Recognizing End-User Transactions in Performance Management

Providing good quality of service (e.g., low response times) in distributed computer systems requires measuring enduser perceptions of performance. Unfortunately, such measures are often expensive or impossible to obtain. Herein, we propose a machine-learning approach to recognizing enduser transactions consisting of sequences of remote procedure calls (RPCs) received at a server. Two problems are addressed. The first problem is labeling an RPC sequence that corresponds to one transaction instance with the correct transaction type. This is akin to text classification. The second problem is transaction recognition, a more comprehensive task that involves segmenting RPC sequences into transaction instances and labeling those instances with transaction types. This problem is similar to segmenting sounds into words as in speech understanding. Using Naive Bayes approach, we tackle the labeling problem with four combinations of feature vectors and probability distributions: RPC occurrences ...

• Type: Conference
• Year: 2000
• Where: AAAI
• Authors: Joseph L. Hellerstein, T. S. Jayram, Irina Rish
• Keywords: AAAI 2000, Intelligent Agents, Rpc Sequence, Transaction Instances, Transaction Recognition