A Comparison of Autonomic Decision Making Techniques
Name
MIT-CSAIL-TR-2011-019.pdf
Size
430.04 KB
Format
Adobe PDF
Checksum (MD5)
f5df84f4360ef6640dffb6e7cc9fdcdb
Author(s)
Maggio, Martina
Hoffmann, Henry
Santambrogio, Marco D.
Agarwal, Anant
Leva, Alberto
Advisor(s)
Anant Agarwal
Date Issued
April 1, 2011
Series/Report no.
MIT-CSAIL-TR-2011-019
Abstract
Autonomic computing systems are capable of adapting their behavior and resources thousands of times a second to automatically decide the best way to accomplish a given goal despite changing environmental conditions and demands. Different decision mechanisms are considered in the literature, but in the vast majority of the cases a single technique is applied to a given instance of the problem. This paper proposes a comparison of some state of the art approaches for decision making, applied to a self-optimizing autonomic system that allocates resources to a software application, which provides direct performance feedback at runtime. The Application Heartbeats framework is used to provide the sensor data (feedback), and a variety of decision mechanisms, from heuristics to control-theory and machine learning, are investigated. The results obtained with these solutions are compared by means of case studies using standard benchmarks.
Persistent DSpace Link
