PINK: Proactive INjection into ack, a queue manager to impose fair resource allocation among TCP ows

Transcription

1 PINK: Proactive INjection into ack, a queue manager to impose fair resource allocation among TCP ows Carlo A. Grazia, Martin Klapez, Natale Patriciello, Maurizio Casoni Department of Engineering Enzo Ferrari University of Modena and Reggio Emilia TCenter Abu Dhabi, 19 October 2015 IEEE WiMob 2015 Workshop on Emergency Networks for Public Protection and Disaster Relief (EN4PPDR'15) C. A. Grazia (PhD Student) Active Queue Management 19 October / 17

2 The PPDR-TC project: Public Protection and Disaster Relief - Transformation Center PPDR-TC goals Eective Public Protection & Disaster Relief (PPDR) communications Preparation of the next generation of PPDR systems The Consortium: C. A. Grazia (PhD Student) Active Queue Management 19 October / 17

3 Talk overview 1 Introduction Problem State-of-the-art 2 PINK Description 3 Results 4 Conclusions C. A. Grazia (PhD Student) Active Queue Management 19 October / 17

4 Problem what to support PPDR communications avoid congestion provide fairness why resources are precious after a disaster satellite tech are often the only solution (TCP problems) recover from congestion is harder with high RTT where network layer: buer management C. A. Grazia (PhD Student) Active Queue Management 19 October / 17

5 Problem: simple PPDR scenario C. A. Grazia (PhD Student) Active Queue Management 19 October / 17

6 State of the Art typical solution Congestion is faced with AQM algos: RED, CoDel, BLUE, ChoKe, etc... In satellite environment, TCP congestion is the critical point weaknesses avoid congestion means smooth trac (bandwidth fairness) dicult to bound the bandwidth packets in the queue IP level bandwidth (congestion control) TCP level for bandwidth fairness, high RTT ow needs more packets most of AQM care about packets, not bandwidth, except... C. A. Grazia (PhD Student) Active Queue Management 19 October / 17

7 State of the Art: GREEN Algorithm specic solution GREEN aims to smooth trac with a smart packet drop probability: ( ) 2 N MSS C p drop =. L RTT Depends on ow RTT, ows number (N), channel bandwidth (L) The higher the RTT, the lower the drop probability Trace RTT for a ne-grained drop is the key point At a rst look, promising for high RTT environment weaknesses We should have a look to the drop probability function C. A. Grazia (PhD Student) Active Queue Management 19 October / 17

8 State of the Art: GREEN function with 10Mbps channel Drop Probability Round Trip Time (ms) 10 Nodes 100 Nodes 1000 Nodes C. A. Grazia (PhD Student) Active Queue Management 19 October / 17

9 Proposed solution: PINK how? a simple no-drop AQM born for TCP trac, transparent for non TCP ows it traces packets to get the ow RTT and calculate the optimal ow rate it gives the optimal ow rate to the ow sender through the rcvwnd ACK eld C. A. Grazia (PhD Student) Active Queue Management 19 October / 17

10 Proposed solution: PINK Sender uses min(cwnd, rcvwnd) ACKs rcvwnd take min(rcvwnd, pinkwnd) TCP rcvwnd is computed TCP Source Router TCP Receiver limitations Information Flow Control/ACK Flow PINK equation: advwnd i = L RTT min i N ACK must pass through the same node: MEOC, Edge/Home Router If is there not only TCP ows? Use scheduling and operate with the new L in the PINK queue with only TCP ows PINK recomputes the checksum, unfeasible for backbone network C. A. Grazia (PhD Student) Active Queue Management 19 October / 17

11 PPDR testbed with ns-3 C. A. Grazia (PhD Student) Active Queue Management 19 October / 17

12 PPDR testbed with ns-3 C. A. Grazia (PhD Student) Active Queue Management 19 October / 17

13 CoDel vs RED: Rate and fairness 4 ns-3 client nodes, backlogged delayed of 5 seconds each, same RTT of 700ms Throughput (Mb/s) Throughput (Mb/s) Time (s) Time (s) Flow 1 Flow 2 Flow 3 Flow 4 Aggregate Flow 1 Flow 2 Flow 3 Flow 4 Aggregate CoDel RED C. A. Grazia (PhD Student) Active Queue Management 19 October / 17

14 GREEN vs PINK: Rate and fairness 4 ns-3 client nodes, backlogged delayed of 5 seconds each, same RTT of 700ms Throughput (Mb/s) Throughput (Mb/s) Time (s) Time (s) Flow 1 Flow 2 Flow 3 Flow 4 Aggregate Flow 1 Flow 2 Flow 3 Flow 4 Aggregate GREEN PINK C. A. Grazia (PhD Student) Active Queue Management 19 October / 17

15 CoDel vs RED: Rate and fairness, dierent RTTs 4 ns-3 client nodes, backlogged, with 700, 750, 800 and 850ms or RTT Throughput (Mb/s) Throughput (Mb/s) Time (s) Time (s) Flow 700ms Flow 750ms Flow 800ms Flow 850ms Aggregate Flow 700ms Flow 750ms Flow 800ms Flow 850ms Aggregate CoDel RED C. A. Grazia (PhD Student) Active Queue Management 19 October / 17

16 GREEN vs PINK: Rate and fairness, dierent RTTs 4 ns-3 client nodes, backlogged, with 700, 750, 800 and 850ms or RTT Throughput (Mb/s) Throughput (Mb/s) Time (s) Time (s) Flow 700ms Flow 750ms Flow 800ms Flow 850ms Aggregate Flow 700ms Flow 750ms Flow 800ms Flow 850ms Aggregate GREEN PINK C. A. Grazia (PhD Student) Active Queue Management 19 October / 17

17 Worst-Case tx time: 2-32 nodes, 5MB of le transfer Completion Time (s) # Nodes Red Green CoDel Pink C. A. Grazia (PhD Student) Active Queue Management 19 October / 17

18 Conclusions PINK a novel no-drop AQM for TCP fairness without congestion deterministic (no)drop policy eective use of the typical BDP standard buer-size optimal run-time time and space complexity RTT-independent and transparent for TCP end nodes outperforms existing AQM algos, rate-based or not C. A. Grazia (PhD Student) Active Queue Management 19 October / 17

19 thank you for your attention

20 extra slides

21 we will see