Exact(2)
In this paper, the authors propose a method to characterize and predict workloads in cloud environments in order to efficiently provision cloud resources.
This approach can be useful to predict and simulate the allocation of virtual machines on hosts and, thus, data centers on the basis of real business configuration behaviour for days, weeks, months, etc. (for example, to predict workloads).
Similar(58)
Other characterisation approaches aim to predict workload resource utilisation, by identifying a feature of the workload.
In contrast, proactive approaches predict workload utilisation and switch nodes to sleep state at the predicted time intervals.
In addition, a Kalman filtering based approach is employed to predict workload in the next control period for which voltage-frequency pairs must be selected.
In contrast, the authors in [35] used a second order ARMA model, taking into account two previous control intervals to predict workload performance, by using response time as the performance metric.
The goal of this preliminary analysis was to find the best model based on the first and last day of stay to predict workload.
Jeff Greenberg, a Ford senior technical leader, said in an interview that his research team had built models that predicted workload based on information from data points relative to time.
Yes No [125] Predicting workload to enable workload consolidation while meeting SLAs.
The proposed approach is shown to achieve high accuracy for predicting workload and resource usages.
The algorithm determines whether new resources need to be acquired or not, based on predicted workload.
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