1. Multi-Core Parallel Routing
05/01/ NetSciCom
Multi-Core Parallel Routing
Multi-Core Parallel Routing -
Ahmet Soran*, Mehmet Hadi Gunes*, Murat Yuksel⁺
*University of Nevada Reno
+University of Central Florida

2. Shortest-Path Routing
Internet routing: shortest-path
Greedy (shortest path)
Single path
Advantage: easy to compute (Dijsktra in OSPF)
05/01/ NetSciCom
Multi-Core Parallel Routing -
Maximum Multi-Commodity Flow Problem:
Multiple commodities between different node pairs
Pass the maximum flow through a network
No restriction on the path characteristics
No restriction on the flow rates
Drawback: NP-hard
5 units
2 units
How to maximize throughput for more than one pair?
Leverage Multi-Core CPUs for Parallelism?

3. Multi-Path Routing - Focus
How to maximize sent data between pairs?
Multipath routing (heuristics)
Requires non-shortest path
Mostly done via prior centralized calculations
Requires significant updates to the routers
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Leverage Multi-Core CPUs
2 units (s-o-q-t)
Multi-Core Parallel Routing -
1 units
(s-o-q-r-t)
MaxFlow – Multi Commodity Flow Problem:
Pass the maximum flow through a network
No restriction on the path characteristics
No restriction on the flow rates
Drawback: NP-hard
2 units (s-p-r-t)
5 units

4. Motivation Multi-core routers are available …
Routers already know how to calculate shortest-path
How can we use them for improving routing protocols?
05/01/ NetSciCom
Split topology into multiple substrates
Assign each substrate to a different core
Calculate shortest-paths in each substrate
Multi-Core Parallel Routing -
Parallelization
End-to-end data flow optimization
Comprehensive Solution
Fast solutions
Adaptive solutions
Improvable solutions

5. How to generate a new substrate efficiently?
05/01/ NetSciCom
How to generate a new substrate efficiently?
Multi-Core Parallel Routing -
Max Flow = N power 3
Multi commodity problem -> np hard

6. Substrates – Core Solution
Total number of substrate sets can be produced : 2 𝐸 𝑆
How to produce new substrate?
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Multi-Core Parallel Routing -

7. Edge Based Removal Most Used Edge First 05/01/2017 - NetSciCom
Multi-Core Parallel Routing -

8. How to Generate Substrates
Graph-Based: Which Node to Remove?
Heuristic Step: Remove the node most central to the network
Betweenness Centrality
# of SPs that pass through
Flow-Based: Which Edge to Remove?
Heuristic Step: Remove the most utilized edge
Can capture dynamism in traffic and network
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9. Substrates: Heuristics
Maximum aggregate throughput
Minimum process time
Goals
Keep a Substrate 0
Iteratively remove parts of S0
Sanity
Node removing
Edge removing
Approaches
the most diverse
non-overlapping shortest paths
Intuition
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Multi-Core Parallel Routing -

10. Substrates: Which Elements to Remove?
Network Centrality Metrics
Betweenness Centrality
# of SPs that pass through
Closeness Centrality
∑ length of SP from the node
Degree Centrality
Node degree
Edge Betweenness Centrality
Page Rank Centrality
EigenVector Centrality
05/01/ NetSciCom
Calculate Scores
Choose elements from ordered list
Remove elements
Create the substrate
Multi-Core Parallel Routing -
Heuristic Step: Remove the node most central to the network.
Independent – subgraphs are generated from the entire actual topology
Cumulative – subgraphs are generated from the previous one sequentially. Subgraphs get smaller with each generation

11. Topologies - Rocketfuel
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1221 (Telstra)
1239 (SprintLink)
1755 (Ebone)
3257 (Tiscali)
Multi-Core Parallel Routing -
Telstra
Sprintlink
Ebone
Tiscali
Exodus
AboveNet
3967 (Exodus)
6461 (AboveNet)

12. Simulation Flow Set Capacity sharing Highest Flow Random All pair
𝐹𝑙𝑜𝑤 𝐷𝑒𝑚𝑎𝑛𝑑= 𝑝𝑜𝑝1 ∗𝑝𝑜𝑝2 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒 2
Capacity sharing
Max-min allocation
Proportionally with flow demand
Highest Flow
Potential maximum flow
Random
Lower bound
05/01/ NetSciCom
Multi-Core Parallel Routing -
It is not a packet simulator, static analysis ..
HF removes the most utilized edges in each step
HF needs to be recalculated
HF is dynamic but computationally intensive
Flow demand is product of populations divided by the square of the geo-distance between two routers

13. Centrality Analysis Multi-Core Parallel Routing -
05/01/ NetSciCom
Multi-Core Parallel Routing -
In the figure, x-axis has two different indicators. First line on the top shows the removal percentage of network nodes/links (which is incremented between 2% and 16% by 2). Second line gives the number of cores (i.e., 2, 4, 8, 16, 32 cores) that will generate subgraphs.
We observe that MCPR heuristics improve with the number of cores
node centrality heuristics perform slightly worse than random network generation, centrality heuristics overall yield better results than random removals
edge centrality heuristics are better with high number of cores and independent removal percentage within 8% to 10%.

14. Centrality Analysis Multi-Core Parallel Routing -
05/01/ NetSciCom
Multi-Core Parallel Routing -
the independent method removes fewer elements in generating the next subgraph.
we observe that the cumulative method performs worse with higher node/edge removal rates
they are not able to yield viable e2e paths
HF removes hot spot(s) from the subsequent subgraphs

15. Number of Core Analysis – Independent (%8 Removal)
05/01/ NetSciCom
Multi-Core Parallel Routing -
Figure 4 presents the effect of the number of cores in the networks with %8 independent edge removal. As observed in both the Telstra and overall average results, the speedup performance improves with the number of cores

16. Substrate Changes Multi-Core Parallel Routing - 05/01/2017 - NetSciCom
node centrality heuristics disrupt the network in subgraphs more than edge centrality and highest flow heuristics
We observe that small removal percentages have low effects on subgraph characteristics

17. Substrate Changes Multi-Core Parallel Routing - 05/01/2017 - NetSciCom
Cumulative subgraph generation significantly changes graph characteristic and removes connectivity in the graph

18. Substrate Changes Multi-Core Parallel Routing - 05/01/2017 - NetSciCom
Average node degree, however, is not effected much by the edge removal heuristics.
In node centrality heuristics, first subgraph removes the most centralized nodes, which causes significant decrease in the average and maximum node degree. After a point, MCPR start to remove periphery nodes from the network and this increases the average node degree.

19. Substrate Changes Multi-Core Parallel Routing - 05/01/2017 - NetSciCom
Note that the removal percentage should be less than 100/coreNumber as after this point, there are no more nodes to remove in the subgraphs

20. Summary A novel divide-and-conquer approach:
Divide: split topology into multiple virtual substrates
Conquer: use shortest-path on each substrate
The overall problem is transformed/mitigated into the divide phase
Significant improvements via using multi-cores
Up to 4 times speed up in aggregate throughput
Particularly applicable to data center networking
05/01/ NetSciCom
Multi-Core Parallel Routing -