Topic 6: SDN in practice: Microsoft's SWAN

Student: Miladinovic Djordje Date: 17.04.2015

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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

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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

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Introduction to SWAN: MPLS issues

Key problems:

1. Lack of coordination among DC services

2. Distributed resource allocation

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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

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Introduction to SWAN: MPLS issues

Key problems:

1. Lack of coordination among DC services

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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

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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

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Introduction to SWAN: MPLS issues

Key problems:

2. Distributed resource allocation

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Introduction to SWAN: MPLS issues

Key problems:

2. Distributed resource allocation

SWAN Solution:

• Leverage the centralized view of the network to find

global optimum

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Introduction to SWAN: Overview

Key features:

• Priority classes of services

• Background

• Elastic

• Interactive

• Within a class of services: max-min fair allocation

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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)

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SWAN Design: Architecture

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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

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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

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SWAN Design: Implementation

Key challenges:

1. Efficient scalable

global allocation algorithm

needed

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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

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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

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SWAN Design: Implementation

Key challenges:

3. Atomic reconfiguration of the system is hard to engineer

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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

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SWAN Evaluation: Testbed-based Setup:

• Modest size

• Varying the demands (not synchronized among DCs)

Goal: • Examine congestion-

controlled updates

Results: • Close to optimal throughput

• Updates are quick and do not cause overhead

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SWAN Evaluation: Data-Driven Setup:

• IDN: more than 40 DCs

• G-Scale: 12 DCs

Network

Utilization:

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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

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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

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Questions, Comments, Suggestions