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ICON: Intelligent Container Overlays

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ICON: Intelligent Container Overlays presented at 17th ACM Workshop on Hot Topics in Networks (HotNets) 2018 in Redmond, Washington

Slides owned and prepared by Aleksandr Zavodovski

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ICON: Intelligent Container Overlays

  1. 1. ICON: Intelligent Container Overlays A. Zavodovski, N. Mohan, S. Bayhan, W. Wong and J. Kangasharju
 HotNets 2018, November 15-16, 2018
  2. 2. Motivation: Edge Computing 2
  3. 3. Motivation: Edge Computing 2 Latency-critical services are predeployed to edge facilities
  4. 4. Motivation: Edge Computing 2 Latency-critical services are predeployed to edge facilities
  5. 5. Motivation: Edge Computing 2 Latency-critical services are predeployed to edge facilities
  6. 6. Motivation: Edge Computing 2 Latency-critical services are predeployed to edge facilities
  7. 7. Motivation: Edge Computing 2 Latency-critical services are predeployed to edge facilities Using app without edge is slow, users are not quite content
  8. 8. Motivation: Edge Computing 2 Latency-critical services are predeployed to edge facilities How to discover local edge servers? Are there any? Using app without edge is slow, users are not quite content
  9. 9. 3 CDN for Services?
  10. 10. NO! 3 CDN for Services?
  11. 11. NO! 3 CDN for Services? Open Standards
  12. 12. NO! 3 CDN for Services? Decentralized Solution Open Standards
  13. 13. NO! 3 CDN for Services? Something like the Internet! Decentralized Solution Open Standards
  14. 14. Motivation • Growing demand for edge computing, making edge pervasive • Applications should unfold towards the edge autonomously • Managing applications by setting only high level objectives • Autonomous adaptation to the changing environment 4
  15. 15. Motivation • Growing demand for edge computing, making edge pervasive • Applications should unfold towards the edge autonomously • Managing applications by setting only high level objectives • Autonomous adaptation to the changing environment • Towards common standards of self-organized service provisioning on global scale 4
  16. 16. Motivation • Growing demand for edge computing, making edge pervasive • Applications should unfold towards the edge autonomously • Managing applications by setting only high level objectives • Autonomous adaptation to the changing environment • Towards common standards of self-organized service provisioning on global scale • Not only about the edge! 4
  17. 17. ICON: Intelligent Container • Virtualized entity containing a service • Built on top of Docker container, VM, unikernel, etc. • Oriented towards microservices architecture (application is a collection of loosely coupled services) 5
  18. 18. ICON: Intelligent Container • Virtualized entity containing a service • Built on top of Docker container, VM, unikernel, etc. • Oriented towards microservices architecture (application is a collection of loosely coupled services) • Capable of observing the environment • Monitors where incoming flows come from • Discovers potential deployment locations 6
  19. 19. ICON: Intelligent Container • Virtualized entity containing a service • Built on top of Docker container, VM, unikernel, etc. • Oriented towards microservices architecture (application is a collection of loosely coupled services) • Capable of observing the environment • Monitors where incoming flows come from • Discovers potential deployment locations • Capable of taking decisions and acting autonomously • Migrates or replicates closer to end-users to satisfy, e.g., latency objectives • Terminates if utility falls below predefined threshold 7
  20. 20. Operation of ICON • Initially, ICON is in the cloud • One or multiple origination points 8
  21. 21. Operation of ICON • Initially, ICON is in the cloud • One or multiple origination points • ICON monitors incoming flows • Where requests are coming from? 9
  22. 22. Operation of ICON • Initially, ICON is in the cloud • One or multiple origination points • ICON monitors incoming flows • Where requests are coming from? • ICON discovers deployment locations • In the domain of end-users or on a path to it 10
  23. 23. Operation of ICON • Initially, ICON is in the cloud • One or multiple origination points • ICON monitors incoming flows • Where requests are coming from? • ICON discovers deployment locations • In the domain of end-users or on a path to it • ICON can take autonomous decisions • Deploy replica of itself 11
  24. 24. Operation of ICON • Initially, ICON is in the cloud • One or multiple origination points • ICON monitors incoming flows • Where requests are coming from? • ICON discovers deployment locations • In the domain of end-users or on a path to it • ICON can take autonomous decisions • Deploy replica of itself 12
  25. 25. The Operation of ICON • Initially, ICON is in the cloud • One or multiple origination points • ICON monitors incoming flows • Where requests are coming from? • ICON discovers deployment locations • In the domain of end-users or on a path to it • ICON can take autonomous decisions • Deploy replica of itself • Migrate closer to the end-users 13
  26. 26. Overlay of ICONs 14 Tokyo Origin
  27. 27. Overlay of ICONs 15 Tokyo Wellington LisbonNew York Origin
  28. 28. Overlay of ICONs 16 Tokyo Wellington LisbonNew York Los Angeles Rio Origin
  29. 29. Overlay of ICONs • Tree is formed organically as ICONs deploy replicas of themselves • Efficient for information propagation • Coordination • Control • Other topologies are possible (e.g., swarm) 17
  30. 30. Independent Edge Providers (IEPs) • Facility where ICON can deploy itself • Can be: • Facility operated by a cloud provider (e.g., Cloudfront, Azure Stack) • Telco edge server (MEC) • Crowdsourced: iExec, Golem, etc.? • Runs container yard application • Built on top of e.g., Kubernetes, Mesos or Docker Swarm • ICON negotiates with the yard on deployment timeslot, hardware resources, price, etc. • Contractual agreements and transactions • Smart contracts are possible option 18
  31. 31. Independent Edge Providers (IEPs) • Facility where ICON can deploy itself • Can be: • Facility operated by a cloud provider (e.g., Cloudfront, Azure Stack) • Telco edge server (MEC) • Crowdsourced: iExec, Golem, etc.? • Runs container yard application • Built on top of e.g., Kubernetes, Mesos or Docker Swarm • ICON negotiates with the yard on deployment timeslot, hardware resources, price, etc. • Contractual agreements and transactions • Smart contracts are possible option 18 Anyone can establish an IEP
  32. 32. Discovery of IEPs • Assumption: IEPs add edge SRV records to authoritative DNS servers of their domains 19
  33. 33. Discovery of IEPs • Assumption: IEPs add edge SRV records to authoritative DNS servers of their domains • Perform tomography • Traceroute to end-users 20
  34. 34. Discovery of IEPs • Assumption: IEPs add edge SRV records to authoritative DNS servers of their domains • Perform tomography • Traceroute to end-users • Identify on-path domains 21
  35. 35. Discovery of IEPs • Assumption: IEPs add edge SRV records to authoritative DNS servers of their domains • Perform tomography • Traceroute to end-users • Identify on-path domains • Perform SRV query 22
  36. 36. Discovery of IEPs • Assumption: IEPs add edge SRV records to authoritative DNS servers of their domains • Perform tomography • Traceroute to end-users • Identify on-path domains • Perform SRV query 22
  37. 37. Discovery of IEPs • Assumption: IEPs add edge SRV records to authoritative DNS servers of their domains • Perform tomography • Traceroute to end-users • Identify on-path domains • Perform SRV query 22
  38. 38. Intelligence of ICONs • Easy version: Governed by utility function • Weights are “control knobs”, e.g., budget vs. latency • Application owner can also “hotswap” the entire utility function • Thresholds: When expected utility of an action exceeds certain boundary value: • Replicate • Migrate • Terminate • More complex version: Overlay forms a picture of the world and adapts the network as a whole 23
  39. 39. Open Questions 24
  40. 40. Open Questions • Intelligence • What kind of intelligence do ICONs need? 24
  41. 41. Open Questions • Intelligence • What kind of intelligence do ICONs need? • Discovery of the closest ICON • How new clients will discover the ICON which is closest to them? 24
  42. 42. Open Questions • Intelligence • What kind of intelligence do ICONs need? • Discovery of the closest ICON • How new clients will discover the ICON which is closest to them? • Security • Trusted execution environments? 24
  43. 43. Open Questions • Intelligence • What kind of intelligence do ICONs need? • Discovery of the closest ICON • How new clients will discover the ICON which is closest to them? • Security • Trusted execution environments? • Contractual agreement between ICON and independent edge providers • Are smart contracts the best option? 24
  44. 44. Future Work 25
  45. 45. Future Work • Implementing container yard for hosting ICONs 25
  46. 46. Future Work • Implementing container yard for hosting ICONs • Negotiation protocol • Like all routers support IP, facilities providing capacity to run services should support some common protocol to negotiate on new service deployment 25
  47. 47. Future Work • Implementing container yard for hosting ICONs • Negotiation protocol • Like all routers support IP, facilities providing capacity to run services should support some common protocol to negotiate on new service deployment • Sophisticated intelligence • Proactively predicting where from most of the requests will come 25
  48. 48. Future Work • Implementing container yard for hosting ICONs • Negotiation protocol • Like all routers support IP, facilities providing capacity to run services should support some common protocol to negotiate on new service deployment • Sophisticated intelligence • Proactively predicting where from most of the requests will come • Game theoretic analysis • What if two (or more) competing applications are deployed using ICONs? 25
  49. 49. Future Work • Implementing container yard for hosting ICONs • Negotiation protocol • Like all routers support IP, facilities providing capacity to run services should support some common protocol to negotiate on new service deployment • Sophisticated intelligence • Proactively predicting where from most of the requests will come • Game theoretic analysis • What if two (or more) competing applications are deployed using ICONs? • Specialized ICONs forming chains of services, and multitier apps • Not limited to edge, e.g., a database may also be packed as ICON • How to coordinate? 25
  50. 50. Summary: Why ICONs? 26
  51. 51. Summary: Why ICONs? • IEPs to tackle growing demand for edge computing • Less cloud monopoly 26
  52. 52. Summary: Why ICONs? • IEPs to tackle growing demand for edge computing • Less cloud monopoly • Open infrastructure standards to enable ad hoc deployment of services on global scale 26
  53. 53. Summary: Why ICONs? • IEPs to tackle growing demand for edge computing • Less cloud monopoly • Open infrastructure standards to enable ad hoc deployment of services on global scale • Reducing administrative overhead: applications move across the network themselves following high level objectives 26
  54. 54. Summary: Why ICONs? • IEPs to tackle growing demand for edge computing • Less cloud monopoly • Open infrastructure standards to enable ad hoc deployment of services on global scale • Reducing administrative overhead: applications move across the network themselves following high level objectives • Taking advantage of autonomous local decision-making leads to faster adaptation to changing environment 26
  55. 55. Summary: Why ICONs? • IEPs to tackle growing demand for edge computing • Less cloud monopoly • Open infrastructure standards to enable ad hoc deployment of services on global scale • Reducing administrative overhead: applications move across the network themselves following high level objectives • Taking advantage of autonomous local decision-making leads to faster adaptation to changing environment 26 Thank you!
  56. 56. Summary: Why ICONs? • IEPs to tackle growing demand for edge computing • Less cloud monopoly • Open infrastructure standards to enable ad hoc deployment of services on global scale • Reducing administrative overhead: applications move across the network themselves following high level objectives • Taking advantage of autonomous local decision-making leads to faster adaptation to changing environment 26 Thank you! aleksandr.zavodovski@helsinki.fi

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