Airlift: Video Conferencing as a Cloud Service using Inter- Datacenter Networks

Transcription

1 Airlift: Video Conferencing as a Cloud Service using Inter- Datacenter Networks Yuan Feng Baochun Li Bo Li University of Toronto HKUST 1

2 Multi-party video conferencing 2

3 Multi-party video conferencing Source Conferencing session Bandwidth demanding and delay sensitive! 2

4 Existing solutions 3

5 Existing solutions Client-server Google+ Hangouts Dedicated server re-distributes video flows Resource bottleneck as the system scales up 3

6 Existing solutions Client-server Google+ Hangouts Dedicated server re-distributes video flows Resource bottleneck as the system scales up Peer-to-peer Skype Peers in the same conferencing session cooperate to relay video flows 3

7 Peer-to-Peer Bottleneck 1 3 link 2 4 4

8 Peer-to-Peer Bottleneck 1 3 link 2 4 4

9 Peer-to-Peer Bottleneck 1 3 link 2 4 needs to minimize the load on the bottleneck 4

10 Peer-to-Peer Bottleneck 1 3 link 2 4 needs to minimize the load on the bottleneck But where is the bottleneck? 4

11 With a single datacenter 1 3 D

12 With a single datacenter 1 3 D

13 With a single datacenter 1 3 D

14 With a single datacenter 1 3 D1 2 4 may still send multiple copies at the bottleneck 5

15 Inter-datacenter networks 6

16 Inter-datacenter networks Cloud providers usually deploy a number of geographically distributed datacenters e.g., Amazon EC2 6

17 Inter-datacenter networks Cloud providers usually deploy a number of geographically distributed datacenters e.g., Amazon EC2 Those datacenters are inter-connected through high-capacity links 6

18 Inter-datacenter networks Cloud providers usually deploy a number of geographically distributed datacenters e.g., Amazon EC2 Those datacenters are inter-connected through high-capacity links Can we use inter-datacenter networks in the cloud to support better video conferencing services? 6

19 With the inter-datacenter network 1 3 D1 D

20 With the inter-datacenter network 1 3 D1 D

21 With the inter-datacenter network 1 3 D1 D

22 With the inter-datacenter network distribute the video on behalf 1 3 of the sender D1 D2 2 4 Maximize the flow rate naturally, yet with a simplified protocol design 7

23 Motivation through experiments Cloud / P2P Throughput (Mbps) Delay (msec) Toronto-Beijing 2.2 / / 148 Cambridge-Sao Paulo 1.7 / / 204 Seoul-Moscow 7.2 / / 436 Substantially improved throughput with similar delays! 8

24 Our design: Airlift 9

25 Key idea 10

26 Key idea Source video 10

27 Key idea Full-service broker 10

28 Key idea Full-service broker Rate control 10

29 Key idea Full-service broker Rate control 10

30 Key idea Full-service broker Rate control 10

31 Aggregated sessions Aggregating video flows from the same source datacenter to the same subset of destination datacenters Each aggregated session is a multicast session in the inter-datacenter network 11

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36 Objective: maximize the total throughput with a constraint on endto-end delays 12

37 Maximizing the total throughput across all multicast sessions Traditional wisdom resorts to Steiner tree packing, which is NP-Complete In Airlift, we use network coding to formulate the optimization problem as a linear program 13

38 Conceptual flows with network coding Source D1 D2 14

39 Conceptual flows with network coding Source a b a b a a+b b a+b a+b D1 D2 14

40 Conceptual flows with network coding Source a b a b a a+b b a+b a+b D1 D2 14

41 Conceptual flows with network coding split Source replicate a b The effective flow is the merge maximal a b conceptual flow a a+b b in the same session a+b a+b D1 D2 14

42 Realizing conceptual flows (1) Replicate (2) Split (3) Merge 15

43 Realizing conceptual flows (1) Replicate (2) Split (3) Merge Source routing 15

44 Realizing conceptual flows (1) Replicate (2) Split (3) Merge Source routing Perform network coding with incoming flows 15

45 Formulating the problem max s.t. X Xapple X x i j(p)/w i, 8i, j =1,...,k i X p2p i j x i j(p) apple x i (e), 8i, j =1,...,k i p2pj i(e) X x i (e) apple C(e), 8e 2E i x i j(p) 0, x i (e) 0, X 0, 8p 2P i j, 8i, j =1,...,k i, 8e 2E 16

46 Formulating the problem max s.t. X X Total throughput Xapple x i j(p)/w i, 8i, j =1,...,k i X p2p i j x i j(p) apple x i (e), 8i, j =1,...,k i p2pj i(e) X x i (e) apple C(e), 8e 2E i x i j(p) 0, x i (e) 0, X 0, 8p 2P i j, 8i, j =1,...,k i, 8e 2E 16

47 Formulating the problem max s.t. X X Total throughput Xapple x i j(p)/w i, 8i, j =1,...,k i X p2p i j Weighted proportional fairness x i j(p) apple x i (e), 8i, j =1,...,k i p2pj i(e) X x i (e) apple C(e), 8e 2E i x i j(p) 0, x i (e) 0, X 0, 8p 2P i j, 8i, j =1,...,k i, 8e 2E 16

48 Formulating the problem max s.t. X X Total throughput Xapple x i j(p)/w i, 8i, j =1,...,k i X p2p i j Weighted proportional fairness x i j(p) apple x i (e), 8i, j =1,...,k i p2pj i(e) X Effective flow on edge e x i (e) apple C(e), 8e 2E i x i j(p) 0, x i (e) 0, X 0, 8p 2P i j, 8i, j =1,...,k i, 8e 2E 16

49 Formulating the problem max s.t. X X Total throughput Xapple x i j(p)/w i, 8i, j =1,...,k i X p2p i j Weighted proportional fairness x i j(p) apple x i (e), 8i, j =1,...,k i p2pj i(e) X Effective flow on edge e x i (e) apple C(e), 8e 2E i Capacity constraint on edge e x i j(p) 0, x i (e) 0, X 0, 8p 2P i j, 8i, j =1,...,k i, 8e 2E 16

50 Formulating the problem max s.t. X X Total throughput Xapple x i j(p)/w i, 8i, j =1,...,k i X p2p i j Weighted proportional fairness Linear program that x i j(p) apple x i (e), 8i, j =1,...,k i p2pcan j i(e) be solved by any X Effective flow on edge e xstandard i (e) apple C(e), LP 8e 2E solver i Capacity constraint on edge e x i j(p) 0, x i (e) 0, X 0, 8p 2P i j, 8i, j =1,...,k i, 8e 2E 16

51 Having the optimal flow rate in each aggregated session, what should be the protocol design in Airlift? 17

52 Transport with network coding Packets in a generation Coded packets h e h e Video flow l Encode Decode l l o l o generations One coded packet = c 1 h+c 2 e+c 3 l+c 4 l+c 5 o 18

53 Transport with network coding Packets in a generation Coded packets h e h e Video flow l Encode Decode l l o l o generations One coded packet = c 1 h+c 2 e+c 3 l+c 4 l+c 5 o Use UDP as the transport protocol, together with TFRC 18

54 The sliding window Combine the design of a sliding window with generations A sliding window shows all packets that have been sent but haven t been acknowledged at the destination Destination will hold multiple buckets for different generations, and will acknowledge the degrees of freedom 19

55 Sliding window Generation 1 Generation RTT Source S i Time ACK (4,3,2) ACK (3,2) ACK (4,2) ACK (2) Destination R i j Time Active bucket Inactive bucket 20

56 Performance evaluation 21

57 Experimental setup We have implemented Airlift, and used the Amazon EC2 inter-datacenter network in our experiments Used PlanetLab nodes as users Logged statistics every 30 seconds, and used link capacities measured live Compared with P2P 22

58 End-to-end delay (Airlift) Total throughput (Airlift) End-to-end delay (P2P) Total throughput (P2P) msec Mbps 23

59 230 End-to-end delay (Airlift) Total throughput (Airlift) End-to-end delay (P2P) Total throughput (P2P) msec Mbps Toronto-Beijing Vancouver-Berin Seoul-Rio 0 23

60 230 End-to-end delay (Airlift) Total throughput (Airlift) End-to-end delay (P2P) Total throughput (P2P) msec Mbps Toronto-Beijing Vancouver-Berin Seoul-Rio 0 23

61 230 End-to-end delay (Airlift) Total throughput (Airlift) End-to-end delay (P2P) Total throughput (P2P) msec Mbps Toronto-Beijing Vancouver-Berin Seoul-Rio 0 23

62 230 End-to-end delay (Airlift) Total throughput (Airlift) End-to-end delay (P2P) Total throughput (P2P) The total throughput is msec to 24 times better, with similar delays 20 Mbps Toronto-Beijing Vancouver-Berin Seoul-Rio 0 23

63 Airlift: towards cloud-based video conferencing Uses inter-datacenter networks to provide a multi-party video conferencing service with high quality The objective is to maximize the total throughput within a delay constraint 24

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65 Thank you iqua.ece.toronto.edu 25