More Google Cluster Data

Posted by John Wilkes, Principal Software Engineer

Google has a strong interest in promoting high quality systems research, and we believe that providing information about real-life workloads to the academic community can help.

In support of this we published a small (7-hour) sample of resource-usage information from a Google production cluster in 2010 (research blog on Google Cluster Data). Approximately a dozen researchers at UC Berkeley, CMU, Brown, NCSU, and elsewhere have made use of it.

Recently, we released a larger dataset. It covers a longer period of time (29 days) for a larger cell (about 11k machines) and includes significantly more information, including:

• the original resource requests, to permit scheduling experiments
• request constraints and machine attriibutes
• machine availability and failure events
• some of the reasons for task exits
• (obfuscated) job and job-submitter names, to help identify repeated or related jobs
• more types of usage information
• CPI (cycles per instruction) and memory traffic for some of the machines

Note that this trace primarily provides data about resource requests and usage. It contains no information about end users, their data, or access patterns to storage systems and other services.

More information can be found via this link, which will (after a short questionnaire) take you to a site that provides access instructions, a description of the data schema, and information about how the data was derived and its meaning.

We hope this data will facilitate a range of research in cluster management. Let us know if you find it useful, are willing to share tools that analyze it, or have suggestions for how to improve it.

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Discovering Talented Musicians with Acoustic Analysis

Posted by Charles DuHadway, YouTube Slam Team, Google Research

In an earlier post we talked about the technology behind Instant Mix for Music Beta by Google. Instant Mix uses machine hearing to characterize music attributes such as its timbre, mood and tempo. Today we would like to talk about acoustic and visual analysis -- this time on YouTube. A fundamental part of YouTube's mission is to allow anyone anywhere to showcase their talents -- occasionally leading to life-changing success -- but many talented performers are never discovered. Part of the problem is the sheer volume of videos: forty eight hours of video are uploaded to YouTube every minute (that’s eight years of content every day). We wondered if we could use acoustic analysis and machine learning to pore over these videos and automatically identify talented musicians.

First we analyzed audio and visual features of videos being uploaded. We wanted to find “singing at home” videos -- often correlated with features such as ambient indoor lighting, head-and-shoulders view of a person singing in front of a fixed camera, few instruments and often a single dominant voice. Here’s a sample set of videos we found.



Then we estimated the quality of singing in each video. Our approach is based on acoustic analysis similar to that used by Instant Mix, coupled with a small set of singing quality annotations from human raters. Given these data we used machine learning to build a ranker that predicts if an average listener would like a performance.

While machines are useful for weeding through thousands of not-so-great videos to find potential stars, we know they alone can't pick the next great star. So we turn to YouTube users to help us identify the real hidden gems by playing a voting game called YouTube Slam. We're putting an equal amount of effort into the game itself -- how do people vote? What makes it fun? How do we know when we have a true hit? We're looking forward to your feedback to help us refine this process: give it a try*. You can also check out singer and voter leaderboards. Toggle “All time” to “Last week” to find emerging talent in fresh videos or all-time favorites.

Our “Music Slam” has only been running for a few weeks and we have already found some very talented musicians. Many of the videos have less than 100 views when we find them.

And while we're excited about what we've done with music, there's as much undiscovered potential in almost any subject you can think of. Try our other slams: cute, bizarre, comedy, and dance*. Enjoy!

Related work by Google Researchers:
“Video2Text: Learning to Annotate Video Content”, Hrishikesh Aradhye, George Toderici, Jay Yagnik, ICDM Workshop on Internet Multimedia Mining, 2009.

* Music and dance slams are currently available only in the US.

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