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K. Tutschku (kurt.tutschku@univie.ac.at)

Chair of Future Communication Prof. Dr. K. Tutschku Institute for Multimedia and Distributed Systems Faculty of Computer Science. Network Virtualization as a Mean for Service Convergence for Future Communication Systems – What can we learn from Federated Experimental Facilities?.

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K. Tutschku (kurt.tutschku@univie.ac.at)

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  1. Chair of Future Communication Prof. Dr. K. Tutschku Institute for Multimedia and Distributed Systems Faculty of Computer Science Network Virtualization as a Mean for Service Convergence for Future Communication Systems –What can we learn from Federated Experimental Facilities? K. Tutschku (kurt.tutschku@univie.ac.at)

  2. Future Internet? ?

  3. Overview • The Internet under pressure • The success of the Internet • Network virtualization: virtual structures for convergent services • The GENI experimental facility • Performance issues of Transport Virtualization • Conclusion

  4. Accessnetworks Core networks Internet under Pressure • Internet will become a network of applications, services und content • Services are the new central elements  Convergence in usage • What changes hereof are anticipated for users, mechanisms and the future network architectures?

  5. Networks under Change: Services Applications POTS Mobile Teletext Voice(wired) Voice(cellular) Data service Services Serviceprovider Reseller A class. national PTT Reseller A X.25 / FR Networkoperator ISDN GSM • Limited convergence

  6. Networks under Change: Services Applications POTS mobile Web IP service Services Serviceprovider IP Service Provider C A B D E Networkoperator ATM/ MPLS GPRS • Limit convergence • Internet Protocol (IP) is main converging layer

  7. Deficiencies of the Current Internet • Performance (“World wide wait”) • However:No convergence; QoS islands with are available (depending on technology and provider) • Reliability: • Again: no convergence • Availability of the Internet ´03: 93.2% − 99.6% • Availability of POTS: 99.99% – 99.999% • However: sophisticated resilience mechanisms available at experienced ISP • Competition / business models: • J. Crowcroft: “… I can go on the web and get my gas, electricity, … changed , why is it not possible to get a SPOT price for broad-band internet?” (E2E-interest mailing list on April 26th, 2008); contracts prohibit change • No convergence; even technically infeasible

  8. Networks under Change: Services Applications Web. Unified communication appl. IP Service Voice Video Messa-ging Data Services Overlays (e.g. Skype) Serviceprovider IP Service Provider A B C D E Networkprovider UMTS PSTN xDSL WLAN Multi-Network Services • Limit convergence • Internet Protocol (IP) is main converging layer (but: hour glass model!) • Integration of different technical and administrative domains by virtual networks: Overlays • Overcome deficiencies and implement new features • Networks/overlays have to be (self-)organized for the services

  9. Data/ Service Data/Service Data/ Service Data/ Service Data/ Service Data/ Service centralized distributed Networks under Change: Services consumer at edge of network provider at edge of network ? ? Network-based provider (server) • Services will be offered and controlled from the edge („edge-based services“) • Central services will be virtualized • Boundaries between consumer and provider vanish (“prosumer”) • Symmetrical rolls require new architectures (ADSL?) and permit new business models („Peer productivity“) • Management of edge-based services? Optimal placement? Different user behavior? Dimensioning? • Which functions should be self-*?

  10. Networks under Change: Services • Application-oriented and self-organizing overlays outperform current services • Support for resources contribution by arbitrary users: „Overlays for Cooperation/ Participation“ • What is the performance of self-*? Scalability? Churn? Dynamical traffic patterns?

  11. Networks under Change: Transport Systems Management plane Servicerequest (FAX, Web) „semi-manual“ provisioning ATM E3 Head-quarter Remote office

  12. Networks under Change: Transport Systems Management Plane Control Plane auto. Signaling auto. provisioning IP layer EPON Head-quarter 100GE layer Remote office Multi-Layer-Networks DWDM layer • State-of-the-art optical transport systems: • Ultra-high transmission capacities; embedding of different transport network into one physical network (multi-layer networks) • Decay of CAPEX per Bit  Increased automation  self-* features (self-operation, self-organization) • However: higher complexity („numerous overlays“?) • How to achieve convergence?

  13. P2P, 67,3% eMail, 1,2% FTP, 0,3% other, 23,3% Web, 7,9% Success of the Current Internet • Efficient P2P-based, self-organizingcontent distribution networks • Ratio of data traffic types at public access node • Data traffic by IP TV Quelle: Telefonica (2003) Quelle: CISCO (2008)

  14. Multi-Source Download (eDonkey, BT) Offers file X Peer Publish X • P2P: two overlays (virtual structures) with different application layer functions (two basic P2P functions: searching / content exchange); each with different topology, addressing, and routing • Search function: able of self-contained re-organization of search mechanism • Downloading peer: self-initiated selection of providing peer (parallel routing of content) based on resource quality (throughput)  select the best (multi-)path for the content • Self-operation of basic P2P functions among networks  convergence is possible Offers file X Publish X Transfer of segment B Publish X Offers file X Index server Query X Query X Transfer of segment A Looking for X

  15. Diversity I: Multi-Provider Environment East coast West coast • High diversity wrt. paths: • Three North-american nation-wide ISPs Tier1 (AS 3967 Exodus, AS3356 Level3, AS6467 Abovenet; M. Liljenstam et al., 2003) • Multiple routes for increasedresilience and compe-tition are (theoretically) readily available! • Network selection not available in current IP  no convergence • Any way: autonomous identi-fication of available resources needed (Thanks to Michael Menth für vsualization)

  16. Diversity II: Multi-Quality Environment • 25% of paths violate the triangle inequality (wrt. packet delay) • Measurements in PlanetLab byS. Banerjee et al. (2004) • Internet routing is far from optimal • Better paths exist; capazity is readilyavailable • Can be offered (competition) • Again: autonomous identification of available resources needed • ! „Multi-homing“ not really available current IP protocols Using an intermediate A direct connection B C Triangle Inequality (TI): D(A,C)≤ D(A,B) + D(B,C)

  17. Virtualization of Operating Systems • One hardware executes multiple systems • Safe: Strong isolation of resources, e.g. for testing and debugging • Individual and powerful: User see whole computing center as his own computer • Efficient: reduction of CAPEX (consolidation of multiple machines in a single physical one) and OPEX (operational issue) • Convergence of operating systems

  18. Virtual Networks for Convergent Services • Diversity • Exploit diversity of resources by smart localization • Provide optimal resources • Overlays • Overlays: application-oriented topology, addressing, and routing • Multi-Network Services • Self-operation of functions •  Enables global convergence • OS virtualization • Strong isolation of resources • Consolidation and efficient operation •  Enables local convergence Convergence by Network Virtualization • Build a „personal network (PN)” for an application (PN  PC) • Integration of different technologies and administrative domains • Re-use of generic infrastructure on small time scale • Push application-layer mechanisms safely down the stack • Avoid “multi-layer” trap  autonomic/self-* operation; particularly smart resource mgmt

  19. A Formal Description for Virtualization • Virtual resources • Generation of logical resources • Sharing: one physical, multiple logical resources • Aggregation: one logical, multiple physical Share Aggregation Virtual Machine Load Balancer Service Service Service Logical Virtual Server Guest OS Guest OS Load Balancer Virtual CPU Virtual Machine Switch Virtual Memory Virtual I/O Virtual Machine Monitor Physical Server CPU Memory I/O

  20. Transport Virtualization (TV) • Example: Virtual Memory • OS integrates disconnected physical memory, even disk space, into continuous memory • location of physical memory doesn’t matter • Transport Virtualization (Tutschku, Nakao, 2008): abstraction concept for data transport resources • Physical location of transport resource doesn't matter (as long resource is accessible) • Achieved by: abstract data transport resources • combined from one or more physical/overlay transport resources, e.g. leased line, wave length path, an overlay link, MPLS path, or an IP forwarding capability • physical resources can be used preclusive or concurrently • basic resources can be located in even different physical networks or administrative domains T. Zinner, P. Tran-Gia A. Nakao

  21. Concurrent Multi-Path Transfer Aim: Very high and reliable transmission between two end hosts Solution: Transport Virtualization: Combine multiple paths (even from different overlays) Aim: Very high and reliable throughput between two end hosts Overlays of provider II pooled transport pipe Overlays of provider I POP Physical topology

  22. Implementation: routing overlays Routing Overlay (= P2P Multi-Source Download) • Gummadi et al (2004): Scalable “One-Hop” (= intermediate) routing overlays • Nakao, Tutschku, Zinner: Consideration of multiple paths • (2008) • ! May be inefficient  Reduction of overhead (since edge-based)  Placement of NV router in core •  Application: Transport System Virtualization for  high-capacity transmissions, e.g. for HD TV •  How can we test it? Encapsulated, send using path 3 Divert selected endhost packets 1 Decapsulate, egress to destination 4 Request Paths for Diverted Packets Path oracle One-hop Source Router (SOR) 2

  23. GENI: The Global Environment for Network Innovation • Started in 2007 • Original agenda • Research: • Identify fundamental questions; Drive a set of experiments to validate theories and models • Experiments & requirements • Drives what infrastructure and facilities are needed • Currently • One very rough blueprint; Five different control architecture • Major ideas infrastructure operation: • Clearing house: settles usage request • Lifetime for resources: has to be returned at prede-fined lifetime

  24. Corporate GENI suites Wireless #1 Compute Cluster #1 Backbone #2 Access #1 Other-Nation Projects My GENI Slice Compute Cluster #2 Backbone #1 Other-Nation Projects Wireless #2 Appealing Idea: Federation My experiment runs across the evolving GENI federation. NSF parts of GENI (Slide by Chip Elliot)

  25. Offer Resource Discovery Aggregates publish resources, schedules, etc., via clearinghouses What resources can I use? GENI Clearinghouse Researcher Components Components Components Aggregate A Computer Cluster Aggregate B Backbone Net Aggregate C Metro Wireless (Slide by Chip Elliot)

  26. Slice Creation Clearinghouse checks credentials & enforces policyAggregates allocate resources & create topologies Create my slice GENI Clearinghouse Components Components Components Aggregate A Computer Cluster Aggregate B Backbone Net Aggregate C Metro Wireless (Slide by Chip Elliot)

  27. Experimentation Researcher loads software, debugs, collects measurements Experiment – Install my software, debug, collect data, retry, etc. GENI Clearinghouse Components Components Components Aggregate A Computer Cluster Aggregate B Backbone Net Aggregate C Metro Wireless (Slide by Chip Elliot)

  28. Slice Growth & Revision Allows successful, long-running experiments to grow larger Make my slice bigger ! GENI Clearinghouse Components Components Components Aggregate A Computer Cluster Aggregate B Backbone Net Aggregate C Metro Wireless (Slide by Chip Elliot)

  29. Federation of Clearinghouses Growth path to international, semi-private, and commercial GENIs Make my slice even bigger ! GENI Clearinghouse Federated Clearinghouse (Slide by Chip Elliot) Components Components Components Components Aggregate A Computer Cluster Aggregate B Backbone Net Aggregate C Metro Wireless Aggregate D Non-NSF Resources

  30. Stop the experiment immediately ! Operations & Management Always present in background for usual reasonsWill need an ‘emergency shutdown’ mechanism GENI Clearinghouse Oops Federated Clearinghouse (Slide by Chip Elliot) Components Components Components Components Aggregate A Computer Cluster Aggregate B Backbone Net Aggregate C Metro Wireless Aggregate D Non-NSF Resources

  31. Federation for Transport Virtualization Path selection Routing Overlay Routing Overlay usedpath Routing Overlay I Path selection for concurrent use pooledressource pooledressource Routing Overlay II Path selection in federated networks  convegence of networks

  32. Transmission Model Data stream divided at router into segments with k parts each provider will offer a set ni of parallel paths (i = 1…m) p1,1 overlay 1 p1,n1 • Scheduling? Assumption: use k parallel paths on m overlays 1 2 k k parts have arrived  src k pooled paths dst  1 Re-sequencing buffer of size L k-1 k pm,1 k parts are send in parallel at time t overlay m Reassemble data stream from obtained parts pm,nm • Buffer occupancy? With paths

  33. So far: Simulation Experiment • Input: • Number of paths • Scheduling • Output: Re-sequencing buffer occupancy distribution •  Search for path selection strategies; future on-line selection for convergence • Path delay distributions • Path capacity Source Destination

  34. Impact of Type of Delay Distribution I Delay • Types of distributions: • Uniform: artificial behavior • Truncated Gaussian: mathematical tractability • Bimodal: two modes of a path • Investigation of different influence factors

  35. Impact of Type of Delay Distribution II Two synchronous, equal capacity paths Three synchronous, equal capacity paths Buffer Buffer Highly non-linear  careful and complex path selection

  36. Current Work: Perform Real-World Measurements • Measurement set-up •  Gain realistic parameters and strategies

  37. Conclusion • Expected features of the Future Internet • Faster, more reliable, more business cases, increased interaction with users: symmetric rolls, „Architecture for Participation“ • Forming of applications-specific overlays • Network virtualization: • Consolidation of multiple (virtual) network into one physical infrastructure • Making data transport independent from resource locations  transport virtualization • Integration/convergence of different transport systems und operator domains by overlays and network virtualization • Design networks for applications (rather than designing applications for networks) • Experimental facilities: • Federation: blue print for future network operation and convergence • Resources with limited lifetime  significant challenges in resource management

  38. Thanks for your • attention! • Questions?

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