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VirtualKnotter: Online Virtual Machine Shuffling for Congestion Resolving in

Virtualized DatacenterXitao Wen, Kai Chen, Yan Chen,

Yongqiang Liu, Yong Xia, Chengchen Hu

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Datacenter as Infrastructure

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Congestion in DatacenterCore

Aggregation

Edge

Pod 0 Pod 1 Pod 2 Pod 3

10:1~100:1

2:1~10:1

Packet loss! Queuing

delay!

Degrading Throughput!

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Congestion in the WildGeneral ApproachesProblem FormulationMain DesignEvaluation

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Spatial Pattern

• Unbalanced utilization– Hotspot: Hot links

account for <10% core links [IMC10]

– Spatially unbalanced utilization

Sender

Rece

iver

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Temporal Pattern

• Long congestion event– lasts for 10s of minutes– Individual event has clear spatial pattern

Core

Lin

k In

dex

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Traffic Stability

• Bursty at a fine granularity– Not predictable at 10s or

100s or milliseconds [IMC10][SIGCOMM09]

• Predictable at timescale of 10s of minutes– 40% to 70% pairwise traffic

can be expected stable– 90%+ predictable traffic

aggregated at core links

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General ApproachesProblem FormulationMain DesignEvaluation

Congestion in the Wild

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General Approaches

• Network Layer– Increase network bandwidth

• Fat-tree, BCube, OSA…

– Optimize flow routing• Hedera, MicroTE

• Application Layer– Optimize VM placement

• Expensive• Requires to

upgrade entire DC network

• Not scalable• Requires

hardware support

• Depends on rich path diversity

• Scalable• Lightweight deployment• Suitable for existing

over-subscribed network

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• Virtualization Layer• VM Live Migration

– Keep continuous service while migrating– 1.1x – 1.4x VM memory transfer

Server

VM

Server

DC Network

VM VM VM

Major Cost!

Background on Virtualized DC

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Optimize VM Placement

• Offload traffic from congested link

active VM

idle VM

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Congestion in the WildGeneral ApproachesProblem FormulationMain DesignEvaluation

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Design Goal

• Mitigate congestion– Maximum link utilization (MLU)

• Controllable migration traffic (i.e. moving VM)– Less than reduced traffic

• Reasonable runtime overhead– Far less than target timescale (10s of mins)

Objective

Constraint

Problem Statement

• Input– Topology and routing of

physical servers– Traffic matrix among VMs– Current Placement

• Variable & Output– Optimized Placement

• NP-hardness– Proof: reduced from

Quadratic Bottleneck Assignment Problem

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Related Work

• Optimize VM placement– Server consolidation [SOSP’07]– Fault tolerance [ICS’07]– Network scalability [INFOCOM’10]

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Main DesignEvaluation

Congestion in the WildGeneral ApproachesProblem Formulation

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Inspiration

Stretch the tie violently, making it loose and less tangled.

Solve each tie gently, by carefully reeving the end out of the tie.

Two-step Algorithm

• Fast and greedy• Search for localizing

overall traffic • May stuck in local

minimum

• Fine-grained and randomized

• Search for mitigating traffic on the most congested links

• Help avoid local minimum

Simulated Annealing

Multiway θ-Kernighan-Lin

Topology & Routing

Traffic Matrix

Current VM Placement

Optimized VM placement

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Multiway Θ-Kernighan-Lin (KL)

• Top-down graph cut improvement

• Introduce Θ to limit # of moves

• O(n2log(n))

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Multiway Θ-Kernighan-Lin (KL)

• Top-down graph cut improvement

• Introduce Θ to limit # of moves

• O(n2log(n))

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Multiway Θ-Kernighan-Lin (KL)

• Top-down graph cut improvement

• Introduce Θ to limit # of moves

• O(n2log(n))

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MLU=.60MLU=.53

Simulated Annealing Searching (SA)

• Randomized global searching

• Terminate when obtains satisfied solution, or predefined max depth is reached

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Evaluation

Congestion in the WildGeneral ApproachesProblem FormulationMain Design

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Methodology

• Baseline Algorithm– Clustering-based algorithm– Pro: best-known static optimality– Con: high runtime and migration overhead

• Metrics– MLU reduction without migration overhead– Overhead

• Migration traffic• Runtime overhead

– Simulation results

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MLU Reduction without Overhead

VirtualKnotter demonstrates similar static performance as that of Clustering.

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Migration Traffic

VirtualKnotter shows significantly less migration traffic than that of Clustering.

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Runtime Overhead

VirtualKnotter demonstrates reasonable runtime overhead.

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Simulation Results

53% less congestion

Altogether, VirtualKnotter obtains significant gain on congestion resolving.

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Conclusions

• Collaborative VM migration can substantially resolve long-term congestion in DC

• Trade-off between optimality and migration traffic is essential to harvest the benefit

DC networking projects of Northwestern LIST: http://list.cs.northwestern.edu/dcn

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Thank you!

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Backup

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General Approaches

Cost

Hardware

Support

Scalability

Other Dependen

cy

Increase Bandwidth

High Yes Varies

Optimize Routing Low Yes Low Rich path

diversity

Optimize VM

PlacementLow No High

VM deployme

nt

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Problem Statement

• Objective– Minimize Maximum Link Utilization (MLU)– “Cool down the hottest spot”

• Constraints– Migration traffic – Server hardware capacity– Inseparable VM

• NP-hardness– Proof: reduced from Quadratic Bottleneck Assignment

Problem

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Observation Summary

• Unbalanced jam (spatial)• Long-term congestion (temporal)• Predictable at 10s of minutes scale (stability)

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Two-step Algorithm

Multiway Θ-Kernighan-Lin Algorithm (KL)• Fast search for approximation

Simulated Annealing Searching (SA)• Fine search for better

solution