Abstract: Today's data centers face the issue of
balancing electricity use and completion times of
their workloads. Rising electricity costs are
forcing data center operators to either operate
within an electricity budget or to reduce
electricity use as much as possible while still
maintaining service agreements. Energy-aware
resource allocation is one technique a system
administrator can employ to address both problems:
optimizing the workload completion time (makespan)
when given an energy budget, or to minimize energy
consumption subject to service guarantees (such as
adhering to deadlines). In this thesis, the
problem of energy-aware static resource allocation
in an environment where a collection of
independent (non-communicating) tasks ("bag-of-
tasks") is assigned to a heterogeneous computing
system is studied. Computing systems often operate
in environments where task execution times vary
(e.g., due to cache misses or data dependent
execution times). These execution times are
modeled stochastically, using probability density
functions. It is desirable for resource
allocations to be robust against these variations,
where energy-robustness is defined as the
probability that the energy budget is not
violated, and makespan-robustness is defined as
the probability a makespan deadline is not
violated. For both energy-constrained and
deadline-constrained problems, novel heuristics
are designed and analyzed.
The rapid increase of the power consumption
of data centers has led to an increase in the
amount cooling resources required to operate these
data centers at a safe threshold. The cooling
systems account for a large portion of the total
power consumption used by a data center, causing
the costs of providing power to these data centers
to rise. In this thesis, novel resource allocation
techniques that maximize the performance of a data
center when constrained to the total power
consumption of the compute servers and Computer
Room Air Conditioning (CRAC) units in addition to
ensuring servers and CRAC units operate within a
redline temperature threshold are designed. As
multicore processors increase in number of cores,
the effects of shared caches can have a pronounced
impact on the execution speed of memory-intensive
tasks. In our model, we consider the power
consumed by compute servers and CRAC units, a
workload with tasks of varying compute and memory
intensity that affects the power consumption of
cores, and the execution speed degradation effects
caused by co-locating tasks to cores within the
same multicore processor. The performance of the
system is quantified as the total reward earned
from completing tasks by their individual
deadlines. A novel genetic algorithm technique, in combination with a new local search technique that guarantees the power and thermal constraints, to solve this problem is being designed and analyzed. The plan is to consider other techniques, as well.
Adviser: H.J. Siegel Co-Adviser: Sudeep Pasricha Non-ECE Member: Darrell Whitley Member 3: Anthony A. Maciejewski Addional Members: N/A
Publications: Mark Oxley, Sudeep Pasricha, Howard Jay Siegel, and Anthony A. Maciejewski, “Energy and
Deadline Constrained Robust Stochastic Static Resource Allocation,” The First Workshop on
Power and Energy Aspects of Computation (PEAC 2013), in the proceedings of the 10th International Conference on Parallel Processing and Applied Mathematics (PPAM 2013), to appear, 10
pp., Warsaw, Poland, Sep. 2013.
Program of Study: ECE514 ECE561 ECE520 ECE554 ECE666 CS545 MATH510 MATH560
