1. What are the contributions mentioned in the paper "Energy prediction for cloud workload patterns" ?
The excessive use of energy consumption in Cloud infrastructures has become one of the major cost factors for Cloud providers to maintain.. This paper introduces an energy-aware profiling model to identify energy consumption for heterogeneous and homogeneous VMs running on the same PM and presents an energy-aware prediction framework to forecast future VMs energy consumption.. This framework first predicts the VMs ’ workload based on historical workload patterns using Autoregressive Integrated Moving Average ( ARIMA ) model.. The predicted VM workload is then correlated to the physical resources within this framework in order to get the predicted VM energy consumption.. Compared with actual results obtained in a real Cloud testbed, the predicted results show that this energyaware prediction framework can get up to 2.
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2. What have the authors stated for future works in "Energy prediction for cloud workload patterns" ?
In future work, the authors aim to facilitate the proposed prediction framework and make an online modeller on the Leeds testbed to make the prediction process dynamic.. Also, the authors will consider the scalability aspects with different prediction scenarios to further show the capability of the proposed work, like predicting the energy usage for a number of VMs to be run on a single or multiple PMs already hosting other running VMs, and predicting the energy usage for these VMs to run all together.. With the evolving technologies of containers, further work will investigate the applicability of using this research in that context and consider attributing the system ’ s energy consumption to container instances instead of VM instances.
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