GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications
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Transcript of GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications
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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.
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Demand
• Big Trend: converge computers with the physical world
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Demand
• Big Trend: converge computers with the physical world– Cyber-Physical Systems
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Demand
• Big Trend: converge computers with the physical world– Cyber-Physical Systems– Real-Time and Embedded (RTE) GENI
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Demand
• Big Trend: converge computers with the physical world– Cyber-Physical Systems– Real-Time and Embedded (RTE) GENI– Virtual Organization
![Page 6: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/6.jpg)
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:
![Page 7: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/7.jpg)
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:
![Page 8: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/8.jpg)
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.
![Page 9: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/9.jpg)
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;
![Page 10: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/10.jpg)
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
![Page 11: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/11.jpg)
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.
![Page 12: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/12.jpg)
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:
![Page 13: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/13.jpg)
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
![Page 14: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/14.jpg)
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:
![Page 15: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/15.jpg)
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
![Page 16: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/16.jpg)
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
![Page 17: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/17.jpg)
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
![Page 18: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/18.jpg)
Solution? The Train/Railway Analogy
![Page 19: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/19.jpg)
Solution? The Train/Railway Analogy
• Similar traffic aggregation: carriage train
![Page 20: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/20.jpg)
Solution? The Train/Railway Analogy
• Similar traffic aggregation: carriage train
• Global/local traffic segregation: express vs. local train
![Page 21: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/21.jpg)
Solution? The Train/Railway Analogy
• Similar traffic aggregation: carriage train
• Global/local traffic segregation: express vs. local train• Hierarchical topology: express vs. local train
![Page 22: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/22.jpg)
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
![Page 23: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/23.jpg)
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
![Page 24: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/24.jpg)
The Equivalent of Train in Network?
![Page 25: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/25.jpg)
The Equivalent of Train in Network?• An aggregate (of flows) is like a train
A C B
Legend Aggregate.
End Node Intermediate Node
Member Flow
![Page 26: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/26.jpg)
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.
End Node Intermediate Node
Member Flow
![Page 27: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/27.jpg)
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.
End Node Intermediate Node
Member Flow
![Page 28: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/28.jpg)
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.
End Node Intermediate Node
Member Flow
![Page 29: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/29.jpg)
The Equivalent of Train in Network?• An aggregate (of flows) is like a train
Legend Aggregate.
End Node Intermediate Node
Member Flow
A C B
![Page 30: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/30.jpg)
The Equivalent of Train in Network?• An aggregate (of flows) is like a train
– Packets of member flows carriages
Legend Aggregate.
End Node Intermediate Node
Member Flow
A C B
![Page 31: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/31.jpg)
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
Legend Aggregate.
End Node Intermediate Node
Member Flow
A C B
![Page 32: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/32.jpg)
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
Legend Aggregate.
End Node Intermediate Node
Member Flow
A C B
![Page 33: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/33.jpg)
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
Legend Aggregate.
End Node Intermediate Node
Member Flow
A C B
![Page 34: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/34.jpg)
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
Legend Aggregate.
End Node Intermediate Node
Member Flow
A C B
![Page 35: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/35.jpg)
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)
Legend Aggregate.
End Node Intermediate Node
Member Flow
A C B
![Page 36: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/36.jpg)
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
Legend Aggregate.
End Node Intermediate Node
Member Flow
A C B
![Page 37: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/37.jpg)
Virtual Link/Topology• Aggregates with the same sender and receiver
end nodes collectively embody a virtual link
A C BF1
F2
F3
Legend
Virtual Link Aggregate. Thickness implies the aggregate’s data throughput
End Node Intermediate Node
![Page 38: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/38.jpg)
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 BF1
F2
F3
Legend
Virtual Link Aggregate. Thickness implies the aggregate’s data throughput
End Node Intermediate Node
![Page 39: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/39.jpg)
State-of-the-Art: GR-Aggregate
• How to build virtual link with hard real-time E2E delay guarantee?
![Page 40: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/40.jpg)
State-of-the-Art: GR-Aggregate
• How to build virtual link with hard real-time E2E delay guarantee?
• [SunShin05]: Guaranteed Rate Aggregate (GR-Aggregate)
![Page 41: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/41.jpg)
State-of-the-Art: GR-AggregateGuaranteed 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
![Page 42: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/42.jpg)
State-of-the-Art: GR-Aggregate
. history, arrivalpacket past s'GRSFunc)( fjf rfpL
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
![Page 43: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/43.jpg)
State-of-the-Art: GR-Aggregate
. history, arrivalpacket past s'GRSFunc)( fjf rfpL
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
![Page 44: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/44.jpg)
State-of-the-Art: GR-AggregateGuaranteed 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
L(p): time when packet p leaves S
. history, arrivalpacket past s'GRSFunc)( fjf rfpL
![Page 45: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/45.jpg)
State-of-the-Art: GR-AggregateGuaranteed 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
L(p): time when packet p leaves S
A specific function, called GRSFunc
. history, arrivalpacket past s'GRSFunc)( fjf rfpL
![Page 46: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/46.jpg)
State-of-the-Art: GR-AggregateGuaranteed 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
L(p): time when packet p leaves S
A specific function, called GRSFunc
. history, arrivalpacket past s'GRSFunc)( fjf rfpL
![Page 47: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/47.jpg)
State-of-the-Art: GR-AggregateGuaranteed 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
L(p): time when packet p leaves S
A specific function, called GRSFunc
. history, arrivalpacket past s'GRSFunc)( fjf rfpL
rf: guaranteed rate
![Page 48: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/48.jpg)
State-of-the-Art: GR-AggregateGuaranteed 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
L(p): time when packet p leaves S
A specific function, called GRSFunc
rf: guaranteed rate
. history, arrivalpacket past s'GRSFunc)( fjf rfpL
![Page 49: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/49.jpg)
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 C is the server output capacity.
![Page 50: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/50.jpg)
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 C is the server output capacity.
• [SunShin05]: GR-Aggregate based Virtual Link:
![Page 51: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/51.jpg)
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 C is the server output capacity.
• [SunShin05]: GR-Aggregate based Virtual Link:– Sender End: aggregates virtual link’s member flows into an
aggregate F using a GR-Server;
![Page 52: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/52.jpg)
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 C is the server output capacity.
• [SunShin05]: GR-Aggregate based Virtual Link:– Sender End: aggregates virtual link’s member flows into an
aggregate F using a GR-Server;
– Intermediate Nodes: each forwards F with a GR-Server at a guaranteed rate of RF, where RF ≥ F, and F is F’s data throughput.
![Page 53: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/53.jpg)
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 C is the server output capacity.
• [SunShin05]: GR-Aggregate based Virtual Link:– Sender End: aggregates virtual link’s member flows into an
aggregate F using a GR-Server;
– Intermediate Nodes: each forwards F with a GR-Server at a guaranteed rate of RF, where RF ≥ F, and F is F’s data throughput.
– Receiver End: disintegrates F into original flows.
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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 C is the server output capacity.
• [SunShin05]: GR-Aggregate based Virtual Link:– Sender End: aggregates virtual link’s member flows into an
aggregate F using a GR-Server;
– Intermediate Nodes: each forwards F with a GR-Server at a guaranteed rate of RF, where RF ≥ F, and F is F’s data throughput.
– Receiver End: disintegrates F into original flows.
– E2E Delay d ≤ / RF + , where , are certain constants.
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New Problem
• GR-Aggregate fits Internet traffic well.
![Page 56: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/56.jpg)
New Problem
• GR-Aggregate fits Internet traffic well.
• When applied to Cyber-Physical Systems traffic
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New Problem
• GR-Aggregate fits Internet traffic well.
• When applied to Cyber-Physical Systems traffic– Real-time sensing/actuating aggregate:
![Page 58: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/58.jpg)
New Problem
• GR-Aggregate fits Internet traffic well.
• When applied to Cyber-Physical Systems traffic– Real-time sensing/actuating aggregate:
• Small data throughput, small E2E delay requirement
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New Problem
• GR-Aggregate fits Internet traffic well.
• When applied to Cyber-Physical Systems traffic– Real-time sensing/actuating aggregate:
• Small data throughput, small E2E delay requirement
– Real-time video aggregate:
![Page 60: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/60.jpg)
New Problem
• GR-Aggregate fits Internet traffic well.
• When applied to Cyber-Physical Systems traffic– Real-time sensing/actuating aggregate:
• Small data throughput, small E2E delay requirement
– Real-time video aggregate:• Large data throughput, small E2E delay requirement
![Page 61: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/61.jpg)
New Problem
• GR-Aggregate fits Internet traffic well.
• When applied to Cyber-Physical Systems traffic– Real-time sensing/actuating aggregate:
• Small data throughput, small E2E delay requirement
– Real-time video aggregate:• Large data throughput, small E2E delay requirement
– Non-real-time traffic
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New Problem
• For real-time sensing/actuating aggregate:
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New Problem
• For real-time sensing/actuating aggregate: – Small data throughput, small E2E delay requirement
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New Problem
• For real-time sensing/actuating aggregate: – Small data throughput, small E2E delay requirement
– GR-Aggregate E2E delay d ≤ / RF +
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New Problem
• For real-time sensing/actuating aggregate: – Small data throughput, small E2E delay requirement
– GR-Aggregate E2E delay d ≤ / RF + – Assigning small guaranteed rate RF (i.e., RF = F)
large E2E delay;
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New Problem
• For real-time sensing/actuating aggregate: – Small data throughput, small E2E delay requirement
– GR-Aggregate E2E delay d ≤ / RF + – Assigning small guaranteed rate RF (i.e., RF = F)
large E2E delay;
– Assigning large guaranteed rate RF (i.e., RF > F )
![Page 67: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/67.jpg)
New Problem
• For real-time sensing/actuating aggregate: – Small data throughput, small E2E delay requirement
– GR-Aggregate E2E delay d ≤ / RF + – Assigning small guaranteed rate RF (i.e., RF = F)
large E2E delay;
– Assigning large guaranteed rate RF (i.e., RF > F ) other aggregates’ guaranteed rates < their data throughputs (when link capacity is precious).
![Page 68: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/68.jpg)
New Problem
• For real-time sensing/actuating aggregate: – Small data throughput, small E2E delay requirement
– GR-Aggregate E2E delay d ≤ / RF + – Assigning small guaranteed rate RF (i.e., RF = F) large
E2E delay;
– Assigning large guaranteed rate RF (i.e., RF > F ) other aggregates’ guaranteed rates < their data throughputs (when link capacity is precious).GR-Aggregate does not talk about this situation.
![Page 69: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/69.jpg)
New Problem
• For real-time sensing/actuating aggregate: – Small data throughput, small E2E delay requirement
– GR-Aggregate E2E delay d ≤ / RF + – Assigning small guaranteed rate RF (i.e., RF = F) large
E2E delay;
– Assigning large guaranteed rate RF (i.e., RF > F ) other aggregates’ guaranteed rates < their data throughputs (when link capacity is precious).GR-Aggregate does not talk about this situation.What will happen?
![Page 70: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/70.jpg)
Solution Heuristic
• Observation:
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Solution Heuristic
• Observation:
The purpose of using GR-Server to provide E2E delay guarantee is to provide a constant per hop transmission delay bound.
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Solution Heuristic
• Observation:
The purpose of using GR-Server to provide E2E delay guarantee is to provide a constant per hop transmission delay bound.
• As long as we can provide such a bound, we are fine.
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Solution Heuristic• So far we know WFQ, WF2Q are GR-Servers, and if flow f is assigned
weight f , it is guaranteed a rate of rf = f C.
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Solution Heuristic• So far we know WFQ, WF2Q are GR-Servers, and if flow f is assigned
weight f , it is guaranteed a rate of rf = f C.
Conventionally, we assign weight f proportional to data throughput, i.e., f C ≥ f.
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Solution Heuristic• So far we know WFQ, WF2Q are GR-Servers, and if flow f is assigned
weight f , it is guaranteed a rate of rf = f C.
Conventionally, we assign weight f proportional to data throughput, i.e., f C ≥ f.
• What if we assign arbitrary weight?
![Page 76: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/76.jpg)
Solution Heuristic• So far we know WFQ, WF2Q are GR-Servers, and if flow f is assigned
weight f , it is guaranteed a rate of rf = f C.
Conventionally, we assign weight f proportional to data throughput, i.e., f C ≥ f.
• What if we assign arbitrary weight?
Discovery: as long as every flow is token-bucket-constrained and f
≤ C, every flow still has a bounded transmission delay, and there is an algorithm TDB({i},{li
max},C) to calculate this transmission delay bound f (l).
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Solution Heuristic• So far we know WFQ, WF2Q are GR-Servers, and if flow f is assigned
weight f , it is guaranteed a rate of rf = f C.
Conventionally, we assign weight f proportional to data throughput, i.e., f C ≥ f.
• What if we assign arbitrary weight?
Discovery: as long as every flow is token-bucket-constrained and f ≤ C, every flow still has a bounded transmission delay, and there is an algorithm TDB({i},{li
max},C) to calculate this transmission delay bound f (l).
To the extreme, we can mimic prioritized preemption by assigning proper weights.
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Solution Heuristic: What does arbitrary weight imply?
F1, data rate = 0.1 F2, data rate = 0.4 F3, data rate = 0.5
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Solution Heuristic: What does arbitrary weight imply?
F1, data rate = 0.1 F2, data rate = 0.4 F3, data rate = 0.5
Server Capacity C = 1, all packet length l = 1.
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Solution Heuristic: What does arbitrary weight imply?
F1, data rate = 0.1 F2, data rate = 0.4 F3, data rate = 0.5
Data Rate Proportional Weight: 1 = 0.1, 2 = 0.4, 3 = 0.5
Server Capacity C = 1, all packet length l = 1.
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Solution Heuristic: What does arbitrary weight imply?
23 101 5.22
t (sec)1 2 3 4 5 6 7 8 9 100
F1, data rate = 0.1 F2, data rate = 0.4 F3, data rate = 0.5
Data Rate Proportional Weight: 1 = 0.1, 2 = 0.4, 3 = 0.5
Transmission delay bound inverse proportionally coupled with data
throughput
Server Capacity C = 1, all packet length l = 1.
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Solution Heuristic: What does arbitrary weight imply?
23 101 5.22
t (sec)1 2 3 4 5 6 7 8 9 100
F1, data rate = 0.1 F2, data rate = 0.4 F3, data rate = 0.5
Data Rate Proportional Weight: 1 = 0.1, 2 = 0.4, 3 = 0.5
Prioritized Weight: 1 = 0.999, 2 = 0.000999, 3 = 0.000001
Transmission delay bound inverse proportionally coupled with data
throughput
Server Capacity C = 1, all packet length l = 1.
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Solution Heuristic: What does arbitrary weight imply?
t (sec)1 2 3 4 5 7 8 9 100
11
6
22.29
202 63 23 101 5.22
t (sec)1 2 3 4 5 6 7 8 9 100
F1, data rate = 0.1 F2, data rate = 0.4 F3, data rate = 0.5
Data Rate Proportional Weight: 1 = 0.1, 2 = 0.4, 3 = 0.5
Transmission delay bound inverse proportionally coupled with data
throughput
According to TDB algorithm, transmission delay bound decoupled from data throughput, and reflects priority: higher priority maps to
shorter
Server Capacity C = 1, all packet length l = 1.
Prioritized Weight: 1 = 0.999, 2 = 0.000999, 3 = 0.000001
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Solution: GD-AggregateProposal: Guaranteed Delay Server (GD-Server):
A queueing server S is a GD-Server, as long as there exists a non-negative monotonically non-decreasing function f(l) (called guaranteed delay function) for each of its flow f , such that
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Solution: GD-Aggregate
. history, arrivalpacket past s'GDSFunc)( fjf fpL
Proposal: Guaranteed Delay Server (GD-Server):
A queueing server S is a GD-Server, as long as there exists a non-negative monotonically non-decreasing function f(l) (called guaranteed delay function) for each of its flow f , such that
![Page 86: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/86.jpg)
Solution: GD-Aggregate
. history, arrivalpacket past s'GDSFunc)( fjf fpL
Proposal: Guaranteed Delay Server (GD-Server):
A queueing server S is a GD-Server, as long as there exists a non-negative monotonically non-decreasing function f(l) (called guaranteed delay function) for each of its flow f , such that
pfj: jth packet of flow f
![Page 87: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/87.jpg)
Solution: GD-AggregateProposal: Guaranteed Delay Server (GD-Server):
A queueing server S is a GD-Server, as long as there exists a non-negative monotonically non-decreasing function f(l) (called guaranteed delay function) for each of its flow f , such that
pfj: jth packet of flow f
L(p): time when packet p leaves S
. history, arrivalpacket past s'GDSFunc)( fjf fpL
![Page 88: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/88.jpg)
Solution: GD-AggregateProposal: Guaranteed Delay Server (GD-Server):
A queueing server S is a GD-Server, as long as there exists a non-negative monotonically non-decreasing function f(l) (called guaranteed delay function) for each of its flow f , such that
pfj: jth packet of flow f
L(p): time when packet p leaves S
A specific function, called GDSFunc
. history, arrivalpacket past s'GDSFunc)( fjf fpL
![Page 89: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/89.jpg)
Solution: GD-AggregateProposal: Guaranteed Delay Server (GD-Server):
A queueing server S is a GD-Server, as long as there exists a non-negative monotonically non-decreasing function f(l) (called guaranteed delay function) for each of its flow f , such that
pfj: jth packet of flow f
L(p): time when packet p leaves S
A specific function, called GDSFunc
. history, arrivalpacket past s'GDSFunc)( fjf fpL
![Page 90: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/90.jpg)
Solution: GD-AggregateProposal: Guaranteed Delay Server (GD-Server):
A queueing server S is a GD-Server, as long as there exists a non-negative monotonically non-decreasing function f(l) (called guaranteed delay function) for each of its flow f , such that
pfj: jth packet of flow f
L(p): time when packet p leaves S
A specific function, called GDSFunc
f(l) : guaranteed delay function
. history, arrivalpacket past s'GDSFunc)( fjf fpL
![Page 91: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/91.jpg)
Solution: GD-AggregateProposal: Guaranteed Delay Server (GD-Server):
A queueing server S is a GD-Server, as long as there exists a non-negative monotonically non-decreasing function f(l) (called guaranteed delay function) for each of its flow f , such that
pfj: jth packet of flow f
L(p): time when packet p leaves S
A specific function, called GDSFunc
. history, arrivalpacket past s'GDSFunc)( fjf fpL
f(l) : guaranteed delay function
![Page 92: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/92.jpg)
Solution: GD-Aggregate
• Discovery: If each ingress flow/aggregate is token-bucket-constrained, WFQ and WF2Q servers are GD-Servers.
![Page 93: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/93.jpg)
Solution: GD-Aggregate
• Discovery: If each ingress flow/aggregate is token-bucket-constrained, WFQ and WF2Q servers are GD-Servers.
• Design: We modified the design of Sun and Shin’s GR-Aggregate into GD-Aggregate, (mainly) by changing GR-Servers to GD-Servers.
![Page 94: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/94.jpg)
Solution: GD-Aggregate
• GD-Aggregate Features:
![Page 95: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/95.jpg)
Solution: GD-Aggregate
• GD-Aggregate Features:– E2E Delay d ≤ k(lmax) + , where k(l) is the guaranteed delay
function at the kth hop, lmax is the maximal packet length.
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Solution: GD-Aggregate
• GD-Aggregate Features:– E2E Delay d ≤ k(lmax) + , where k(l) is the guaranteed delay
function at the kth hop, lmax is the maximal packet length.
– We found a way to assign weight to mimic priority so that
![Page 97: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/97.jpg)
Solution: GD-Aggregate
• GD-Aggregate Features:– E2E Delay d ≤ k(lmax) + , where k(l) is the guaranteed delay
function at the kth hop, lmax is the maximal packet length.
– We found a way to assign weight to mimic priority so that • An aggregate with small data throughput can have small k(l),
![Page 98: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/98.jpg)
Solution: GD-Aggregate
• GD-Aggregate Features:– E2E Delay d ≤ k(lmax) + , where k(l) is the guaranteed delay
function at the kth hop, lmax is the maximal packet length.
– We found a way to assign weight to mimic priority so that • An aggregate with small data throughput can have small k(l),
• and hence small E2E delay guarantee.
![Page 99: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/99.jpg)
Solution: GD-Aggregate
• GD-Aggregate Features:– E2E Delay d ≤ k(lmax) + , where k(l) is the guaranteed delay
function at the kth hop, lmax is the maximal packet length.
– We found a way to assign weight to mimic priority so that • An aggregate with small data throughput can have small k(l),
• and hence small E2E delay guarantee.
• No waste of link capacity
![Page 100: GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications](https://reader036.fdocuments.us/reader036/viewer/2022081514/56815af5550346895dc8b02d/html5/thumbnails/100.jpg)
Solution: GD-Aggregate
• GD-Aggregate Features:– E2E Delay d ≤ k(lmax) + , where k(l) is the guaranteed delay
function at the kth hop, lmax is the maximal packet length.
– We found a way to assign weight to mimic priority so that • An aggregate with small data throughput can have small k(l),
• and hence small E2E delay guarantee.
• No waste of link capacity k(l) is a linear function of packet length l.
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Solution: GD-Aggregate
• GD-Aggregate Features:– E2E Delay d ≤ k(lmax) + , where k(l) is the guaranteed delay
function at the kth hop, lmax is the maximal packet length.
– We found a way to assign weight to mimic priority so that • An aggregate with small data throughput can have small k(l),
• and hence small E2E delay guarantee.
• No waste of link capacity k(l) is a linear function of packet length l.
• Each priority’s capacity and delay guarantee can be planned with simple optimization tools.
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Solution: GD-Aggregate
• GD-Aggregate Features:– E2E Delay d ≤ k(lmax) + , where k(l) is the guaranteed delay
function at the kth hop, lmax is the maximal packet length.
– We found a way to assign weight to mimic priority so that • An aggregate with small data throughput can have small k(l),
• and hence small E2E delay guarantee.
• No waste of link capacity k(l) is a linear function of packet length l.
• Each priority’s capacity and delay guarantee can be planned with simple optimization tools.
(8 Theorems, 4 Corollaries, 14 Lemmas)
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Evaluation: Application Background
• Underground Mining: A Typical Cyber-Physical Systems Application
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3000m
300m
6000m
Panel 1
Panel 2
Panel 3
North
EastWest
South
Coal
An underground coal mine
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3000m
300m
6000m
Panel 1
Panel 2
Panel 3
Active Mining Area (Face)
Underground mines often cover huge areas; and are dangerous.
North
EastWest
South
Coal
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3000m
300m
6000m
Panel 1
Panel 2
Panel 3
Active Mining Area (Face)
Underground mines often cover huge areas; and are dangerous.
Need to replace all human workers with remotely controlled robots/vehicles.
North
EastWest
South
Coal
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3000m
300m
Active Mining Area (Face)
Above-Ground Remote Control
Room
6000m
Panel 1
Panel 2
Panel 3
Vision: Human remotely controls robots/vehicles from above-ground control room, via wired WAN backbone and wireless LANs
North
EastWest
South
Coal
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3000m
300m
Active Mining Area (Face)
Above-Ground Remote Control
Room
6000m
A wireless LAN base station (a.k.a. access point, in the case of IEEE 802.11)
A wireline physical link, part of the underground mining RTE-WAN
Panel 1
Panel 2
Panel 3
Coal
North
EastWest
South
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3000m
300m
Active Mining Area (Face)
Above-Ground Remote Control
Room
6000m
A wireless LAN base station (a.k.a. access point, in the case of IEEE 802.11)
A wireline physical link, part of the underground mining RTE-WAN
A virtual link (may consist of several GR/GD-aggregates) with its two end nodes
AB
Panel 1
Panel 2
Panel 3
Coal
North
EastWest
South
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Evaluation: Traffic Feature
• Remote underground mining creates all typical CPS traffic (aggregates)
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Evaluation: Traffic Feature
• Remote underground mining creates all typical CPS traffic (aggregates)
• Virtual link AB may consist of following aggregates:
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Evaluation: Traffic Feature
• Remote underground mining creates all typical CPS traffic (aggregates)
• Virtual link AB may consist of following aggregates:– F1: tele-robotic sensing/actuating aggregate
small data throughput, short hard real-time E2E delay requirement ( ≤ 50ms)
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Evaluation: Traffic Feature
• Remote underground mining creates all typical CPS traffic (aggregates)
• Virtual link AB may consist of following aggregates:– F1: tele-robotic sensing/actuating aggregate
small data throughput, short hard real-time E2E delay requirement ( ≤ 50ms)
– F2: tele-robotic video aggregatelarge data throughput, short hard real-time E2E delay requirement ( ≤ 50ms)
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Evaluation: Traffic Feature
• Remote underground mining creates all typical CPS traffic (aggregates)
• Virtual link AB may consist of following aggregates:– F1: tele-robotic sensing/actuating aggregate
small data throughput, short hard real-time E2E delay requirement ( ≤ 50ms)
– F2: tele-robotic video aggregatelarge data throughput, short hard real-time E2E delay requirement ( ≤ 50ms)
– F3: Non-real-time traffic aggregatetolerates seconds of E2E delay.
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Evaluation: Result
Aggregate’s data throughput (kbps)
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Evaluation: Result
When link capacity C is precious, i.e., total data throughput of F1, F2, and F3 = = C.
Aggregate’s data throughput (kbps)
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Evaluation: Result
When link capacity C is precious, i.e., total data throughput of F1, F2, and F3 = = C.
GR-Aggregate has to allocate guaranteed rate proportional to data throughput.
Aggregate’s data throughput (kbps)
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Evaluation: Result
When link capacity C is precious, i.e., total data throughput of F1, F2, and F3 = = C.
GR-Aggregate has to allocate guaranteed rate proportional to data throughput.
Aggregate’s data throughput (kbps)
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Evaluation: Result
When link capacity C is precious, i.e., total data throughput of F1, F2, and F3 = = C.
GR-Aggregate has to allocate guaranteed rate proportional to data throughput.
Aggregate’s data throughput (kbps)
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Evaluation: Result
When link capacity C is precious, i.e., total data throughput of F1, F2, and F3 = = C.
GR-Aggregate has to allocate guaranteed rate proportional to data throughput.
GD-Aggregate can still let F1 has highest priority.
Aggregate’s data throughput (kbps)
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Evaluation: Result
When link capacity C is precious, i.e., total data throughput of F1, F2, and F3 = = C.
GR-Aggregate has to allocate guaranteed rate proportional to data throughput.
GD-Aggregate can still let F1 has highest priority.
Aggregate’s data throughput (kbps)
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Evaluation: Result
When link capacity C is precious, i.e., total data throughput of F1, F2, and F3 = = C.
GR-Aggregate has to allocate guaranteed rate proportional to data throughput.
GD-Aggregate can still let F1 has highest priority.
Aggregate’s data throughput (kbps)
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Related Work
• Overlay Network: soft real-time, statistic
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Related Work
• Overlay Network: soft real-time, statistic• DiffServ: FIFO, poor performance for bursty traffic
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Related Work
• Overlay Network: soft real-time, statistic• DiffServ: FIFO, poor performance for bursty traffic• Real-Time Virtual Machine: still open problem,
especially on mutual exclusion and closed-form schedulability formulae.
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Related Work
• Overlay Network: soft real-time, statistic• DiffServ: FIFO, poor performance for bursty traffic• Real-Time Virtual Machine: still open problem,
especially on mutual exclusion and closed-form schedulability formulae.
• [Geogiadis96] also found the decoupling technique, fluid model, no aggregation.
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Related Work
• Overlay Network: soft real-time, statistic• DiffServ: FIFO, poor performance for bursty traffic• Real-Time Virtual Machine: still open problem,
especially on mutual exclusion and closed-form schedulability formulae.
• [Geogiadis96] also found the decoupling technique, fluid model, no aggregation.
• [Goyal97b] per packet guaranteed rate, known a priori, or refer to the minimum rate. Does not talk about aggregation.
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Conclusion
GD-Aggregate:
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Conclusion
GD-Aggregate:• Supports flow aggregation and E2E delay guarantee
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Conclusion
GD-Aggregate:• Supports flow aggregation and E2E delay guarantee• A tool to build hard real-time virtual link/topology
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Conclusion
GD-Aggregate:• Supports flow aggregation and E2E delay guarantee• A tool to build hard real-time virtual link/topology• Decouples E2E delay guarantee from data throughput
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Conclusion
GD-Aggregate:• Supports flow aggregation and E2E delay guarantee• A tool to build hard real-time virtual link/topology• Decouples E2E delay guarantee from data throughput• Supports priority
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Conclusion
GD-Aggregate:• Supports flow aggregation and E2E delay guarantee• A tool to build hard real-time virtual link/topology• Decouples E2E delay guarantee from data throughput• Supports priority• Simple linear closed-form formulae for analysis and
admission control
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Conclusion
GD-Aggregate:• Supports flow aggregation and E2E delay guarantee• A tool to build hard real-time virtual link/topology• Decouples E2E delay guarantee from data throughput• Supports priority• Simple linear closed-form formulae for analysis and
admission control• Can be planned with simple optimization tools
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References[Fisher04] B. Fisher et al., “Seeing, hearing, and touching: Putting it all
together,” SIGGRAPH'04 Course, 2004.[Georgiadis96] L. Georgiadis et al., “Efficient network QoS provisioning
based on per node traffic shaping,” IEEE/ACM Trans. on Networking, vol. 4, no. 4, August 1996.
[Goyal97a] P. Goyal et al., “Determining end-to-end delay bounds in heterogeneous networks,” Multimedia Systems, no. 5, pp. 157-163, 1997.
[Goyal97b] P. Goyal and H. M. Vin, “Generalized guaranteed rate scheduling algorithms: A framework,” IEEE/ACM Trans. on Networking, vol. 5, no. 4, pp. 561-571, August 1997.
[Hartman02] H. L. Hartman and J. M. Mutmansky, Introductory Mining Engineering (2nd Ed.). Wiley, August 2002.
[SunShin05] W. Sun and K. G. Shin, “End-to-end delay bounds for trafc aggregates under guaranteed-rate scheduling algorithms,” IEEE/ACM Trans. on Networking, vol. 13, no. 5, pp. 1188-1201, October 2005.
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Thank You!