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Virtualization in Mobile Systems

Virtualization in Mobile Systems. Mahadev Satyanarayanan School of Computer Science Carnegie Mellon University. Based on results and insights from recent collaborative research with:

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Virtualization in Mobile Systems

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  1. Virtualization in Mobile Systems Mahadev SatyanarayananSchool of Computer ScienceCarnegie Mellon University Based on results and insights from recent collaborative research with: Steve Smaldone, Adam Wolbach, Benjamin Gilbert, Jan Harkes, Nilton Bila, Sarah Rutlidge, Liviu Iftode, Eyal de Lara, Andres Lagar-Cavilla, Victor Bahl, Ramon Caceres, Nigel Davies, Roy Want

  2. Sad Reality of Mobile Computing • Hardware limitations • vs. static elements of same era • weight, power, size constraints • CPU, memory, disk, display,  • Wireless communication uncertainty • bandwidth / latency variation • intermittent connectivity • may cost real money • Finite energy source • actions may be slowed or deferred • communication costs energy

  3. What Has Changed? Previous slide was true 15+ years ago (early 1990s) huge hardware and wireless networking improvements since but deep essentials haven’t changed on autopilot, same slide will be true 15+ years hence (2020) Resource poverty is the enduring attribute of mobile computing How can we change this? fundamental paradigm shift How can we escape the resource trap?

  4. Escaping the Resource Trap Today’s mobility metaphor:Self-sufficient but resource-poor New mobility metaphor: Leveraged and resource-rich • Leverage the Cloud! • (but keep the Swiss Army Knife as fallback)

  5. Leveraging the Cloud - IThe Legacy PC World

  6. 2009 ACM Turing Award • Chuck Thacker • Pioneer Honored for Design of First Modern Personal Computer and Other Major Innovations

  7. Why Was the PC Invented? from“Hints forComputer System Design”by Butler LampsonSOSP 1983 The nicest thing about the Alto is that it doesn’t run faster at night (J. Morris) A similar lesson was learned about processor time. With interactive use the response time to a demand for computing is important, since a person is waiting for it. Many attempts were made to tune the processor scheduling as a function of priority of the computation, working set size, memory loading, past history, likelihood of an i/o request, etc; these efforts failed.  The natural extension of this strategy is the personal computer, in which each user has at least one processor to himself. What we gained: unvarying crisp interaction From

  8. What We Gave Up: Mobility • Personal computing involved a tradeoff • we won big on one set of issues (crisp interaction + usability) • we gave up on a second (seamless mobility + easy adminstration) • Today, one’s PC is a personal fortress • complete computing world of its own • uniquely customized by you • long, slow setup process to get every setup detail right • discourages use of pervasive hardware • ok for stateless apps like web browsing • not ok for personal productivity apps • expensive system administration • Can we regain what we gave up 25+ years ago? • enjoy seamless mobility across pervasive hardware? • trivial system administration at edges

  9. VM image(memory, disk, etc.)+ additional meta-data Parcel Parcel Suspend Resume VM-based Transient PC Model Cloud Storage “Carry-nothing” seamless mobility (not thin client) “Your own PCanywhere, anytime” Proposed in WMCSA June 2002 “Internet Suspend/Resume”, Kozuch and Satyanarayanan

  10. ISR Evolution • ISR-2early 2002 – late 2004 • Performance tradeoff exploration • VMM = VMware Workstation 3.0 • dist. storage = Coda • loadable kernel module (“fauxide”) • user-level ISR client (“vulpes”) • ISR-1 late 2001 – early 2002 • Proof of concept • VMM = VMware Workstation 3.0 • dist. storage = NFS • copyin/copyout of entire VM state • ISR-3late 2004 – mid 2006 • Pilot deployment • VMM = VMware Workstation 4.5 • dist. storage = user-level chunk store • major vulpes changes • 23 users, peak usage Jan-Aug 2005 • extensive analysis of usage data • predicted high value of CAS • revealed fatal flaw in client design • OpenISR mid 2006 – now (release 0.9.9) • Production-quality open source system • VMM-agnostic (VirtualBox, KVM, VMware, ) • complete re-engineering of code base • total rewrite of kernel code (nexus) • ongoing evolution of client and serverfunctionality and performance • continuous deep use (~15-20 users) • PocketISR boot capability

  11. Using Mobile Devices with ISR • ISR disadvantage: variable user experience • (Suspend & Resume delays depend on Internet connectivity) • Can smartphones improve ISR user experience? • device that people already carry • ample storage + multiple modes of connectivity • very small mobility footprint • Embodied in experimental Horatio extension to OpenISR(Smaldone et al, MobiSys 2009)

  12. Types of Cloud Computing VM Storage Local Cloud cloud-cloud local-cloud Remote execution(e.g. Grid Computing) Managed execution(e.g. Amazon EC2) Cloud more comute power VM Execution Classic PC model(e.g. laptops) Transient PC(e.g. ISR) crisper interaction Local better availability better safety local-local cloud-local

  13. Leveraging the Cloud - II New Resource-Rich Mobile Apps

  14. Machine Translation Today 0.85 0.8 Human Scoring Range 0.7447 0.7289 0.7 0.6 0.5610 BLEU SCORES 0.5551 0.5137 0.5 0.4 0.3859 0.3 CBMT Spanish Google Chinese (‘06 NIST) Systran Spanish SDL Spanish Google Arabic (‘05 NIST) Google Spanish ’08 top lang Based on same Spanish test set

  15. Face Recognition Today

  16. What’s The Catch? • These are resource-intensive applications • State-of-art performance and quality only with room full of servers • How do we achieve this “in the wild”? • (on resource-poor, energy-limited mobile hardware) • Obvious solution: leverage the cloud! • But your cloud may be far away  • End-to-end latency matters for crisp interaction • e.g., real-time two-way language translation on mobile devices • e.g, augmented reality for cognitive assistance via “smart glasses” •  and many other examples

  17. Latency Hurts Even If Bandwidth Good(E.g. QuakeViz interactive benchmark on VNC thin client 100 Mbps)

  18. Sample Internet2 RTTs (milliseconds)

  19. Latency on 3G Networks • “The wireless delay in the 3G network dominates the whole network path delay, e.g., latency to the first pingable hop is around 200ms, which is close to the end-to-end Ping latency to landmark servers distributed across the U.S.” • from“Anatomizing Application Performance Differences on Smartphones”, to appear in MobiSys 2010 (Huang et al)

  20. AndroidPhone Nokia N810Tablet HandtalkWearableGlove Solution: Create a Tiny Cloud Nearby Olympus Mobile Eye TrekWearableComputer WAN todistant cloudon Internet Low-latencyhigh-bandwidth1-hop wirelessnetwork cloudlet =(compute cluster+ wireless access point+ wired Internet access+ no battery limitations) “data center in a box” Coffee shopCloudlet

  21. Local Wireless Bandwidth • Original motivation for cloudlets was latency • But 1-hop wireless bandwidth to cloudlet also a win • wireless LAN bw typically 100X wireless WAN bw • e.g. 802.11n ≈ 400 Mbps but HSPDA ≈ 2 Mbps • shipping large objects within interactive time bounds • e.g. captured images in an augmented reality system • 4MB JPEG image takes 80 ms @ 400 Mbps, but 16 seconds @ 2 Mbps

  22. Cloudlet vs. Cloud

  23. inherent tension Key Challenges • 1. Trusting infrastructure • tamper-resistant hardware (“first-world infrastructure”) • portable device as root of trust (e.g TrustSniffer) • 2. Finding the exactly right software on it uniformity  deployer value specificity  end-user value

  24. Transient Customization • Deliver fully configured virtual machine (VM) to infrastructure • Problem: too large, too slow for transient use • Solution: assemble VM on the fly  dynamic VM synthesis • prefetch large, relatively static, widely-used piece (“base VM”) • deliver small patch (“VM overlay”) just before use • discard VM after use • VM overlay can come from • mobile device over wireless link, or • web site under control of mobile device (URL and decryption key)

  25. private VM overlay user-drivendevice-VMinteractions Usecloudlet done VM residue Dynamic VM Synthesis Preload base VM Discover & negotiateuse of cloudlet Mobile D e vice Cloudlet (base + overlay)  launch VM Execute launch VM Finish use Create VM residue Discard VMOptional: cache VM overlay Depart

  26. Typical Overlay Sizes(base VM = 8GB Ubuntu Linux)

  27. Nearly half the totalAll in the infrastructurePotentially optimizable VM Synthesis Time at 100Mbps(untuned proof-of-concept prototype)

  28. When Bandwidth Drops to 10Mbps

  29. In Closing  • Leverage the Cloud! • (but keep the Swiss Army Knife handy for emergencies) Virtual Machine Technology

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