As energy proportional computing has extended the success of DVFS (Dynamic
voltage and frequency scaling) to the entire system, DVFS control algorithms
will play a key role in reducing server clusters' power consumption. The focus
of this paper is to provide accurate cluster-level DVFS control for power
saving in a server cluster. To achieve this goal, we propose a request tracing
approach that online classifies the major causal path patterns and monitors
their performance data as a guide for accurate DVFS control. The request
tracing approach significantly decreases the time cost of performance profiling
experiments that aim to establish the empirical performance model. Moreover, it
decreases the controller complexity so that we can introduce a much simpler
feedback controller, which only relies on the single-node DVFS modulation at a
time as opposed to varying multiple CPU frequencies simultaneously. Based on
the request tracing approach, we present a hybrid DVFS control system that
combines an empirical performance model for fast modulation at different load
levels and a simpler feedback controller for adaption. We implement a prototype
of the proposed system, called PowerTracer, and conduct extensive experiments
on a 3-tier platform. Our experimental results show that PowerTracer
outperforms its peer in terms of power saving and system performance.