Abstract. Response time predictions for workload on new server architectures can enhance Service Level Agreement–based resource management. This paper evaluates three performance prediction methods using a distributed enterprise application benchmark. The historical method makes predictions by extrapolating from previously gathered performance data, while the layered queuing method makes predictions by solving layered queuing networks. The hybrid method combines these two approaches, using a layered queuing model to generate the data for a historical model. The methods are evaluated in terms of: the systems that can be modelled; the metrics that can be predicted; the ease with which the models can be created and the level of expertise required; the overheads of recalibrating a model; and the delay when evaluating a prediction. The paper also investigates how a prediction-enhanced resource management algorithm can be tuned so as to compensate for predictive inaccuracy and balance the costs of SLA violations and server usage.

Abstract—It is essential that a monitoring system is being designed with performance and scalability in mind. But due to the diversity and complexity of both the monitoring and the monitored systems, it is currently difficult to reason on both performance and scalability using ad hoc techniques. Thus, both simulation is required and analytical models based on well established techniques such as queueing theory have to be developed. In this paper we provide an analytical modeling of the behaviour of the commonly used manager-agent monitoring frameworks within two scenarios: single manager-single agent and single manager-multiple agents. The two designed models enable the automation of the estimation of the scalability limit of the two types of management monitoring schemes regarding a performance metric like the monitoring delay. We validate our developed models through simulation based on parameters values obtained from the performance measurement of a life running JMX-based monitoring applications with the two scenarios. Index Terms—Monitoring, Scalability, Modeling techniques I.

published in Parallel Computing:

Abstract not found