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Protocols for Fast Delivery of
Large Data Volumes
CS4482 High Performance Networking
Dilum Bandara
Dilum.Bandara@uom.lk
Some slides extracted from Dr. Dan Massey’s CS557 class at Colorado
State University
High Data Volume Applications
 High definition video streaming
 Ultra high-definition video
 Sensor networks
 Video surveillance
 Radar networks
 Array of Radio telescopes
 Data transfer between grids/clouds
 Data transfer from CERN
 Virtual reality
 Holographic 3D display
Some applications require ordered delivery others don’t
2
Source: NAIC/Arecibo Obs/NSF
Source: www.idrshare.com
Latency Bandwidth Tradeoff
 Bandwidth is increasing
 10-100 Gbps networks
 Latency is not reducing
 Speed-of-light limitation
 Small transfers are latency limited
 telnet, ssh, chat messages, small file transfer
 Large transfers are still bandwidth limited
 Bulk transfer of files
3
Bulk Transfer
4
Source: N. Tolia et al., “An Architecture for Internet Data Transfer,” NSDI ‘06, 2006
TCP Design Assumptions
 Low physical link error rates
 Packet loss = congestion signal
 No packet reordering at network (IP) level
 Packet reordering = congestion signal
 These design assumptions are challenged today
 Parallel networking hardware
 Packet reordering
 Dedicated links or reservations
 No congestion
 Bulk transfers
 Streaming not needed 5
Parallel TCP
 Create many parallel TCP connections to
aggregate bandwidth
6
Source: www.codeproject.com/Articles/28788/Distributed-Computing-in-Small-and-Medium-Sized-Of
Parallel TCP (Cont.)
7
.
.
.
1
2
N
.
.
.
i
Bottleneck capacity c
AIMD connection
Xi = send rate
Source: E. Altman, “Parallel TCP Sockets: Simple Model, Throughput and Validation,” IEEE Infocom, 2006
Parallel TCP – Pros & Cons
 Pros
 Aggregated bandwidth
 More resilient to network layer packet losses
 Only one stream would may experience timeout
 More aggressive behavior
 Slow-start is faster, k x MSS
 Recovery is faster compared to single stream with giant
window
 Only one stream may experience loss
 Multiplicative decrease is effectively 1/(2k) rather than 1/2
 Can work around max TCP buffer size limitations
 k x buffer size
8
Parallel TCP – Pros & Cons (Cont.)
 Cons
 Ideally, each connection should use a different path
 Exploits TCP’s fairness
 Can become unfair to other flows
 Requires changes to applications to support parallel
streams
 May perform worse if loss is due to congestion
 May add to congestion
 Selecting optimum buffer size & number of streams is
hard
9
Parallel TCP Performance
10
Source: www.codeproject.com/Articles/28788/
Distributed-Computing-in-Small-and-Medium-
Sized-Of
Scalable TCP (SCTP)
 Modifies congestion control algorithm
 Each packet loss decreases congestion window
by a factor of 1/8 instead of Standard TCP's ½
congestion window
 When packet loss stops, rate is ramped by
adding one packet every 100 successful ACKs
 Standard TCP – increase by inverse of congestion
window  very large windows take a long time to
recover
11
TCP friendly Rate Adaptation Based
On Loss (TRABOL)
 UDP
 Fast, best effort, insensitive to congestion
 TCP
 Slow, reliable, sensitive to congestion
 TRABOL
 Fast, best effort, sensitive to congestion
 Depends on end application/user feedback
 End application/user specifies 2 rates
 Target Rate (TR)
 Minimum Rate (MR)
 Congestion control is similar to TCP
 AIMD 12
TRABOL (Cont.)
13
Source: A. Trimmer et al.,
“Performance of High-
Bandwidth TRABOL Protocol
for Radar Data Streaming,”
IEEE Region 5 TPS
Conference, 2006.
TRABOL Performance
14
Application aWare Overlay Networks
(AWON)
 Packets are marked based on application requirements
 Drop packets in an application-aware manner
 Multicast nodes send aggregated requests to source
nodes
15
Source: T. Banka et al., “An Architecture and a Programming Interface for Application-Aware Data
Dissemination Using Overlay Networks,” COMSWARE '07, 2007.
Application-Specific Data Sample
Selection
16
Content-Based Packet Marking
17
ADU – Application Data Unit
On-The-Fly Data Selection
18
• Compensation for lost packets
• Select a packet from a higher rate
AWON Performance
19
Measurements on PlanetLab
Other Solutions
20
Source: http://cloudcomputingseminar.wordpress.com/2012/06/16/unit-2-grid-computing/
• XTP – Xpress Transport Protocol - Fast & light weight
• RBUDP – Reliable Blast UDP - high-bandwidth, reliable
• Tsunami – Improvement of RBUDP

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Protocols for Fast Delivery of Large Data Volumes

  • 1. Protocols for Fast Delivery of Large Data Volumes CS4482 High Performance Networking Dilum Bandara Dilum.Bandara@uom.lk Some slides extracted from Dr. Dan Massey’s CS557 class at Colorado State University
  • 2. High Data Volume Applications  High definition video streaming  Ultra high-definition video  Sensor networks  Video surveillance  Radar networks  Array of Radio telescopes  Data transfer between grids/clouds  Data transfer from CERN  Virtual reality  Holographic 3D display Some applications require ordered delivery others don’t 2 Source: NAIC/Arecibo Obs/NSF Source: www.idrshare.com
  • 3. Latency Bandwidth Tradeoff  Bandwidth is increasing  10-100 Gbps networks  Latency is not reducing  Speed-of-light limitation  Small transfers are latency limited  telnet, ssh, chat messages, small file transfer  Large transfers are still bandwidth limited  Bulk transfer of files 3
  • 4. Bulk Transfer 4 Source: N. Tolia et al., “An Architecture for Internet Data Transfer,” NSDI ‘06, 2006
  • 5. TCP Design Assumptions  Low physical link error rates  Packet loss = congestion signal  No packet reordering at network (IP) level  Packet reordering = congestion signal  These design assumptions are challenged today  Parallel networking hardware  Packet reordering  Dedicated links or reservations  No congestion  Bulk transfers  Streaming not needed 5
  • 6. Parallel TCP  Create many parallel TCP connections to aggregate bandwidth 6 Source: www.codeproject.com/Articles/28788/Distributed-Computing-in-Small-and-Medium-Sized-Of
  • 7. Parallel TCP (Cont.) 7 . . . 1 2 N . . . i Bottleneck capacity c AIMD connection Xi = send rate Source: E. Altman, “Parallel TCP Sockets: Simple Model, Throughput and Validation,” IEEE Infocom, 2006
  • 8. Parallel TCP – Pros & Cons  Pros  Aggregated bandwidth  More resilient to network layer packet losses  Only one stream would may experience timeout  More aggressive behavior  Slow-start is faster, k x MSS  Recovery is faster compared to single stream with giant window  Only one stream may experience loss  Multiplicative decrease is effectively 1/(2k) rather than 1/2  Can work around max TCP buffer size limitations  k x buffer size 8
  • 9. Parallel TCP – Pros & Cons (Cont.)  Cons  Ideally, each connection should use a different path  Exploits TCP’s fairness  Can become unfair to other flows  Requires changes to applications to support parallel streams  May perform worse if loss is due to congestion  May add to congestion  Selecting optimum buffer size & number of streams is hard 9
  • 10. Parallel TCP Performance 10 Source: www.codeproject.com/Articles/28788/ Distributed-Computing-in-Small-and-Medium- Sized-Of
  • 11. Scalable TCP (SCTP)  Modifies congestion control algorithm  Each packet loss decreases congestion window by a factor of 1/8 instead of Standard TCP's ½ congestion window  When packet loss stops, rate is ramped by adding one packet every 100 successful ACKs  Standard TCP – increase by inverse of congestion window  very large windows take a long time to recover 11
  • 12. TCP friendly Rate Adaptation Based On Loss (TRABOL)  UDP  Fast, best effort, insensitive to congestion  TCP  Slow, reliable, sensitive to congestion  TRABOL  Fast, best effort, sensitive to congestion  Depends on end application/user feedback  End application/user specifies 2 rates  Target Rate (TR)  Minimum Rate (MR)  Congestion control is similar to TCP  AIMD 12
  • 13. TRABOL (Cont.) 13 Source: A. Trimmer et al., “Performance of High- Bandwidth TRABOL Protocol for Radar Data Streaming,” IEEE Region 5 TPS Conference, 2006.
  • 15. Application aWare Overlay Networks (AWON)  Packets are marked based on application requirements  Drop packets in an application-aware manner  Multicast nodes send aggregated requests to source nodes 15 Source: T. Banka et al., “An Architecture and a Programming Interface for Application-Aware Data Dissemination Using Overlay Networks,” COMSWARE '07, 2007.
  • 17. Content-Based Packet Marking 17 ADU – Application Data Unit
  • 18. On-The-Fly Data Selection 18 • Compensation for lost packets • Select a packet from a higher rate
  • 20. Other Solutions 20 Source: http://cloudcomputingseminar.wordpress.com/2012/06/16/unit-2-grid-computing/ • XTP – Xpress Transport Protocol - Fast & light weight • RBUDP – Reliable Blast UDP - high-bandwidth, reliable • Tsunami – Improvement of RBUDP

Editor's Notes

  1. (At source, at multicast node)