minnesota systems cloud research vision n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Minnesota Systems Cloud Research Vision PowerPoint Presentation
Download Presentation
Minnesota Systems Cloud Research Vision

Loading in 2 Seconds...

play fullscreen
1 / 18

Minnesota Systems Cloud Research Vision - PowerPoint PPT Presentation


  • 81 Views
  • Uploaded on

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

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Minnesota Systems Cloud Research Vision' - caraf


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
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
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
Analytics

Data

in

Computation

Resultsout

with thanks to Ian Foster

cloud limitations localized
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
slide5
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
Example: Dispersed-Data-Intensive Services
  • Data is geographically distributed
    • Costly, inefficient to move to central location

blog1

blog2

blog3

nebula a new cloud model

Nebula

Central

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

Decentralized, less-managed cloud

dispersed storage/compute resources

low latency deployment: native client

example dispersed data intensive services1
Example: Dispersed-Data-Intensive Services
  • Data is geographically distributed
    • Costly, inefficient to move to central location

blog1

blog2

blog3

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

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

experimental results image processing
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

example 2
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
user centric cloud
User-centric cloud

RAi knows user i profile

Vision paper: University of Minnesota,

CSE TR-11-006, March 2011.

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