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Minnesota Systems Cloud Research Vision

Minnesota Systems Cloud Research Vision. Jon Weissman Abhishek Chandra Distributed Computing Systems Group Department of CS&E University of Minnesota NSF Science of Cloud Computing Workshop, March 2011. Introduction: The Cloud Today. Dominant Usage Modes batch : analytics

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Minnesota Systems Cloud Research Vision

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  1. Minnesota Systems Cloud Research Vision Jon Weissman Abhishek Chandra Distributed Computing Systems Group Department of CS&E University of Minnesota NSF Science of Cloud Computing Workshop, March 2011

  2. Introduction: The Cloud Today • Dominant Usage Modes • batch: analytics • hosting: web services • storage: archive/backup/sharing end-user-neutral • Dominant Platform Modes • high latency: install and access • limited distribution: few data-centers localized

  3. Analytics Data in Computation Resultsout with thanks to Ian Foster

  4. Cloud Limitations: localized • Large volumes of widely distributed data • too expensive to move PBs of data centrally • poor locality to data sources • High latency deployment and access • limits highly network-sensitive user-facing services • limits short-term services • in-situ/distributed, lightweight

  5. Idea • Make the cloud more “distributed” • “move” it closer to data • “move” it closer to end-users • “move” it closer to other clouds • Make it lower latency • non-virtualized, on-demand

  6. Example: Dispersed-Data-Intensive Services • Data is geographically distributed • Costly, inefficient to move to central location blog1 blog2 blog3

  7. Nebula Central Nebula: A New Cloud Model • Stretch the cloud • exploit the rich collection of edge computers • volunteers (P2P, @home), commercial (CDNs)

  8. Nebula Decentralized, less-managed cloud dispersed storage/compute resources low latency deployment: native client

  9. Example: Dispersed-Data-Intensive Services • Data is geographically distributed • Costly, inefficient to move to central location blog1 blog2 blog3

  10. Challenges • Algorithmic/systems challenges • Organization drivers • CDN vs. volunteers • trusted local clouds? • Vision paper: HotCloud 2009, DIDC 2011

  11. Cloud Limitations: user-neutral • Mobile users/applications: phones, tablets • resource limited: power, CPU, memory • applications are becoming ^ sophisticated • Improve mobile user experience • performance, reliability, fidelity • tap into the cloud based on current resource state, preferences, interests => user-centric cloud processing

  12. Cloud Mobile Opportunity • Dynamic outsourcing • move computation, data to the cloud dynamically • User context • exploit user behavior to pre-fetch, pre-compute, cache • Multi-user sharing • Implicit sharing based on interests, social ties

  13. Server Server Server Server Server …. Code repository Outsourcing Client Proxy …. Application Profiler Outsourcing Controller Mobile end Example 1 • Outsourcing • local data capture + cloud processing • images/video, speech, digital design, aug. reality Commercial cloud Nebula could also be the back-end

  14. Experimental Results -Image Processing Avg. Time • Response time • Both WIFI & 3G • Up to 27× speedup • 219K, WIFI • Power consumption • Save up to 9× times • 219K, WIFI Face recognition Avg. Power

  15. Example 2 • Dynamic user profile • contains activities in time and space • “read nytimes.com at 9am on the train;likestechnology articles” • Patterns are relationships between activities • repetitive, sequential, concurrent, time-bounded • “user always does X and then does Y” • Exploiting patterns: pre-fetching, pre-computing, caching in the cloud

  16. User-centric cloud RAi knows user i profile Vision paper: University of Minnesota, CSE TR-11-006, March 2011.

  17. Summary • Trends • Dynamic large distributed data • Mobile users • Our vision of the (a?) Cloud • locality of users, data • deep mobile integration, user-centricity

  18. Thank you! Questions?

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