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Power Meter Essay

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To get raw power metrics, we can use following hardware power meters: 1) Watts Up Meter Watts Up meter is a power meter which can be used to monitor and log power consumption of the data centre server. For this project we are planning to use Watts Up – PRO [9] which can monitor, log and make the data available to an external system via. USB. The data collected by the meter can be transmitted to the load balancing and load monitoring system either locally or over the Internet. 2) PowerSpy2 Alciom PowerSpy2 is another power meter which can be used for real-time power measurement and monitoring. PowerSpy2 will connect to the external system, which will monitor and collect data, via. Bluetooth. For designing a good load migration manager, we …show more content…

The applications which we researched more on is: 1) PowerTop PowerTop is a utility developed by Intel to monitor power consumption [10]. It supports Intel, AMD, ARM and UltraSPARC processors. This tool gives power consumption per process, CPU utilization, CPU Idle report, CPU frequency report, device information which are helpful in identifying high power demanding application. Result is available in .csv and .html form. Internally, PowerTop also makes use of RAPL utility. 2) Cloud Suits Today’s online services such are social networking, web search, etc. are based on massive working sets, real-time constraint and high level of parallelism. To simulate real-world online services in the field of cloud and data-centric computing Cloud Suite [11] is a widely used benchmark. It covers a wide range of application categories in today’s data centre i.e. media streaming, data serving, etc. Cloud Suite provides an effortless way to deploy benchmark into public and private cloud system using Docker containers. It is also integrated with Google Perfkit Benchmark. 1.1.4. Load Scheduling using Deep Learning Our plan is to leverage deep learning techniques on process metadata to determine process power profile. We looked for a similar implementation of our approach and found few machine-learning based approaches to achieve energy efficient scheduling. One approach is based on reinforcement learning [12]. In this approach, they proposed an idea to

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