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How can power usage of VM be monitored? 


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Power usage of virtual machines (VMs) can be monitored using various methods. One approach is to use a long short term memory (LSTM) neural network to predict power consumption for different devices based on power usage inputs . Another method is to use a decision tree to measure the power consumption of each VM, partitioning the dataset into easy-modeling pieces . Additionally, a fine-grained monitoring middleware called BitWatts can provide real-time and accurate power estimation of software processes running at any level of virtualization in a system . Another tree regression-based method can accurately measure the power consumption of VMs on the same host by splitting the dataset into easy-modeling subsets . Finally, a two-dimensional lookup table can be constructed for each VM to estimate power consumption based on CPU utilization and last-level-cache (LLC) miss rate .

Answers from top 4 papers

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Papers (4)Insight
Proceedings ArticleDOI
23 May 2013
14 Citations
The paper proposes a method called VPower, which uses a two-dimensional lookup table (LUT) to estimate the power consumption of a virtual machine (VM) based on CPU utilization and last-level-cache (LLC) miss rate.
Open accessProceedings ArticleDOI
17 Apr 2015
60 Citations
The paper proposes a middleware called BitWatts that monitors the power consumption of software processes running at any level of virtualization in a system. It uses a distributed actor implementation to collect process usage and infer fine-grained power consumption without the need for hardware investment.
Open accessJournal ArticleDOI
06 May 2015-IEEE Access
19 Citations
The paper proposes a tree regression-based method to accurately measure the power consumption of VMs on the same host. The method uses profiling features of each VM at the host level to divide VM power from host power in a fair way.
The paper proposes a decision tree method to measure the power consumption of each virtual machine (VM) in cloud data centers. This method partitions the collected dataset into easy-modeling pieces using a selected resource feature with a proper value.

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