Carrie Grimes Bostock
Carrie Grimes Bostock graduated from Harvard with an A.B. in Anthropology/Archaeology in 1998, and an interest in quantitative methods for dealing with disparate data. She graduated from Stanford in 2003 with a PhD in Statistics after working with David Donoho on Nonlinear Dimensionality Reduction problems, and has been at Google since mid-2003. Dr. Grimes spent many years leading a research and technical team in Search at Google trying to figure out what criteria make a search engine index "good," "fast," and "comprehensive" - and how to achieve those goals. Currently, she is the Technical Lead for resource planning, pricing, and management data/software in Technical Infrastructure.
Authored Publications
Google Publications
Other Publications
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Availability in Globally Distributed Storage Systems
Daniel Ford
Francois Labelle
Florentina Popovici
Murray Stokely
Van-Anh Truong
Proceedings of the 9th USENIX Symposium on Operating Systems Design and Implementation, USENIX (2010)
Preview abstract
Highly available cloud storage is often implemented with
complex, multi-tiered distributed systems built on top of clusters of
commodity servers and disk drives. Sophisticated management, load
balancing and recovery techniques are needed to achieve high
performance and availability amidst an abundance of failure sources
that include software, hardware, network connectivity, and power issues. While
there is a relative wealth of failure studies of individual components of
storage systems, such as disk drives, relatively little has been
reported so far on the overall availability behavior of large
cloud-based storage services.
We characterize the availability properties of cloud
storage systems based on an extensive one year study of Google's
main storage infrastructure and present statistical models
that enable further insight into the impact of multiple
design choices, such as data placement and replication strategies.
With these models we compare data availability under a variety of
system parameters given the real patterns of failures observed in our fleet.
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Using a Market Economy to Provision Compute Resources Across Planet-wide Clusters
Murray Stokely
Jim Winget
Ed Keyes
Benjamin Yolken
Proceedings for the International Parallel and Distributed Processing Symposium 2009, IEEE, pp. 1-8
Preview abstract
We present a practical, market-based solution to the resource provisioning problem in a set of heterogeneous resource clusters. We focus on provisioning rather than immediate scheduling decisions to allow users to change long-term job specifications based on market feedback. Users enter bids to purchase quotas, or bundles of resources for long-term use. These requests are mapped into a simulated clock auction which determines uniform, fair resource prices that balance supply and demand. The reserve prices for resources sold by the operator in this auction are set based on current utilization, thus guiding the users as they set their bids towards under-utilized
resources. By running these auctions at regular time intervals, prices fluctuate like those in a real-world economy and provide motivation for users to engineer systems that can best take advantage of available resources.
These ideas were implemented in an experimental resource market at Google. Our preliminary results demonstrate an efficient transition of users from more congested resource pools to less congested resources. The disparate engineering costs for users to reconfigure their jobs to run on less expensive resource pools was evidenced by the large price premiums some users were willing to pay for more expensive resources. The final resource allocations illustrated how this framework can lead to significant, beneficial changes in user behavior, reducing the excessive shortages and surpluses of more traditional allocation methods.
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Query logs alone are not enough
Preview
Daniel Russell
WWW 2007 Workshop on Query Log Analysis: Social and Technological Changes
On Image Manifolds which are Isometric to Euclidean Space
Image Manifolds which are Isometric to Euclidean Space
Hessian Eigenmaps: New Locally Linear Embedding Techniques For High-Dimensional Data
New Methods in Nonlinear Dimensionality Reduction
Ph.D. Thesis, Stanford University (2003)
When does geodesic distance recover the true hidden parametrization of families of articulated images?
