Automatic Request Categorization in Internet Services Abhishek B. Sharma , Ranjita Bhagwan , Monojit Choudhury , Leana Golubchik , Ramesh Govindan and Geoffrey M. Voelker§ Microsoft Research, India University of Southern California § University of California, San Diego ABSTRACT Modeling system performance and workload characteristics has become essential for e ciently provisioning Internet services and for accurately predicting future resource requirements on anticipated workloads. The accuracy of these models bene ts substantially by di erentiating among categories of requests based on their resource usage characteristics. However, categorizing requests and their resource demands often requires signi cantly more monitoring infrastructure. In this paper, we describe a method to automatically di erentiate and categorize requests without requiring sophisticated monitoring techniques. Using machine learning, our method requires only aggregate measures such as total number of requests and the total CPU and network demands, and does not assume prior knowledge of request categories or their individual resource demands. We explore the feasibility of our method on the .Net PetShop 4.0 benchmark application, and show that it works well while being lightweight, generic, and easily deployable. 1. INTRODUCTION As Internet and enterprise hosting services have evolved from single-host platforms to large data
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Automatic request categorization in internet services
Sharma, Abhishek B.; Bhagwan, Ranjita; Choudhury, Monojit; Golubchik, Leana; Govindan, Ramesh; Voelker, Geoffrey M.
Follow ACM SIGMETRICS Performance Evaluation Review
, Volume 36 (2)
Association for Computing Machinery – Aug 31, 2008
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