulator of BOINC Applications. Contrary to manually-designed
scheduling policies that often perform well only for the specific
project they were designed for and require months of tuning, our
resulting scheduling policies provide better overall throughput across
the different VC projects considered in this work and were generated
by our method in a time window of one week.