| amazon_s3_presentation_url | amazon_s3_video_url | author | categories | comments | date | excerpt | featured_image_name | image | layout | link | post_format | session_id | session_track | slideshare_embed_url | slideshare_presentation_url | slug | speakers | tags | title | video_length | video_thumbnail | wordpress_id | youtube_video_url | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
connect |
bkk16 |
false |
2016-02-22 09:20:39 -0800 |
Review design. The current approach to predict the idle time duration is based on statistics on the previous idle time durations. The presentation will show the weaknesses of this approach and how by tracking the irq behavior we predict the next event to guess estimate the idle duration. |
BKK16-203.jpg |
|
resource-post |
|
BKK16-203 |
Power Management |
bkk16-203 |
Daniel Lezcano |
|
BKK16-203: Irq prediction or how to better estimate idle time |
53:43 |
None |
3325 |
Review design. The current approach to predict the idle time duration is based on statistics on the previous idle time durations. The presentation will show the weaknesses of this approach and how by tracking the irq behavior we predict the next event to guess estimate the idle duration.
Event Date: March 8, 2016