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GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications

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  1. GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications Qixin Wang*, Xue Liu**, Jennifer Hou*, and Lui Sha* *Dept. of Computer Science, UIUC **School of Computer Science, McGill Univ.

  2. Demand • Big Trend: converge computers with the physical world

  3. Demand • Big Trend: converge computers with the physical world • Cyber-Physical Systems

  4. Demand • Big Trend: converge computers with the physical world • Cyber-Physical Systems • Real-Time and Embedded (RTE) GENI

  5. Demand • Big Trend: converge computers with the physical world • Cyber-Physical Systems • Real-Time and Embedded (RTE) GENI • Virtual Organization

  6. Demand • Big Trend: converge computers with the physical world • Cyber-Physical Systems • Real-Time and Embedded (RTE) GENI • Virtual Organization • Calls for RTE-WAN with following features:

  7. Demand • Big Trend: converge computers with the physical world • Cyber-Physical Systems • Real-Time and Embedded (RTE) GENI • Virtual Organization • Calls for RTE-WAN with following features: • Scalability:

  8. Demand • Big Trend: converge computers with the physical world • Cyber-Physical Systems • Real-Time and Embedded (RTE) GENI • Virtual Organization • Calls for RTE-WAN with following features: • Scalability: • Similar traffic aggregation.

  9. Demand • Big Trend: converge computers with the physical world • Cyber-Physical Systems • Real-Time and Embedded (RTE) GENI • Virtual Organization • Calls for RTE-WAN with following features: • Scalability: • Similar traffic aggregation. • Global/local traffic segregation;

  10. Demand • Big Trend: converge computers with the physical world • Cyber-Physical Systems • Real-Time and Embedded (RTE) GENI • Virtual Organization • Calls for RTE-WAN with following features: • Scalability: • Similar traffic aggregation. • Global/local traffic segregation; • Network hierarchy and modularity

  11. Demand • Big Trend: converge computers with the physical world • Cyber-Physical Systems • Real-Time and Embedded (RTE) GENI • Virtual Organization • Calls for RTE-WAN with following features: • Scalability: • Similar traffic aggregation. • Global/local traffic segregation; • Network hierarchy and modularity, which also assists composability, dependability, debugging etc.

  12. Demand • Big Trend: converge computers with the physical world • Cyber-Physical Systems • Real-Time and Embedded (RTE) GENI • Virtual Organization • Calls for RTE-WAN with following features: • Scalability: • Similar traffic aggregation. • Global/local traffic segregation; • Network hierarchy and modularity; • Configurability:

  13. Demand • Big Trend: converge computers with the physical world • Cyber-Physical Systems • Real-Time and Embedded (RTE) GENI • Virtual Organization • Calls for RTE-WAN with following features: • Scalability: • Similar traffic aggregation. • Global/local traffic segregation; • Network hierarchy and modularity; • Configurability: • Runtime behavior regulation

  14. Demand • Big Trend: converge computers with the physical world • Cyber-Physical Systems • Real-Time and Embedded (RTE) GENI • Virtual Organization • Calls for RTE-WAN with following features: • Scalability: • Similar traffic aggregation. • Global/local traffic segregation; • Network hierarchy and modularity; • Configurability: • Runtime behavior regulation • Flexibility:

  15. Demand • Big Trend: converge computers with the physical world • Cyber-Physical Systems • Real-Time and Embedded (RTE) GENI • Virtual Organization • Calls for RTE-WAN with following features: • Scalability: • Similar traffic aggregation. • Global/local traffic segregation; • Network hierarchy and modularity; • Configurability: • Runtime behavior regulation • Flexibility: • Ease of reconfiguration

  16. Demand • Big Trend: converge computers with the physical world • Cyber-Physical Systems • Real-Time and Embedded (RTE) GENI • Virtual Organization • Calls for RTE-WAN with following features: • Scalability: • Similar traffic aggregation. • Global/local traffic segregation; • Network hierarchy and modularity; • Configurability: • Runtime behavior regulation • Flexibility: • Ease of reconfiguration • Hard Real-Time E2E Delay Guarantee

  17. Demand • Big Trend: converge computers with the physical world • Cyber-Physical Systems • Real-Time and Embedded (RTE) GENI • Virtual Organization • Calls for RTE-WAN with following features: • Scalability: • Similar traffic aggregation. • Global/local traffic segregation; • Network hierarchy and modularity; • Configurability: • Runtime behavior regulation • Flexibility: • Ease of reconfiguration • Hard Real-Time E2E Delay Guarantee

  18. Solution? The Train/Railway Analogy

  19. Solution? The Train/Railway Analogy • Similar traffic aggregation: carriage  train

  20. Solution? The Train/Railway Analogy • Similar traffic aggregation: carriage  train • Global/local traffic segregation: express vs. local train

  21. Solution? The Train/Railway Analogy • Similar traffic aggregation: carriage  train • Global/local traffic segregation: express vs. local train • Hierarchical topology: express vs. local train

  22. Solution? The Train/Railway Analogy • Similar traffic aggregation: carriage  train • Global/local traffic segregation: express vs. local train • Hierarchical topology: express vs. local train • Configuration: routing, capacity planning

  23. Solution? The Train/Railway Analogy • Similar traffic aggregation: carriage  train • Global/local traffic segregation: express vs. local train • Hierarchical topology: express vs. local train • Configuration: routing, capacity planning • Flexibility: change the train planning, not the railway

  24. The Equivalent of Train in Network?

  25. The Equivalent of Train in Network? • An aggregate (of flows) is like a train A C B Legend Aggregate. Member Flow End Node Intermediate Node

  26. The Equivalent of Train in Network? • An aggregate (of flows) is like a train • Sender End Node: merges member flows into the aggregate A C B Legend Aggregate. Member Flow End Node Intermediate Node

  27. The Equivalent of Train in Network? • An aggregate (of flows) is like a train • Sender End Node: merges member flows into the aggregate • Receiver End Node: disintegrates the aggregate into original flows A C B Legend Aggregate. Member Flow End Node Intermediate Node

  28. The Equivalent of Train in Network? • An aggregate (of flows) is like a train • Sender End Node: merges member flows into the aggregate • Receiver End Node: disintegrates the aggregate into original flows • Intermediate Nodes: only forward the aggregate packets A C B Legend Aggregate. Member Flow End Node Intermediate Node

  29. The Equivalent of Train in Network? • An aggregate (of flows) is like a train A C B Legend Aggregate. Member Flow End Node Intermediate Node

  30. The Equivalent of Train in Network? • An aggregate (of flows) is like a train • Packets of member flows  carriages A C B Legend Aggregate. Member Flow End Node Intermediate Node

  31. The Equivalent of Train in Network? • An aggregate (of flows) is like a train • Packets of member flows  carriages • Sender End Node: assembles the carriages into a train A C B Legend Aggregate. Member Flow End Node Intermediate Node

  32. The Equivalent of Train in Network? • An aggregate (of flows) is like a train • Packets of member flows  carriages • Sender End Node: assembles the carriages into a train • Receiver End Node: dissembles the train into carriages A C B Legend Aggregate. Member Flow End Node Intermediate Node

  33. The Equivalent of Train in Network? • An aggregate (of flows) is like a train • Packets of member flows  carriages • Sender End Node: assembles the carriages into a train • Receiver End Node: dissembles the train into carriages • Intermediate Nodes: only forward the train, but cannot add/remove carriages A C B Legend Aggregate. Member Flow End Node Intermediate Node

  34. The Equivalent of Train in Network? • An aggregate (of flows) is like a train • Packets of member flows  carriages • Sender End Node: assembles the carriages into a train • Receiver End Node: dissembles the train into carriages • Intermediate Nodes: only forward the train, but cannot add/remove carriages • Forwarding (routing) on the per train basis, not per carriage basis A C B Legend Aggregate. Member Flow End Node Intermediate Node

  35. The Equivalent of Train in Network? • An aggregate (of flows) is like a train • Packets of member flows  carriages • Sender End Node: assembles the carriages into a train • Receiver End Node: dissembles the train into carriages • Intermediate Nodes: only forward the train, but cannot add/remove carriages • Forwarding (routing) on the per train basis, not per carriage basis • Local Train: few hops (physical links) A C B Legend Aggregate. Member Flow End Node Intermediate Node

  36. The Equivalent of Train in Network? • An aggregate (of flows) is like a train • Packets of member flows  carriages • Sender End Node: assembles the carriages into a train • Receiver End Node: dissembles the train into carriages • Intermediate Nodes: only forward the train, but cannot add/remove carriages • Forwarding (routing) on the per train basis, not per carriage basis • Local Train: few hops • Express Train: many hops A C B Legend Aggregate. Member Flow End Node Intermediate Node

  37. Virtual Link/Topology • Aggregates with the same sender and receiver end nodes collectively embody a virtual link A C B F1 F2 F3 Legend Aggregate. Thickness implies the aggregate’s data throughput Virtual Link End Node Intermediate Node

  38. Virtual Link/Topology • Aggregates with the same sender and receiver end nodes collectively embody a virtual link • Many virtual links altogether build up virtual topology A C B F1 F2 F3 Legend Aggregate. Thickness implies the aggregate’s data throughput Virtual Link End Node Intermediate Node

  39. State-of-the-Art: GR-Aggregate • How to build virtual link with hard real-time E2E delay guarantee?

  40. State-of-the-Art: GR-Aggregate • How to build virtual link with hard real-time E2E delay guarantee? • [SunShin05]: Guaranteed Rate Aggregate (GR-Aggregate)

  41. State-of-the-Art: GR-Aggregate Guaranteed Rate Server (GR-Server) [Goyal97a]: A queueing server S is a GR-Server, as long as there exists a constant rf (called guaranteed rate) for each of its flow f , such that

  42. State-of-the-Art: GR-Aggregate Guaranteed Rate Server (GR-Server) [Goyal97a]: A queueing server S is a GR-Server, as long as there exists a constant rf (called guaranteed rate) for each of its flow f , such that

  43. State-of-the-Art: GR-Aggregate Guaranteed Rate Server (GR-Server) [Goyal97a]: A queueing server S is a GR-Server, as long as there exists a constant rf (called guaranteed rate) for each of its flow f , such that pfj: jth packet of flow f

  44. State-of-the-Art: GR-Aggregate Guaranteed Rate Server (GR-Server) [Goyal97a]: A queueing server S is a GR-Server, as long as there exists a constant rf (called guaranteed rate) for each of its flow f , such that L(p): time when packet p leaves S pfj: jth packet of flow f

  45. State-of-the-Art: GR-Aggregate Guaranteed Rate Server (GR-Server) [Goyal97a]: A queueing server S is a GR-Server, as long as there exists a constant rf (called guaranteed rate) for each of its flow f , such that L(p): time when packet p leaves S A specific function, called GRSFunc pfj: jth packet of flow f

  46. State-of-the-Art: GR-Aggregate Guaranteed Rate Server (GR-Server) [Goyal97a]: A queueing server S is a GR-Server, as long as there exists a constant rf (called guaranteed rate) for each of its flow f , such that L(p): time when packet p leaves S A specific function, called GRSFunc pfj: jth packet of flow f

  47. State-of-the-Art: GR-Aggregate Guaranteed Rate Server (GR-Server) [Goyal97a]: A queueing server S is a GR-Server, as long as there exists a constant rf (called guaranteed rate) for each of its flow f , such that rf: guaranteed rate L(p): time when packet p leaves S A specific function, called GRSFunc pfj: jth packet of flow f

  48. State-of-the-Art: GR-Aggregate Guaranteed Rate Server (GR-Server) [Goyal97a]: A queueing server S is a GR-Server, as long as there exists a constant rf (called guaranteed rate) for each of its flow f , such that rf: guaranteed rate L(p): time when packet p leaves S A specific function, called GRSFunc pfj: jth packet of flow f

  49. State-of-the-Art: GR-Aggregate • [Goyal97a] proves WFQ, WF2Q are GR-Server, with rf = f C, wheref is the weight of flow f(note f ≤ 1), and Cis the server output capacity.

  50. State-of-the-Art: GR-Aggregate • [Goyal97a] proves WFQ, WF2Q are GR-Server, with rf = f C, wheref is the weight of flow f(note f ≤ 1), and Cis the server output capacity. • [SunShin05]: GR-Aggregate based Virtual Link: