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 Results out Data in with thanks to Ian Foster Computation

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 blog1 blog2 blog3 Data is geographically distributed Costly, inefficient to move to central location

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

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

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

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

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

15. Example 2 Dynamic user profile – contains activities in time and space – “read nytimes.com at 9am on the train; likes technology 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. Example 3: Multi-User Sharing Data/Code Sharing – Common application code, common data – Caching, code/data reuse, pre-fetching User preference-based: – Find common user interests and preferences – Implicit inference based on user profiles – Explicit cooperation based on social relationships

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

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

19. Thank you! Questions?