Walter Scott, Jr. College of Engineering

Graduate Exam Abstract

Kyle Tarplee
Ph.D. Final
Apr 16, 2015, 9:00 am - 11:00 am
ECE Conference Room
Highly Scalable Algorithms For Scheduling Tasks and Provisioning Machines on Heterogeneous Computing Systems
Abstract: As high performance computing systems
increase in size, new and more efficient
algorithms are needed to schedule work
on the machines, understand the
performance trade-offs inherit in the
system, and determine which machines
to provision. The extreme scale of these
newer systems requires unique task
scheduling algorithms that are capable of
handling millions of tasks and thousands
of machines. A highly scalable
scheduling algorithm is developed that
computes high quality schedules,
especially for large problem sizes.
Large-scale computing systems also
consume vast amounts of electricity,
leading to high operating costs. Through
the use of novel resource allocation
techniques, system administrators can
examine this trade-off space to quantify
how much a given performance level will
cost in electricity, or see what kind of
performance that can be expected when
given an energy budget. Trading-off
energy and makespan is often difficult for
companies because it is unclear how
each affects the profit. A monetary-
based model of high performance
computing is presented and a highly
scalable algorithm is developed to
quickly find the schedule that maximizes
the profit per unit time. Cloud computing
has made it possible to provision
machines that are best suited to a
particular workload. An algorithm is
designed to find the best set of
computing resources to allocate to the
workload that takes into account the
uncertainty in the task arrival rates, task
execution times, and power
consumption. Reward rate, cost, failure
rate, and power consumption can be
optimized, as desired, to optimally trade-
off these conflicting objectives.
Adviser: Anthony A. Maciejewski
Co-Adviser:
Non-ECE Member: Dan Bates, Mathematics
Member 3: Howard Jay Siegel
Addional Members: Edwin Chong
Publications:
Efficient and Scalable Computation of the Energy and Makespan Pareto Front for Heterogeneous Computing Systems.
Kyle M. Tarplee, Ryan Friese, Anthony A. Maciejewski, and Howard Jay Siegel,
6th Workshop on Computational Optimization (WCO 2013), in the proceedings of the Federated Conference on Computer Science and Information Systems (FedCSIS 2013), cosponsors: Polskie Towaszystwo Informatycznq (PTI), iBS PAN, AGH University of Science and Technology, and Wroclaw University of Economics (UE), Krakow, Poland, Sep. 2013.

Energy-Aware Profit Maximizing Scheduling Algorithm for Heterogeneous Computing Systems.
Kyle M. Tarplee, Anthony A. Maciejewski, and Howard Jay Siegel,
Extreme Green and Energy Efficiency in Large Scale Distributed Systems Workshop (ExtremeGreen 2014), cosponsors: IEEE Computer Society and the ACM, in the proceedings of the 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid 2014), Chicago, IL, May 2014

Efficient and Scalable Pareto Front Generation for Energy and Makespan in Heterogeneous Computing Systems.
Kyle M. Tarplee, Ryan Friese, Anthony A. Maciejewski, and Howard Jay Siegel,
in Recent Advances in Computational Optimization, Studies in Computational Intelligence Series, Springer, 2015

Kyle M. Tarplee, Ryan Friese, Anthony A. Maciejewski, and Howard Jay Siegel. Scalable linear programming based resource allocation for makespan minimization in heterogeneous computing systems. under journal review, Submitted 2014.

Kyle M. Tarplee, Ryan Friese, Anthony A. Maciejewski, and Howard Jay Siegel. Energy and makespan tradeoffs in heterogeneous computing systems using efficient linear programming techniques. under journal review, 2014.

Kyle M. Tarplee, Anthony A. Maciejewski, and Howard Jay Siegel. Robust performance-based resource provisioning using a steady state model for multi-objective stochastic programming. under journal review, 2015
Program of Study:
ECE 501
ECE 516
ECE 555
ECE 658
ECE 666
MATH 510