Adaptive Scheduling Using Support Vector Machine on Heterogeneous Distributed Systems
Yong-Won Park · 2011
Since the advent of the modern von Neumann computer in the 1940s, startling advances have been made in computing technology with the creation of innovative, reliable, and faster electronic components, from vacuum tubes to transistors. Computing power has risen exponentially over relatively brief periods of time as Moore’s law projected a salient trend for the growth in memory-chip performance, estimating the capacity of the integrated circuit to double every 18 months. Although these developments were essential in solving computationally intensive problems, faster devices were not the sole contributing factor to high performance computing. Since electronic processing speeds began to approach limitations imposed by the laws of physics, it became evident that the performance of uniprocessor computers was limited. This has led to the prominent rise of parallel and distributed computing. Such systems could be homogeneous or heterogeneous. In the past decade homogeneous computing has solved large computationally intensive problem by harnessing a multitude of computers via a high-speed network. However, as the amount of homogeneous parallelism in applications decreases, the homogeneous system cannot offer the desired speedups. Therefore, heterogeneous architectures to exploit the heterogeneity in computations came to be a critical research issue. In heterogeneous computing (HC) systems consisting of a multitude of autonomous computers, a mechanism that can harness these computing resources efficiently is essential to maximize system performance. This is especially true in mapping tasks to heterogeneous computers according to the task computation type, so as to maximize the benefits from the heterogeneous architecture. The general problem of mapping tasks onto machines is known to be NP-complete, as such, many good heuristics have been developed. However, the performance of most heuristics is susceptible to the dynamic environment, and affected by various system variables.