We propose a linear regression method and a maximum likelihood technique for estimating the service demands of requests based on measurement of their response times instead of their CPU utilization. Our approach does not require server instrumentation or sampling, thus simplifying the parameterization of performance models. The benefit of this approach is further highlighted when utilization measurement is difficult or unreliable, such as in virtualized systems or for services controlled by third parties. Both experimental results from an industrial ERP system and sensitivity analyses based on simulations indicate that the proposed methods are often much more effective for service demand estimation than popular utilization based linear regression methods. In particular, the maximum likelihood approach is found to be typically two to five times more accurate than utilization based regression, thus suggesting that estimating service demands from response times can help in improving performance model parameterization. * Stephan Kraft is also affiliated with Queen'
In this paper, we propose simple performance models to predict the impact of consolidation on the storage I/O performance of virtualized applications. We use a measurement-based approach based on tools such as blktrace and tshark for storage workload characterization in a commercial virtualized solution, namely VMware ESX server. Our approach allows a distinct characterization of read/write performance attributes on a per request level and provides valuable information for parameterization of storage I/O performance models. In particular, based on measures of quantities such as the mean queue-length seen upon arrival by requests, we define simple linear prediction models for the throughput, response times, and mix of read/write requests in consolidation based only on information collected in isolation experiments for the individual virtual machines.
We propose simple models to predict the performance degradation of disk requests due to storage device contention in consolidated virtualized environments. Model parameters can be deduced from measurements obtained inside Virtual Machines (VMs) from a system where a single VM accesses a remote storage server. The parameterized model can then be used to predict the effect of storage contention when multiple VMs are consolidated on the same server. We first propose a trace-driven approach that evaluates a queueing network with fair share scheduling using simulation. The model parameters consider Virtual Machine Monitor level disk access optimizations and rely on a calibration technique. We further present a measurement-based approach that allows a distinct characterization of read/write performance attributes. In particular, we define simple linear prediction models for I/O request mean response times, throughputs and read/write mixes, as well as a simulation model for predicting response time distributions. We found
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