1. BOINC: Progress and Plans David P. Anderson Space Sciences Lab University of California, Berkeley BOINC:FAST August 2013

2. Computing resources PCs laptops mobile devices appliances Consumer desktop data center Organizational volunteer computing desktop grid computing cluster, cloud, Grid, super computing BOINC

3. Why volunteer computing? Consumer > 1 billion PCs  most w/GPU > 2 billion mobile devices Low cost to scientists Organizational ~10 million cluster/cloud nodes High cost

4. Volunteer computing status Volunteers: 400K people, 600K computers > 15 PetaFLOPS, many million jobs/day > 50 science projects, e.g.  IBM World Community Grid  CERN@home  Einstein@home  Climateprediction.net  Folding@home

5. Capacity in 5 years Participation: 50M devices? Processing  CPU: 50M * 1 TFLOPS = 50 ExaFLOPS  GPU: 50M * 20 TFLOPS = 1000 ExaFLOPS Storage:  50M * 10 TB = 500 ExaBytes Network bandwidth  50M * 100Mbps = 5 Petabit/sec

6. My talk VM-based applications Remote job submission BOINC on Android Volunteer storage

7. Using VM technology Consumer platforms:  85% Windows  7% Linux  7% Mac OS X Developing/maintaining versions for different platforms is hard  Even making a portable Linux executable is hard

8. BOINC VM-based applications You supply:  A VM image for your favorite environment can use the same one for many apps  An executable for that environment  An XML configuration file BOINC supplies:  The VBox Wrapper

9. How it works on the client BOINC client VirtualBox executive Vbox wrapper VM instance shared directory: executable input, output files VirtualBox “snapshots” provide checkpointing

10. Remote job submission science portal or PC BOINC server Scientists (users) sysadmins job submission file management

11. Managing multiple users User access control User quotas Sharing limited processing resources  handle small batches first  don’t starve large batches  enforce user quotas long-term

12. BOINC on Android Available in Google, Amazon app stores 7 projects have Android/ARM apps Battery, WiFi considerations GUI (Java) BOINC client (native) RPC apps (native)

13. BOINC storage architecture Data archival Applications Locality scheduling Dataset storage BOINC storage infrastructure Result archival

14. Storage-related features Client: allocating space among projects Client: support for large (multi-GB) files  efficient transfer, verification, copying Scheduler: storage policy plug-ins Example: volunteer data archival  multi-level erasure coding

15. Research opportunities Many job scheduling issues Big-data issues Simulation tools  client emulator  server emulator (Simba)  storage emulator

16. Thank you Please contact me to discuss BOINC or research collaboration: davea@ssl.berkeley.edu