Cloud Probing

.

Cloud Platforms (taxonomy)

• Cloud Platforms (Amazon)◦ raw access at VM level◦ client decides when and what to migrate

• App Platforms (Heroku)◦ container level◦ heroku packs containers to VMs◦ user has limited access to migrations

• DIY Platforms (Docker)◦ container level◦ manual install at each VM, then automation◦ Docker is a Github for OS images

M.Zhanikeev -- [email protected] -- Cloud Probing -- http://bit.do/150115icm -- 2/24...

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Cloud Populations

APP

Cloud/DC

APP

APP

VM Container

APP

Cloud/DC

APP

APP …

• population =service (heroku,docker, videostreaming 01)

• app can be VM orcontainer

• users can be includedas e2e QoS 04

01 myself+0 "Multi-Source Stream Aggregation in the Cloud" Book on Advanced Content Delivery, Wiley (2014)

04 myself+0 "A holistic community-based architecture for measuring E2E QoS at DCs" IJCSE (2014)


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Related Topics• active probing 03

◦ available bandwidth, bulk transfer, etc.

• delay space and network coordination 07

• Virtual Network Embedding (VNE) 09

• migration cost and energy-efficient clouds◦ migration schedules and greyboxes 05

• fog computing -- clouds at network edge 08

• BigData Networking -- circuits-over-packets in particular 02

03 1+myself "Active Network Measurement: Theory, Methods, and Tools" ITU (2009)

07 myself+1 "Application of Graph Theory to Clustering in Delay Space" APSITT (2010)

09 J.Lu+1 "Efficient Mapping of Virtual Networks onto a Shared Substrate" Washington Univ. (2006)

05 myself+0 "Optimizing Virtual Machine Migration for Energy-Efficient Clouds" IEICEJ (2014)

08 myself+0 "A Cloud Visitation Platform for Federated Services at Network Edge" 10th CISSE (2014)

02 myself+0 "Circuit Emulation for Big Data Transfers in Clouds" Book on Networking for Big Data, CRC (2015)


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Cloud Probing is Reversed VNE

• VNE: optimize mapping of many virtual graphs onto one physical topology◦ problem: feasibility low, complexity very high◦ unlikely for cloud providers to implement it in near future

• Cloud Probing: optimize your own population◦ basically a distributed version of client-side VNE◦ no need for support from cloud providers -- can use today!


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Experiment on Amazon (AWS) Cloud


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Experiment on Amazon (AWS) Cloud

• Planetlab (legacy) → Amazon Cloud

• 15 VMs across 8 AWS regions• 5 VMs migrate to random locations every hour

◦ roughly equal distribution is enforced

• each hour: continuous probing in random pairs of VMs◦ rx/tx direction is emulated as HTTP GET or POST


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Experiment : AWS Population

0 1000 2000 3000 4000 5000size (kbytes)

0

1

2

3

4

5tx

,rx th

roug

hput

as y

=log

( 1 +

x in

kbp

s)Intra-DC

0 1000 2000 3000 4000 5000size (kbytes)

0

1

2

3

4

5

tx,rx

thro

ughp

ut a

s y=l

og( 1

+ x

in k

bps) Inter-DC

AverageMin/Max3 sigma band


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Formulations


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Groping by Probing• probing: migrate and see what happens

• groping: no way to know whether migration results in better or worseperformance

• ... in advanced designs, can use history to assign probabilities → markovmodeling

Migrate

IDLE BETTER WORSE

Revert

Migrate

Revert


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The Low-Start Model

Performance

Cost

Stop

New state

• the low-startconcept

• each newimprovement comes athigher cost

• stop or newstate? ... in practicenew state is, bynature, more likely


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Stress Ring Visualization


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Stress Ring (1) Copy AMI

california

ireland

oregon

saopaulo

singapore

sydney

virginia

key (copyami)sizes (1 10)parties (ab) • stress ring: pressure implodes the

balloon

• key: which metric becomes stress

• sizes: kbytes ... small = delay, large= throughput

• parties: AA = intrA-DC, AB =intER-DC

• Copy API is AWS action for moving VMimages across DCs

• ... Brazil is very far from Tokyo


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Stress Ring (2) Intra-DC Delay and Bulk

california

ireland

oregon

saopaulo

singapore

sydney

tokyo

virginia

key (probe)sizes (1 10)parties (aa)

california

ireland

oregon

saopaulo

singapore

sydneytokyo virginia

key (probe)sizes (2000 5000)parties (aa)


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Stress Ring (3) Inter-DC Bulk

california

ireland

oregon

saopaulo

singapore

sydney

tokyo

virginia

key (probe)sizes (2000 5000)parties (ab)

• 2-ring version• outside ring: same as before

• inside ring: the main contributor tostress

• reading: California's throughput is notbad but variance is high and mostlycaused by Oregon


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Stress Optimization


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Stress: Graph vs Ringusing

bigdatabigdatabigdata

guigui

apiapiapi

pmstackspmstacks

vmsvmsvms

vmappsvmappsvmappsvmapps

distappsdistappsdistappsdistapps

scrumscrum

ticketdevticketdev

mongodbmongodb

eclipseeclipse

making researching

optimization

migration

visualization

apps

tools

tractractractractrac

.Cloud Populations.....

.

... are mostly rings, almost nevergraphs

• related topic: graph drawing 10

• rings are easiler to draw and understand

• rings are better for managementdecisions -- which DCs causes themost stress?

• facebook social graph vs google circles

10 T.Kamada+1 "An algorithm for drawing general undirected graphs" Information Processing Letters (1989)


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Stress Optimization• v (will call it key later) -- an arbitrary performance metric

• DCs/regions are a and b, i.e. performance is vab• same-node (intra-DC) vaa (always a) and directional vab ̸= vba• (even ring is a) graph G(N,M) of n nodes and m links

• collect a set of values{vab

}for pairwise a, b ∈ G　

• then stress is an aggregate of probing data:

Sa = f(vaa, vab, vac, ..., vax), where{a, b, c, ...x

}∈ G, (1)

• ... f() is an arbitrary aggregator function (sum, average, etc.).

• stress optimization is then:

minimize∑

Sx, x ∈ G, (2)


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Analysis: Models

1. Pooler Model (1 ring)◦ BigData aggregation◦ 3 VMs, 1 VM collects and stores data from other 2 VMs

2. Syncer Model (2 rings)

◦ 1st ring: same as Pooler Model, only all-to-all throughput◦ 2nd ring: e2e delay between users and 3 VMs -- with time belts, etc.

• ... are trace-based simulations -- AWS experiment is the trace


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Analysis: MigrationPooler model

Syncer model

• Pooler Model is more stable --certain combinations of DCs are better

• Syncer Model -- less stablebecause of 2 rings and daytimefluctuations


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Analysis: Overall

0 100 200 300 400 500Ordered list of values

0

20

40

60

80

100

Com

plet

ion

time

(s)

Do nothingOptimize

Pooler Model

0 1000 2000 3000 4000 5000 6000Ordered list of values

2.25

2.55

2.85

3.15

3.45

3.75

Ave

rage

del

ay (l

og o

f ms)

Syncer Model

• stress optimization results inbetter performance in more than80% of cases


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Implementation


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Implementation: the TopoAPI

API Service Contract (key) Population

TopoAPI Service

Stats

New session ID

ADD( a, b, value) OK

OPTIMIZE( model) Graph, Migrations, …

Read Result Solve

• an independentservice --heroku-based API

• fully abstracta, b, value performancetuple

• sessions are up to client

• generic: stress ring isonly one model, others arepossible


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That’s all, thank you ...


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Date post:	13-Jul-2015
Category:	Technology
Upload:	marat-zhanikeev
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Cloud Probing

Technology