Profiling over Adaptive Ranges

Modern computer systems are called on to deal with billions of events every second, whether they are instructions executed, memory locations accessed, or packets forwarded. This presents a serious challenge to those who seek to quantify, analyze, or optimize such systems, because important trends and behaviors may easily be lost in a sea of data. We present Range Adaptive Profiling (RAP) as a new and general purpose profiling method capable of hierarchically classifying streams of data efficiently in hardware. Through the use of RAP, events in an input stream are dynamically classified into increasingly precise categories based on the frequency with which they occur. The more important a class, or range of events, the more precisely it is quantified. Despite the dynamic nature of our technique, we build upon tight theoretic bounds covering both worst-case error as well as the required memory. In the limit, it is known that error and the memory bounds can be independent of the stream s...

• Type: Conference
• Year: 2006
• Where: CGO
• Authors: Shashidhar Mysore, Banit Agrawal, Timothy Sherwood, Nisheeth Shrivastava, Subhash Suri
• Keywords: CGO 2006, Purpose Profiling Method, Range Adaptive Profiling, Software Engineering, Tight Theoretic Bounds