Topic 6: SDN in practice: Microsoft's SWAN. Student: Miladinovic Djordje Date:

Transcription

1 Topic 6: SDN in practice: Microsoft's SWAN Student: Miladinovic Djordje Date:

2 SWAN at a glance Goal: Boost the utilization of inter-dc networks Overcome the problems of current traffic engineering practice Why improve inter-dc?: Has a significant fraction and is rapidly growing An expensive dedicated WANs Current WANs have poor efficiency Idea: Use SDN centralization logic to improve efficiency of inter-dc WANs 2

3 Background: MPLS TE Goal: Efficient usage of network capacity Principles: Load balancing of the traffic (typically ECMP) Network path calculation using CSPF Service differentiation for prioritizing Different services mapped to different tunnels DSCP bits 3

4 Introduction to SWAN: MPLS issues Key problems: 1. Lack of coordination among DC services 2. Distributed resource allocation 4

5 Introduction to SWAN: MPLS issues Key problems: 1. Lack of coordination among DC services Services on the network send packets whenever they want and however much traffic they want Capacity defined by the traffic peaks Leads to under-subscription Waste of bandwidth 5

6 Introduction to SWAN: MPLS issues Key problems: 1. Lack of coordination among DC services 6

7 Introduction to SWAN: MPLS issues Key problems: 1. Lack of coordination among DC services SWAN Solution: Exploit delay-tolerant services Global coordination of the sending rate of services 7

8 Introduction to SWAN: MPLS issues Key problems: 2. Distributed resource allocation No entity has a global view Greedy resource allocation We are stuck in local optimum 8

9 Introduction to SWAN: MPLS issues Key problems: 2. Distributed resource allocation 9

10 Introduction to SWAN: MPLS issues Key problems: 2. Distributed resource allocation SWAN Solution: Leverage the centralized view of the network to find global optimum 10

11 Introduction to SWAN: Overview Key features: Priority classes of services Background Elastic Interactive Within a class of services: max-min fair allocation 11

12 Introduction to SWAN: Overview Key features: How it works: 1. All services except interactive ones talk to the controller 2. Controller computes service rates and network paths 3. Controller updates the switches Forwarding is label-switched (VLAN IDs used) 12

13 SWAN Design: Architecture 13

14 SWAN Design: Implementation Key challenges: 1. Efficient scalable global allocation algorithm needed 2. Limited number of forwarding rules on the switches 3. Atomic reconfiguration of the system is hard to engineer 14

15 SWAN Design: Implementation Key challenges: 1. Efficient scalable global allocation algorithm needed Maximize network utilization subject to: 1. Service priorities 2. Max-min fairness principle The algorithm features: 1. Uses Linear Programming 2. Approximation for max-min fairness principle 15

16 SWAN Design: Implementation Key challenges: 1. Efficient scalable global allocation algorithm needed 16

17 SWAN Design: Implementation Key challenges: 1. Efficient scalable global allocation algorithm needed Problem: 2. Limited number of forwarding rules on the switches Solution: Cut down the number of usable paths and re-run the algorithm 17

18 SWAN Design: Implementation Key challenges: 3. Atomic reconfiguration of the system is hard to engineer Enable forwarding state updates: Add new rules before deleting the old ones Avoid causing congestion The updates have to be quick Continue carrying significant traffic during updates 18

19 SWAN Design: Implementation Key challenges: 3. Atomic reconfiguration of the system is hard to engineer 19

20 SWAN Design: Implementation Key challenges: 3. Atomic reconfiguration of the system is hard to engineer SWAN approach: Step by step reconfiguration (find intermediate configurations using LP) Scratch capacity Tradeoff between bandwidth utilization and number of steps SWAN also allows the possibility of having a bounded-congestion in order to exploit full bandwidth 20

21 SWAN Evaluation: Testbed-based Setup: Modest size Varying the demands (not synchronized among DCs) Goal: Examine congestioncontrolled updates Results: Close to optimal throughput Updates are quick and do not cause overhead 21

22 SWAN Evaluation: Data-Driven Setup: IDN: more than 40 DCs G-Scale: 12 DCs Network Utilization: 22

23 SWAN Evaluation: Data-Driven Fairness: Even when the demands cannot be fully met SWAN well approximates max-min fair sharing Network Updates: Scratch capacity based tradeoff Congestion-control benefits 23

24 SWAN Discussion Non-conforming traffic Services can send more than allocated Solution: traffic profiling Truthful declaration Services ask more bandwidth than they can consume Solution: pricing Richer service-network interface Future work: more sophisticated interface between services and the network 24

25 Questions, Comments, Suggestions 25