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Enhancing Cloud Computing Through Distributed Systems and User-Centric Approaches

This workshop presentation by Jon Weissman and Abhishek Chandra at the University of Minnesota explores the contemporary limitations and potential improvements in cloud computing. It highlights current usage modes, such as batch analytics and web hosting, and identifies challenges like high latency and data locality issues. The authors propose innovative solutions for decentralizing cloud architecture, including using edge computing and mobile solutions to deliver lower latency and enhance user experience. The vision emphasizes dynamic interactions with distributed data and services, aiming for a more responsive cloud ecosystem.

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Enhancing Cloud Computing Through Distributed Systems and User-Centric Approaches

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Presentation Transcript

  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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