CloudSense: Continuous Fine-Grain Cloud Monitoring with Compressive Sensing
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CitationKung, H.T., Chit-Kwan Lin, and Dario Vlah. 2011. CloudSense: Continuous fine-grain cloud monitoring with compressive sensing. In proceedings of 3rd USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 2011), Portland, OR, June 14-15.
AbstractContinuous fine-grain status monitoring of a cloud data center enables rapid response to anomalies, but handling the resulting torrent of data poses a significant challenge. As a solution, we propose CloudSense, a new switch design that performs in-network compression of status streams via compressive sensing. Using MapReduce straggler detection as an example of cloud monitoring, we give evidence that CloudSense allows earlier detection of stragglers, since finer-grain status can be reported for a given bandwidth budget. Furthermore, CloudSense showcases the advantage of an intrinsic property of compressive sensing decoding that enables detection of the slowest stragglers first. Finally, CloudSense achieves in-network compression via a low-complexity encoding scheme, which is easy and convenient to implement in a switch. We envision that CloudSense switches could form the foundation of a "compressed status information plane" that is useful for monitoring not only the cloud data center itself, but also the user applications that it hosts.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:9972706
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