Resource: LCU13 Name: High Performance Networking - Deep dive Date: 29-10-2013 Speaker: Jarmo Hillo / Raj Murali Srinivasan Video: http://www.youtube.com/watch?v=QTtKOoIteaY

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High end telecom networking – deep dive
LCU 2013

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1000x Packet Traffic Technologies are Maturing
… times more capacity
3D silicon
process for
maximum
efficiency in
minimum
space
Optimal
architectural
split for variety
of payload
Ultrafast
interfaces
reaching
100Gbit/s

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Single core networking
Only priority queuing doable with
single core
• Static priorities
• Highest priority first with
starving prevention logic
• Packet order not an issue
• Does not scale
Classify
100%

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Multicore SoC Load Sharing
Multicore SoC great for load
sharing
• Static resource allocation
• Queue selection typically by hash of incoming
packet header or round robin
• HW aware applications
• Unpredictable, uneven core load
Hash
95% 5%60%>100%

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Multicore SoC Load Balancing
Multicore SoC great for load
balancing
• Dynamic resource allocation with rules
• Fast per packet decision
• Event Machine model: each thread asks for
new job after current is finished
• Automatic scaling – “single thread”
programming model
Scheduler
65% 65%65%65%

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Hickups with multicore scaling
Relative speedup does not
scale linearly with cores, for
example 64 cores, 1% serial
results to less than 40x speedup

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• Mobile traffic for large amount of cells
• Find max throughput - QoS threshold for adding more users
Case study: WCDMA HSPA
Static core allocation
• Users allocated per core based on resource
availability to maintain enough peak reserves
• High headroom
required
• Low average load
Dynamic core allocation
• Allocation of users to a resource pool of cores
• Statistical multiplexing gain
• Low headroom
• High average load
Core1 Core2 Core3 Core4
AverageloadPeakreserve
Core5
~50%
Core1 Core2 Core3 Core4 Core5
~90%
80% gain

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Case study: Linux userspace PMD vs. socket
packet test
HW load balancer
further improves
scaling with number
of cores
Dataplane in
userspace scales
linearly
Kernel approach
saturates at ten
cores

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HW abstraction of networking SoC
Logical functionality and packet
flow in typical networking SoC
Null latency intelligent packet
scheduling to the cores

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SW abstraction of networking SoC
Open Event Machine SW architecture

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Abstraction
Scalability
Efficiency
What do we need
1 x

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No need to (re)program as
SoC or core resources
increase. “Single core”
programming model
Maintain same
architectural split across
platforms
Agnostic to programming
model
Application portability
Common terminology
Abstraction is the key for portability
Resources
API
Functional
SoC
core core core…
Applications

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Optimal balance of cores,
accelerators and
interfaces
Eliminates single core
bottleneck
Allows dynamic load
balancing
Minimize the serialization
Ready for ultrafast
interface technologies
No packet drop before
classification
Scalability for optimal capacity
HW offload
Interfaces
Optimal number of
resources

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User space direct HW
access
No context switching
Idle time power saving
Moore’s Law, FinFet
Dedicated processing
units
Efficiency for maximum density
Power efficiency
Compute
efficiency
22nm 7nm

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Open Data Plane
OpenDataPlane™
ODP will be the de-facto data plane programming model
Common API for networking SoC
Abstraction with performance
Application portability across
platforms, time to market
Scaling to any number of cores
and accelerators
Efficiency in mind

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How does ODP map to Openstack
ODP
ODP

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How does ODP map to NFV
ODP ODP ODP

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How does ODP map to Open Daylight
ODP
ODPODP ODP
ODP

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Resource and
functional
abstraction
Scalablility
for variety of
payload
Compute
efficiency
Summary
… get ready for 1000x packet compute

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1GB of personalized
data per user per day
means 1000x capacity
compared today.
Questions?