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Distributed Compute for Both
Performance and Cost
Optimization
- Maximizing Data Center
Performance in Financial Services
Andrew Bach, Chief Architect for
Financial Services, Juniper Networks
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Agenda
The Need for High Performance
The Challenge
The Limitations Today
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Agenda
The Need for High Performance
The Challenge
The Limitations Today
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Timeline of the financial industry
10,000 – 6000
BCE
Local Trade
Market
OCE
Trade Routes
Established
1604
First Exchange
Formed
1800’s
Futures
Market
Developed
1800’s
NaIonal
Markets
Developed
1400
First Banks
1602
First Stock
Issued
1700’s
REGIONAL
Exchanges
Developed
Late 1900’s
Global
Exchanges
Developed
10,000 – 6000 BCE
OCE
1400
1500
1600
1700
1800
1900
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Scale of the business
•
The total market cap of all listed companies is over
$55 Trillion
….
…Compared to country GDPs, that would make the
sector the largest economy in the world.
Largest Economies by GDP (in Trillions of $)
Germany -‐ $3.6T
India -‐ $1.9T
Japan -‐ $5.8T
China -‐ $7.5T
US -‐ $15T
$55 Trillion
Market Cap
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Real money
–
DTCC clears, settles over:
§
$24 quadrillion dollars per year
§
24 X 10
15
or $24,000,000,000,000,000
§ One US bank alone transacts over $35 Trillion dollars a day
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400+ Years of Rapid Technology Adoption
And a rich history of technology innovation in markets
•
First stock ticker to disseminate data (1867)
•
First telephones on the trading floors (1878)
•
First electronic ticker display board (1966)
•
Wireless handheld devices on Trading
floors 15 years before iPad invented (1995)
•
Industry’s first private network offering
global connectivity
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Transactions become bandwidth
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Agenda
The Need for High Performance
The Challenge
The Limitations Today
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•
Requirement for long
term retention of data
•
Requirement to archive
meta data
•
Real time risk
management is now
required
•
Requirement for
precision timing
•
Increasing focus on
cyber security
•
Time to market must be
faster
•
Product life time is
shorting
•
Margins are driving OPEX
reduction
•
Regulation is increasing
the cost of business
•
Technology is a
strategic weapon
•
Fueling the race to the
triple crown of
technology (0 cost, 0
latency, 0 time to
market)
•
Bandwidth demand
continues to grow at
30% - 50% per year
•
Rate of data growth is
also increasing
•
Exchange trading floors
continue to fade away
•
Machine assisted
trading dominates
•
Heuristic trading based
on hyper-contextual
information is growing
•
Risk assessment is now
routinely based on
massive near real time
data bases
•
Shared public/private
cloud solutions
Regulatory Model
is Driving Change
FSS is Evolving to
a Commodity
Industry
Technology Adoption
is Accelerating to
Meet Accelerating
Business Needs
Business Model
Continues to Evolve
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Customer
A Typical Challenge – Exchange Gateway Trade
Plant
Total Latency
≈150.0µS
Trade Plant Size:
≈ 100 Servers
≈ 1500 Ports
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A Typical Challenge – Market Data Ticker Plant
Internal
Users
Feed handler &
publishing servers
server farm
Pub/sub
Client subscriber
servers
World Wide
Market Data
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Next Challenge – Heuristic Based Trading
Market data
feeds
News feeds
Social feeds
Streaming
news video
Ticker Plant
Heuristic
Processing
Heuristic
Processing
Heuristic
Processing
Heuristic
Processing
Historical
Data Base
Heuristic
Processing
Human
Oversight
and control
Execution
Venues
Trade Plant
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Agenda
The Need for High Performance
The Challenge
The Limitations Today
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Slowing of Processor Speed
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Latency Reduction Trends
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Agenda
The Need for High Performance
The Challenge
The Limitations Today
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At about 200 – 500NS for
a reasonable switch
Need to focus on a
different approach
Lower latency
Eliminate servers
Reduce network ports
Application can be
embedded in
A VM in the switch
A FPGA in the data
path of the switch
ASIC Packet
forwarding engine
(PFE)
The Race to Zero is
Ending
Embed Application
Snippets into the
Switching Fabric
Embed Snippets at the
Control or Data Plane of
the Network
Compute-Integrated Networking
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What Types of Computing Can We Use
•
CPU
•
Good for general purpose and data flow management
•
FPGA
•
Highly optimizable field programmable hardware executing
custom code and data flow tasks
•
ASIC
•
Highly optimized purpose built processor, a Packet Forwarding
Engine (PFE) for example
•
Merchant silicon
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Network Switch/Application Acceleration/Packet Forwarding Accelerator
PFE
Intel X86 , DDR3, SSD
HYPERVISOR
NETWORK OS
ACTIVE
NETWORK OS
STANDBY
GUEST APPLICATION(S)
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Programing distributed compute systems cost effectively
•
For X86 environment with VM/Containers either HLL or third party
libraries
•
For FPGA
•
Data Flow
•
OpenCL
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Customer
Risk
Management
embedded in
Data center
Switch
Symbol
routing
embedded in
Data center
Switch
Data pre/post
processing
Customer
40% less
ports Latency
down 50µS
Total Latency
≈100.0µS
Trade Plant Size:
≈ 60 Servers
≈ 1000 Ports
Total Latency
≈150.0µS
Trade Plant Size:
≈ 100 Servers
≈ 1500 Ports
A Different Approach: Distributed Computing for
an Exchange Gateway Trade Plant
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A Different Approach: Market Data Ticker Plant
Internal
Users
Feed handler &
publishing servers
server farm
Pub/sub
Client subscriber
servers
World Wide
Market Data
Feeds
Internal
Users
World Wide
Market Data
Feeds
Feed handler &
publishing in top of
rack switch
Pub/sub
server farm
Client subscriber
imbedded in top of
rack switch
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