Abstract—In this paper, we propose a model to predict
resource utilization matrix for a given workload by mining the
information residing in application as well as system logs for
resource utilization. Unlike regression based or queuing
network based approaches, our mechanism neither requires
estimating per-function resource utilization nor does it require
to benchmark individual business transactions in order to
derive resource utilization matrix for the desired workload. In
our experimental analysis, we have tried to predict the
utilization of server resources like cpu, memory, disk and
network usage based on several workload pattern. Across all
experiments, we find the average absolute error in predicting
utilization of all resources was less than 6%. This model
becomes particularly helpful in the scenario where there are
only few data-points available for system running with light
workload and it is essential to analyze the impact of any change
in workload pattern demanding heavy resource usage. Our
model is not only useful for resource provisioning and what-if
analysis to assess the impact of any workload change but also
can be used for bottleneck analysis and early alert generating
engine.
Index Terms—Resource utilization, system demand,
performance analytics, workload analysis.
Mehul Vora is with Innovation Labs, PERC, TATA Consultancy Services
(TCS) Ltd., Mumbai, India (e-mail: mehul.vora@tcs.com).
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Cite:Mehul Nalin Vora, "Predicting Utilization of Server Resources from Log Data," International Journal of Computer Theory and Engineering vol. 6, no. 2, pp. 118-123, 2014.
