is it time to go global with cloud performance management?

Mission Statement

1. federated clouds = diversification2. many DCs and/or cloud providers

3. we care mostly about performance4. practical solutions are needed

Marat Zhanikeev -- maratishe@gmail.com Is It Time to Go Global with Cloud Performance Management? -- http://tinyurl.com/marat140328 2/30...

Example: BizStore

BizStore: One DC is Not Enough

• rememberJune 2013?• most services today use vertical intergration -- no diversity

• Hitachi does not share DCs with NEC

• regional diversity of one provider is bad◦ how many Amazon DCs in Japan?

.(the only possible) Solution..

... is to sign contracts with multiple DCs and manage on

client side◦ to be officially presented/released in April 01

01 myself+0 "High Availability Cloud Storage ... Social Graph ... Smart Distribution" NS研 (April 2014)

BizStore: One DC is Not Enough

Kansai

Okinawa Locations

Data Centers

Kyushu

Osaka Office DC1

DC1 DC2 Naha Office

Network distance

storage network

Employee A …. Content / Social Metadata High Availability Data Store DC1 DC2 ….

DC1 DC2 Business trip

Store APIs

Proposed Software

BizStore: Store Diversification

• in software: not a priority list -- optimization engine!• realtime performance monitoring, read/write optimization, etc.

• sub-file data unit -- chunks

SSD Growing network

distance User

HDD DC1 DC2 …

Network

BizStore: Socially Aware Store• content relevance based on

social graph• relevance is a distribution• individual redundancy based on distribution

• other link types: same time, location,filetype, ...

• link strengh != 1Descending

Relevance

Distribution

Redundancy (user setting)

Physical limit of redundancy

End of content

There is a link

When a file is …

Between Created Viewed Edited Deleted

Example: Cloud Streaming

Cloud Streaming: Fixing Problems

Traditionalstreaming

P2Pstreaming

Cloud streaming

Adaptivestreaming

• Congestion(Flash Crowds)

• Unreliable throughput

• Unreliable sources

• Unreliable throughput

• Congestion

Fixed Fixed

Cloud Streaming: Design

VMpopulation

CurrentSources

ServiceProvider

Tracker

ServiceProvider

Parentpeers

P2Pstreaming

Cloudstreaming

ClientClient

02 myself+0 "Multi-Source Stream Aggregation in the Cloud" Wiley Book on ACDN, Chapter 10 (2014)

Practical Solutions for FederatedClouds

A Shortlist of (S)olutions

1. S1: Nextgen traffic processors at DCs

2. S2: QoS Context and Performance Visualization at DCs

3. S3: Performance Modeling for Federated Clouds

4. S4: Client Side Traffic Boostings5. .... definitely not a complete list

Solution (S) 1:Nextgen Traffic Processors at DCs(work in progress)

S1: Multicore Packet Capture

Global Networks

Data Center Internals

Gateway Switch

Capture Manager

CPU CPU CPU

… Storage

Mirror

• multicore is the key

• multicore !=traditional parallel processing03

• on-demand capture, DPI,heterogeneous tasks 04

03 myself+0 "...Multicore Capture in Data Center Forensics" ACM AISACCS-SFCS (June 2014)

04 myself+0 "A Lock-Free Shared Memory Design for ... Multicore Packet Traffic Capture" IJNM (in print)

S1: Multicore Hates Memory Locks• lockfree design 04 : no messages, no memory locks

PF_RING

TimeManager

Shared Memory

Capture

Core 1

Core 2

Core 3

Core X Manager

PF_ RING

Shared memory

One thread

Create

Lifespan Stale check

Process/wrap Wrap wait

Assign

04 myself+0 "A Lock-Free Shared Memory Design for ... Multicore Packet Traffic Capture" IJNM (in print)

Solution (S) 2: DC Performance APIs

S2: E2E QoS, M2M Patterns

Meter Merger

Per flow statistics

Analyzer

History,state

Profiler

Clients

Probe Analysis machine

Web application

• clean slate: captureQoS context 05

• visualize usercommunities

• export via APIs to usersand/or service providers

05 myself+0 "A holistic community-based architecture for measuring E2E QoS at data centres" IJCSE (in print)

S2: Has to be a Clean Slate

ProbeRouter

Data center infrastructure

source IP

timestamp Key Key DLL0 #01 #02 #03 1 #042 #05 #06….….2^24 #07

source portdest IP

dest port

protocolpacket size

Packet Hash table

DLL#05#04

#02#03#07

Exportover UDP

Byte048

12162024…

0 (bits) 32

Source port Dest port

Source IPDestination IP

* psize pspace

Start time (s)Start time (us)

* psize pspace1 11

Data unit

psize:Packet size

pspace:Packet space

Exportvia

a file

UDP RX

Buffer (5s)

Byte048

12162024…

0 (bits) 32

Source port Dest port

Source IPDestination IP

D psize pspace

Start time (s)Start time (us)

D psize pspace1 11

Data unit

D:Direction(0 or 1)

Merger

Find flow fromopposite direction

Analyzer

History

Read and update

Ring buffer of data units per IP on internal networks

Statistic MeaningMinOWD Global minimum OWDMaxBatch Max byte count of a

packet burstBulks Throughputs in flows

Per source-dest pair

• has to be a clean slate!• cisco, ntop, sflow are notfeasible

• QoS context is somethingnew

• (figure is vector, so, zoom in!)

S2: But Payoff is Great!

0 6400 12800 19200Batch size (bytes)

Solution (S) 3: Cloud Weather System(work in progress)

S3: Cloud Weather System

(high/low)Pressure

Typhoon

Drought

Goodweather

Badweather

• continents: user, services 07

• water: network• weather, clouds, etc.: changes inperformance

• droughts: insufficiency ofinfrastructure, users do not get enoughcapacity

• typhoons: basically, Flash Crowds inservices, going viral, ...

• forecasting: possible with enoughperformance monitoring, similar to stockmarket

07 myself+0 "Cloud Weather System as a Futuristic Performance Model" IEICE総合大会 (March 2013)

Solution (S) 4: Mobile ThroughputBoosters

S4: Mobile Throughput Booster

• so far, only possible in wireless -- WiFi Direct

Single Connection Multipath

Singular Connectivity

Traditional Applications

Traditional Multipath

Multiple Connectivity

No known cases (wasted potential)

Group Communication 3G/LTE/* + WiFi Direct THIS PROPOSAL

S4: Group Resource Pooling

Remote connectivity Local Connectivity

Content Provider

Main Client

Delegated Client

3G/LTE/* Access 3G/LTE/*

Access

S4: Converged Wireless Campus

Student

Develop, make secure

APP + CODE

Campus

Another Student

APP + CODE

2 2 Distribute

3 Meet and delegate

API Tokens

Distribute

Pass at delegation

University 4

Solution (S) 5: Over-the-NetworkIndexing

S5: Indexing in Clouds

Indexer

Network

Traditional Client

Indexer

Index Read, Write

Stringex Client

S5: Over-the-Network Optimization• in short: throughput-centric network storage optimization 08

Stringex

Stringex Client

Sync Engine

Optimization

Local Cache

Check 1 2

08 myself+0 "A New Practical Design for Browsable Over-the-Network Indexing" ISEEE (April 2014)

S5: Performance

3.15 3.85 4.55 5.25 5.95 6.65Index Size (log)

Lucene

Stringex

That’s all, thank you ...

[01] myself+0 (April 2014)High Availability Cloud Storage ... Social Graph ... Smart DistributionNS研

[02] myself+0 (2014)Multi-Source Stream Aggregation in the CloudWiley Book on ACDN, Chapter 10

[03] myself+0 (June 2014)...Multicore Capture in Data Center ForensicsACM AISACCS-SFCS

[04] myself+0 (in print)A Lock-Free Shared Memory Design for ... Multicore Packet Traffic CaptureIJNM

[05] myself+0 (in print)A holistic community-based architecture for measuring E2E QoS at data centresIJCSE

[06] myself+0 (May 2014)Marat Zhanikeev -- maratishe@gmail.com Is It Time to Go Global with Cloud Performance Management? -- http://tinyurl.com/marat140328 30/30

Towards a Practical Method for Interactive Traffic Visualizations in Data CentersSC研

[07] myself+0 (March 2013)Cloud Weather System as a Futuristic Performance ModelIEICE総合大会

[08] myself+0 (April 2014)A New Practical Design for Browsable Over-the-Network IndexingISEEE

is it time to go global with cloud performance management?

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