HybridTE Traffic Engineering for Very Low-Cost Software-Defined Data-Center Networks. Philip Wette, Holger Karl Universität Paderborn, Germany

Transkript

1 HybridTE Traffic Engineering for Very Low-Cost Software-Defined Data-Center Networks Philip Wette, Holger Karl Universität Paderborn, Germany 2 /

2 M ' +, ' % ( ) (*% $ &% T " /.DB2.-3B/ /.DB2.-3B/H- P 54G0/14E34 F I SQ:K:ON:KJ Traffic engineering C in a network to improve desirable performance metrics *"%cs: A 42@34? R 210/.- P 540/03-03 E14/@Ly 63-arding disciplines 7 <;:98 = > = 3 /

3 4 / U V W X 7 Y Z [ ^]@34\4 _ ` cdcba ` F-?4 6-G _] ea f g h h ddcb h i-3e0 j, CoNEXT k l m n po mq 7 k Y Y utsr = v w y z v } ~ } ~ non-standard features or have too high requirements on the switches z v } ~ } ~ 0L \4/0 6-GH 4G@/ L \4/ E-3D 540/0G0@D@E 0L \4/0 4-3ƒ \44D5 ˆ Š Š Š Š Œ ˆ Œ Š Ž ŠŒ Š

4 Goal Design a routing algorithm / TE technique for data centers built from low-cost off-the-shelf OpenFlow switches 5 /

5 ~ y z y ~ y š Data Center Traffic Properties alternative paths typically distributed (Big Data, multi-tier) 6 / Ÿ ž of all flows smaller than 10 KB bytes are transported by a few elephant flows T. Benson et. Al. Network Traffic Characteristics of Data Centers in the Wild. In Proceedings of IMC ACM.

6 7 / z V z W y y flows g \G [] 2 R 0G@4/ƒ 5 E4BE 6-GHGG@ 5]54G@/ 4/@\D.GG@ 6-G5]54/@3 4-3ƒ _ 0 4GD 4BE5 ª4 Concentrate effort

7 º ½ Á À ¹ ² ± HybridTE HybridTE: "If we can't route all flows individually, let's only try the elephants" 01atially handle all flows using proactively installed default routes -/ 5 6-GH/1@BD4G4/.-@ 3-H10 45 ³ ² µ¾ ³ ½ ¼» º µ ³ ² Controller simple scheme? 8 /

8 ~ ~ Â ) Ç $ ' +% " Æ (Å + ( Ä % Ã y y U Hybrid TE: Main ingredients 9 /

9 È ) "% Ì '* $ Ë" Ê" (Å ( ' + É [ 210E1@G@Í\@-GH- + Õ Ñ Ô ( Å $" Ó +* ÃÒ Ç *%Ñ + Ê ) " + Ê (Æ ) ' Ð Ï Ã + Ä "* Î Ã HybridTE Default Routes Destination Routing : Initially spread the traffic among all available paths "destination rack /@random along shortest paths \4/E.3/51-E / j BE@ 6 5\3@E\G0 1- \45@ 0?@4Gƒ 44324\ ?02 \ƒ ' *"% (ÆÅ Ê 10 /

10 Â Ç $ Ù Ø Ê Ä % (" $ ' ' Ñ % +* Ç% & ("Å ) $ %" Ö [ h Ë Ä % (" " $ ) Ç $ ' +% Æ Ú /1@BD4G4 4B//.-@ 1-0/@ HybridTE Elephant Rerouting Controller ~ V initially no +%* Ë 54G.3 no byte count, data rate, it hard to make proper routing decisions information about the elephant 6 \3@E\G0 4B/ \10B4 210\0 least loaded shortest path Ç% (% ÆÆÛ $*%* '* " 3-H10/E4GG-E 1@E 4 6]210/.-343 A 34/H 11 /

11 š Þ ~ v z Ý Ü ß à á ì à h öõôóò î à \4 0 34/H@ \4/E4Dí4 Periodic Rerouting Zhases Compute data rates of background traffic Compute the natural demand âëëêéèåçæåäãâ remove elephants 0í@mum possible data rate if the network were non-blocking (limited at host NICs only) üûú5 Gbps Demand: 1.4 Gbps 3 Gbps 5 Gbps ý þ ù üûú øø ïind optimal routing (NP-complete problem) /5@4G 4B/ 1-ñ34\3- -\1@ B/-1@ 41-ð 5/1@BD4G4 R 4/.-34 loaded shortest path 210/G.543 4/@\D 12 /

12 f 34GG-3/1-, ( "Ô Ê Ë % $Æ Ã P y ÿ V W U Ü 9;J How to recognize elephants? Controller A 51-0/@E0GDD 3ƒ -/@D0E0/3@ 0H2-Lle analysis: HadoopWatch, INFOCOM 2014 D@3i/1@BD4G, Stanford 2007 j 4 0G 54E0?4\ 43@ [ 6\3@ SQ:K:ONO8 p to the SDN controller Q: SQJ N S J J J Q: S p /5@4G/@n packets \4?04E43/0H0/1@BD4G4 5@ 534\051-E ª4GD 05 \1@ D@4B R f GG@E4 13 /

13 «J : J 9o Q;N JKJ 8 KKN OK NQ p ï C v y C Evaluation Plot line HybridTE viable with limited information available in practice? S N Q: SQ NQ achieve them in practice 8Q S Q o ;8:<J KN;8:QN J 8 14 / ly: Evaluated HybridTE using different numbers 8false positives: Some mice erroneously treated as elephants -false negatives: Some elephants erroneously treated as mice -reporting delays: how long does it take to detect an elephant? H H Cand Hedera

14 ~ v \4/@G. j z C Â y ~ C 5\1-E45 E0HH@3/34/14EÍ@/@\ E0/50G@43 Gƒ [ B20 T Evaluation scenario srhosts per rack 72 racks in total, 2 core switches, 8 racks per pod / 2 pod switches, 10G full-duple links ]5410BE@ E 1-/410í@ time dilation Gen ž \4/@G. 05 c j BE@ same statistical properties as reported by Benson et al. 15 /

15 V Ý š Â L " " " " Ý y ï W v Evaluation scenario y evel 10/ G links $ 10 G links % 5 G links ric: Average flow completion time 16 /

16 I [ \ ZYX WV U RMô Q PONMKJú I /. - +F 0 * = * 8 = <5 ; < <, / H, ;, < 1 0, - > Results Load level 1, false negatives RMô Q PONMKJú _^Y ] 50% of all elephants reported 100% of all elephants 75% of reported all elephants reported D D B & A > < = 4; 1 0 :5; <,5; 08 * :, / 9, ,+ * ) ( ' & 65+, 4;5 9 0; 15 1 ) ( ' & :+,7+ 0 *;. =+ 0-0 <,5; = :+,7 <5 0+, : 4; ;; 1 +G, 9 & A > 17 /

17 g e Results Load level 1.5, false negatives È between HybridTE and ECMP increases with increasing load 1average, ECMP yields 20% higher flow completion times 18 / for higher load levels

18 T Ú Ë*" $ Ç% + &*% ) ( & Ñ Ê (ÆÅ " Ì" $Æ ËÆ * Û Results Load level 1.75, false negatives h Û g ' ' (*% Æ f ed c b + a M ` Ú 19 /

19 z v Ý Ü w v Ý ~ y y z Ý Ü Impact of False Positives on the Flow Completion Time i load level 1, increasing false positives rate j Controller š v 20 /

20 Š Œ Š v Œ p on l? =8 0-6 <5;+, * 0 k 4; 7, ;F < 0 /+ 0 * <5H 0 1 = 0+ / 0 m v s Š vu q w u Š t s ˆ ŒŽ p Š r q ˆ Time between the start of an elephant and it's report to HybridTE 0flow completion time relative to ECMP results on flow trace 7 at load level 1 21 /

21 U Elephant Detection Through Simple Packet Sampling ÿ Ý yprobability to send packets; 1number of packets to see before classifying a flow as elephant 22 /

22 23 / Ü Ý z v ~ v C C Ü realistic traffic j f h P ƒ 5\G40 D. -/ zb 3423@G 6-GH -E 1-0/4GD 0/ 54 1@B/ [ ƒ i\03 j Ü Ý z v š } ~ y } S J J S S 9 N 9K: J: Q;J J N ~ 9J ^ 34?4 43-1@B/ cc 5/1@BD4G4 \4/50í4Í-E 43ƒ 6-G 4/@3 6-GHH- 5\-(in case not too many false positives are reported) Ý Ü z v y š j 53-/E4/4\/1@BD4G4G@134/í 1@E 4 \45. 6 BE0B 4/@43Euncertain information about elephants Conclusion

23 Thank you for your attention. Holger Karl 24 /

24 ƒ ˆ Š Œ Ž ƒ Ž ƒ Š 25 /

25 Ÿ ž ì W y š Þ +, Ä " Ì*Å $ "Ô & Õ Ÿ š b $ Ù $ Ò c b Ÿ b Ç *%Ñ Ê ( Ù ž* (Æ ) ' Ð Ï ' Ñ Ë $ $% '* Ì Ä '*% (" a A Strawman: Per-flow routing? š0 new flows/sec to saturate a full-duple 10G link šø š b new flows/sec to fully utilize a 48-port switch Ì '*% b '* Æ h $Æ* 5403/14 6-GH hd -/ D E45 34D 51-0/@GG@/510 6-GH bd -/ D Ênot possible today Ç%* Ê +" % Ê (ÆÅ " 54BE/ G F14D ghg4b5í4b/íhh- B/0 6/-1/5@4G/ 26 /

26 ~ z V Ä Ä )% Ï DCT 2 Gen Traffic Generator for Data Centers ì +%a schedule of TCP connections b 34L@ƒ \4/.E4í4 50 4G.\4BE5 4B/ 14B traffic as given before 27 /

27 ì W z " «ª " ± ± " «ª ² " «ª ² 2 Gen t C 8 ;8:Q~O: Q : low arrival times ªntra-rack communication partners per server ªnter-rack communication partners per server "ª " ª ª µ ³ ª "ª " ª ª µ ³ " ª P. Wette, H. Karl: DCT 2 Gen: A Versatile TCP Traffic Generator for Data Centers. arxiv: (2014) (to be published) 28 /

28 X ¾ ]^ ½ º» ¼ ^XY ¹ º¼ÁÀ MaiNet Distributing Mininet to Multiple Machines winet is a framework for distributing Mininet emulations onto a cluster of servers (called workers)  À ¾ X X Ã Ä ¾ X Â^ P. Wette, M. Dräler, A. Schwabe, F. Wallaschek, M. Hassan Zahraee, H. Karl: MaiNet: Distributed Emulation of Software-Defined Networks. In Proceedings of the 2014 IFIP Networking Conference (Networking 2014). IEEE, pp. 1-9 (2014) 29 /

29 ï z MaiNet at a Glance winet partitions the virtual topology in N parts partition a new topology is built and emulated using Mininet at a dedicated worker routed over the physical network È : minimize edge cut 30 /

30 ˆ Š ˆ ˆ Œ v Š Ž Æ ˆ Œ ŒŽ Å Ž o o ˆ ÌKKËõ Ê É ÈMÇõ ô ÇM ù Worker 2 Worker 3 s2 s3 ì 31 /

31 š ÇM ù Communication over Partition Boundaries Ípairs are only internal to the Linu kernel Ì KKËõ Ê É ÈMÇõ ô Worker 2 Worker 3 s2 s3 32 / ì

32 š ÇM ù Communication over Partition Boundaries Ípairs are only internal to the Linu kernel Ì KKËõ Ê É ÈMÇõ ô Worker 2 Worker 3 s2 s3 33 / ì

33 š ÇM ù Communication over Partition Boundaries Ípairs are only internal to the Linu kernel Ì KKËõ Ê É ÈMÇõ ô Worker 2 Worker 3 s2 s3 34 / ì

34 Š Œ o Ž Š p Î s uu ˆ ˆŽŒ ÌKKËõ Ê É ÈMÇõ ô ÇM ù Worker 2 Worker 3 s2 s3 ì 35 /

35 (Frontend Node) Controlling an Eperiment coordinate eperiments ÌKKËõ Ê É ÈMÇõ ô ÇM ù Worker 2 Worker 3 s2 s3 Òtend Ñ Ð Ï ì 36 /

36 (Frontend Node) Controlling an Eperiment coordinate eperiments ÌKKËõ Ê É ÈMÇõ ô ÇM ù Worker 2 Worker 3 Ô Óò s2 s3 RPC RPC Òtend Ñ Ð Ï ì 37 /

37 '* '* M Ç% (% + (Æ Ù "Û Ñ +* Ç +* T Ö y ~ ~ Ý C š w y ñ E/4]@ƒ G4\]55-G/4 E@Dð 5E0/5034/E@3@BE 10GH- Specifying Eperiments with MaiNet winet has its own Python API %API Õ a Ö 5\1@ -E E0H0E4D5/4 ^0í@ h B/0 \4/14 P 2.@a A/41010 h G@E05@Gƒ transparent to the user y winet also supports dynamic changes to the topology at runtime 1 6-\ D. 10L f 421@B J Q: S 9;N Q 8 <;: 8 JØ Ù <;:99N SKKN : NØ ;S 38 /

38 ', (*% Ä $Æ % & $ * M % $ " + $% (% ( Ë Ñ ( ' Ç% Û a % ) Æ ( & ` Code Eample 1 import mainet 2 import TreeTopo from mininet.topolib 3 4 cluster = mainet.mininetcluster("pc1","pc2"," 5 cluster.start() 6 7 ep = mainet.eperiment(cluster, TreeTopo(3,2)) 8 ep.addcontroller(" ", " 9 ep.setup() print ep.get("h2").cmd("ping -c5 12 print ep.get("h5").cmd("ping ep.stop() 15 cluster.stop() 39 /

39 ï Speedups using MaiNet Tree: linear speedup, but no gain from adding a fourth worker 443/ bottleneck e than linear speedup for Switch Pairs 4B/H-/--3 4B/ B2.-3B/ A E0HH@3/GG 40 /

40 ƒ ˆ Š Œ Ž ƒ Ž ƒ Š 41 /