Everest-OSDI08 - Everest Scaling down peak loads through...

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Unformatted text preview: Everest: Scaling down peak loads through I/O off-loading Dushyanth Narayanan Austin Donnelly Eno Thereska Sameh Elnikety Antony Rowstron Microsoft Research Cambridge, United Kingdom { dnarayan,austind,etheres,samehe,antr } @microsoft.com Abstract Bursts in data center workloads are a real problem for storage subsystems. Data volumes can experience peak I/O request rates that are over an order of magnitude higher than average load. This requires significant over- provisioning, and often still results in significant I/O re- quest latency during peaks. In order to address this problem we propose Everest, which allows data written to an overloaded volume to be temporarily off-loaded into a short-term virtual store. Everest creates the short-term store by opportunistically pooling underutilized storage resources either on a server or across servers within the data center. Writes are tem- porarily off-loaded from overloaded volumes to lightly loaded volumes, thereby reducing the I/O load on the for- mer. Everest is transparent to and usable by unmodified applications, and does not change the persistence or con- sistency of the storage system. We evaluate Everest using traces from a production Exchange mail server as well as other benchmarks: our results show a 1.470 times re- duction in mean response times during peaks. 1 Introduction Many server I/O workloads are bursty, characterized as having peak I/O loads significantly higher than the av- erage load. If the storage subsystem is not provisioned for its peak load, its performance during peaks degrades significantly, resulting in I/O operations having signifi- cant latency. We observe that workloads are usually un- balanced across servers in a data center, and often even across the data volumes associated with a single server. We propose Everest, a system that improves the perfor- mance of overloaded volumes by transparently exploit- ing statistical multiplexing across the storage bandwidth resources in the data center. Everest monitors the performance of a data volume, and if the load on the volume increases beyond a pre- defined threshold, it utilizes spare bandwidth on other storage volumes to absorb writes performed to the over- loaded volume. It does this by maintaining a virtual short-term persistent store , into which data is temporar- ily written, or off-loaded. The store is virtual in the sense that storage resources are not explicitly allocated to it; rather it is created by pooling idle bandwidth and spare capacity on existing data volumes either on a sin- gle server or across a set of servers in the same data cen- ter. In the common case, this can remove the majority of writes from the peak load, allowing the data volume un- der stress to serve mostly reads. When the peak subsides, the off-loaded data is lazily reclaimed back to the original volume, freeing the space in the Everest store. Everest handles short-term peaks and is not designed to handle long-term changes in load: these must be addressed by...
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This note was uploaded on 12/08/2011 for the course CS 525 taught by Professor Gupta during the Spring '08 term at University of Illinois, Urbana Champaign.

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Everest-OSDI08 - Everest Scaling down peak loads through...

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