For those of you who are interested, here's my PhD thesis:
Adaptive Scheduling in Heterogeneous Distributed Computing Systems.
Abstract
The main focus of this research is in the area of adaptive scheduling for heterogeneous
distributed systems. Given an unreliable, non-dedicated set of
processing and communication resources, a scheduler is required to allocate
tasks to processors. No information about the state of the system, which
can vary over time, or the tasks to be processed, is known in advance and
thus must be estimated dynamically. Current schedulers do not adequately
address this dynamism. To address this, a property estimation method is
presented, which utilizes a k-Nearest Neighbours algorithm, a smoothed average
and an analytical benchmark. These estimated properties are then
used by two different scheduling techniques, which make less restrictive assumptions
than the current state-of-the-art methods. A multi-heuristic evolutionary
method utilizes a genetic algorithm and eight simple heuristics to
efficiently allocate tasks to processors. A deterministic method utilizes the
error inherent in estimating the properties of the system and the execution
time of tasks, to allocate tasks to processors. The algorithms have been
implemented on a real-world heterogeneous distributed system with up to
150 processors. A set of real-world problems from the areas of cryptography,
bioinformatics, and biomedical engineering were used as a test set to measure
the effectiveness of the scheduling algorithms. Experiments have shown that
both methods achieve better efficiency than other state-of-the-art heuristic
algorithms. Finally, a low memory distributed reconstruction application for
large digital holograms is presented, which has significantly increased the size
of holograms that can be reconstructed, over the previous state-of-the-art.
