This document contains scaling High Performance Artificial Intelligence System with the Computational Thinking Approach

1. PRESENTED BY AZHAR K MUSTAPHA
NERVESIS SDN BHD
HOW TOBUILD ANARTIFICIAL INTELLIGENCE
WEB SYSTEM that cansupport more
than 5000 request per second
Platform: http://www.zygy.com Company: http://www.nervesis.com

2. WITHARTIFICIAL INTELLIGENCE
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Zygy gatekeeper
Personal Assistant Platform,
Identity management & Single
Sign on system
midas
Typo-based “page not found”
advertising targeting Telco
subscribers
Zygy smart streaming
A solution for Video on
Demand (VOD) and live web
casting for Video and Audio
(AV).
PROPHECY
Public Conversation
Intelligence

3. PERSONALASSISTAnTPLATFORM
RELATE WHATMATTERS WITHOUTEFFORT
SSO |COLLATE |JOURNALIZE |RELATE
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4. Whatis midas?
Turn URL typos into your
goldmines
Now, “page not found” can be converted into a relevant
advertising page for your business, noticed by millions of
unique viewers.
www.lazadah.com.my/

5. Web
Server
Database
Server
Handle Presentation
Handle AI Logic
Handle Other Logic
Web Server
1-Webserver-1-Database with HIGH
LOAD?
Store Data
Data CRUD (Create, Retrieve
Update, Delete)
DATABASE Server NO RESPONSE
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6. SCALING with COMPUTATIONALTHINKING
* http://www.cs.cmu.edu/~CompThink/
* http://www.bbc.co.uk/education/guides/zp92mp3/revision
Divide & Conquer
Bootstrapping
Generalization /
Similarities
Rules
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7. Decomposition StEPS INTOLAYERS
Web Request
HTML Presentation
AI and Logic Layer
Database Layer
Request
Routed by
Session
Routed By
Capacity
Static Content Dynamic HTML
Boxes (Pagelet)
Probability Calculation | Edit
Distance | Score |Feature
Classification | More …
Cache | Database Read | Write
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8. PATTERNSOR SIMILARITIES
Web Request Can be routed by session
and capacity
Static HTML Characterized by file,
images, videos
Pagelet Pagelets are independent if
content are independent
Probability Calculation &
Edit Distance
Some values are almost
static
Cache Almost static values can be
cached
Database Read Read cannot catch up with
4 million rows although with
index
Certain group of tables are
independent
Database Write Data update is not frequent
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9. ALGORITHMS/ RULES

10. ROUTInGDISTRIBUTION
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11. PAGELETSDISTRIBUTION
$("#shop“).load(“/pageletA“)
$("#shop“).load(“/pageletB“)
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12. AIPOST-TRAINING: PRECALCULATION & ANTICIPATION
CacheINPUTS (xi )
PRECALCULATED PARAMETERS
MATRIX PRUNING (RE)
Naïve Bayes Classifier
Cost function for Deep Neural Network
CACHEOUTPUTS (fi )
PRECALCULATED PROBABILITIES
ANTICIPATED CALCULATION
cMAP = argmax
c∈C
P(x1, x2,…, xn | c)P(c)
Feature-based Linear Classifier
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13. Anticipate calculation for
next iteration
ANTICIPATE
Cache Inputs that are almost
static with cache strategy
based on size.
OUTPUT
Pre calculate certain
probabilities and
parameters,
PRE
CALCULATION
Cache Inputs that are almost static
with cache strategy based on size
INPUT
ANTICIPAT
E
OUTPUTINPUT
PRE
CALCULATIO
N
DISTRIBUTED SMARTCACHING
DATABASE
Persistent layer (part of Logic Layer) will
update data thru cache instead directly to
database
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14. DISTRIBUTED DATABASES
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Database divided into several independent segments
Cache layer will update data only thru Master DB

15. APPLICATIONARCHITECTURE
Distribution by weight
or capacity (CPU,
Memory, Disk IO,
Network IO)
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16. ABSTRACTION /
GENERALIZATION

17. INDEPENDENT DISTRIBUTEDLAYERING
Static Content
Routing
Static Content
Clusters
AI Logic 1
Precalc Cache
Slave DB 1
Pagelet
Routing
Pagelet
Clusters
AI Logic 2
Anticipate
Cache
Master DB 1
Routing By
Session
Static Content
Clusters
AI Logic 3
Input Cache
Master DB 2
Routing By
Capacity
Pagelet
Clusters
AI Logic 4
Output Cache
Slave DB 2
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18. AZHAR K.MUSTAPHA CONTACT
NervesisSdnBhd
Lot 5.50, Wisma Central,Jalan
Ampang 50450 Kuala Lumpur,
Malaysia
www.nervesis.com
www.zygy.com
midas.nervesis.com
enquiry@nervesis.com
www.facebook.com/azhar.k.mustapha
my.linkedin.com/in/azharkm
ADDRESS:
WEBSITE:
EMAIL:
Nervesis Sdn BhdCOMPaNY:

19. THANKSFOR YOURTIMEPlatform: http://www.zygy.com
Company: http://www.nervesis.com