Jump to Content
Carrie Grimes Bostock

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
Sort By
  • Title
  • Title, descending
  • Year
  • Year, descending
    Availability in Globally Distributed Storage Systems
    Daniel Ford
    Francois Labelle
    Florentina Popovici
    Murray Stokely
    Van-Anh Truong
    Luiz Barroso
    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. View details
    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. View details
    Query logs alone are not enough
    Daniel Russell
    WWW 2007 Workshop on Query Log Analysis: Social and Technological Changes
    Preview
    Image Manifolds which are Isometric to Euclidean Space
    David L. Donoho
    Journal of Mathematical Imaging and Vision, vol. 23 (2005), pp. 5-24
    On Image Manifolds which are Isometric to Euclidean Space
    David L. Donoho
    Journal of Machine Imaging and Vision (2005)
    Hessian Eigenmaps: New Locally Linear Embedding Techniques For High-Dimensional Data
    David L. Donoho
    Proc. of National Academy of Sciences, vol. 100 (2003), pp. 5591-5596
    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?
    David L. Donoho
    ESANN (2002), pp. 199-204