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Analyzing Application Performance on ODROID Boards: Power and CPU Load Observations

This progress report details experiments conducted on an ODROID board to observe CPU load and power consumption while running various applications. Different applications, including video players and games, were analyzed. Key observations revealed significant variations in CPU usage when playing videos across different formats and under different media players. The report explores the challenges of scheduling virtual cores in a hypervisor context, as well as proposing a model for optimizing task execution by mapping virtual CPUs to physical ARM cores.

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Analyzing Application Performance on ODROID Boards: Power and CPU Load Observations

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  1. Progress Report 2014/05/23

  2. Different Applications • Run different applications on ODROID board and observe the loading and power reading. • Type 1 big.LITTLE core board • Applications including • Video • Game • Mobile benchmarks

  3. Video - .rmvb

  4. Video - .mkv

  5. Observations • The CPU loadings vary from one media player to the other on playing the same video. • A media player results in different CPU loading while playing videos with different format.

  6. Game - Angry Bird

  7. Observation • There are many “peaks of loading”. • Probably caused by user behaviors.

  8. Benchmark - Antutu

  9. Benchmark - Quadrant

  10. Benchmark - Vellamo

  11. Short Summary • It is difficult to predict or estimate the behavior of each task in the system during runtime.

  12. Scheduler in Hypervisor • Instead of focusing on scheduling tasks to (virtual) cores in one OS, we can consider scheduling virtual cores to physical cores from the hypervisor perspective.

  13. Problem • Given a set of VMs, each VM has several virtual cores with different frequencies. • How to schedule these virtual cores to a set of big and little physical cores, such that each virtual core is can have sufficient computing cycles in a fixed time interval.

  14. Core Model • Need to build the Loading-to-Power model of big and little core. • Decide which is better, “separate” or “consolidate” • Run bzip2 and busyAdding using one big or little core, and measure the power consumption. • Use cpulimit to cap the loading of task.

  15. Result – bzip2

  16. Result - busyAdding

  17. Short Summary • The power consumption is almost linear to the loading. • Even the loading is the same, the power consumption is slightly higher while executing bzip2 than busyAdding. • Probably the power consumed by cache or other components on core.

  18. Current Idea • “Big vCPUs to big ARM cores, little vCPUsto little ARM cores”. • If big ARM cores can not provide enough CPU resource, then consider schedule big vCPUs on both big and little ARM cores. • Note that we can “slice” a core.

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