Recognizing End-User Transactions in Performance Management

Abstract

Providing good quality of service (e.g., low response times) in distributed computer systems requires measuring end-user perceptions of performance. Unfortunately, in practice such measures are often expensive or impossible to obtain. Herein, we propose a machine learning approach to recognizing end-user transactions consisting of sequences of remote procedure calls (RPCs) received at a server. Two problems are addressed. The first is labeling previously segmented transaction instances with the correct transaction type. This is akin to work done in document classification. The second problem is segmenting RPC sequences into transaction instances. This is a more difficult problem, but it is similar to segmenting sounds into words as in speech understanding. Using Naive Bayes, we tackle the labeling problem with four combinations of feature vectors and probability distributions: RPC occurrences with the Bernoulli distribution and RPC counts with the multinomial, geometric, and shifted ge..