minnesota systems cloud research vision jon weissman abhishek chandra distributed computing systems...
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Minnesota Systems Cloud Research Vision
Jon WeissmanAbhishek Chandra
Distributed Computing Systems GroupDepartment of CS&E
University of Minnesota
NSF Science of Cloud Computing Workshop, March 2011
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
Analytics
Resultsout
Datain
with thanks to Ian Foster
Computation
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
Þ in-situ/distributed, lightweight
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
Example: Dispersed-Data-Intensive Services
blog1 blog2
blog3
Data is geographically distributed Costly, inefficient to move to central location
Nebula: A New Cloud Model
• Stretch the cloud– exploit the rich collection of edge computers – volunteers (P2P, @home), commercial (CDNs)
NebulaCentral
Nebula• Decentralized, less-managed cloud
– dispersed storage/compute resources– low latency deployment: native client
Example: Dispersed-Data-Intensive Services
blog1 blog2
blog3
Data is geographically distributed Costly, inefficient to move to central location
Challenges
• Algorithmic/systems challenges
• Organization drivers– CDN vs. volunteers– trusted local clouds?
• Vision paper: HotCloud 2009, DIDC 2011
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
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
Example 1
• Outsourcing– local data capture + cloud processing– images/video, speech, digital design, aug. reality
Server Server Server Server Server
Proxy
Code repository
….
….
Mobile end
Application Profiler
Outsourcing Client
Outsourcing Controller
Nebula could also be the back-end
Commercial cloud
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. PowerFace recognition
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
User-centric cloud
RAi knows user i profile
Vision paper: University of Minnesota, CSE TR-11-006, March 2011.
Summary• Trends
– Dynamic large distributed data– Mobile users
• Our vision of the (a?) Cloud – locality of users, data– deep mobile integration, user-centricity