Network Telemetry through TomographyYilong Geng1, Shiyu Liu1, Zi Yin1, Ashish Naik2, Balaji Prabhakar1, Mendel Rosenblum1, and Amin Vahdat2
1 Stanford University 2 Google Inc.
Motivation Network Telemetry through Tomography
• Network tomography
• Network tomography with LASSO
• Network tomography with neural networks
• Network microscopy
• Want to observe and monitor network and
application performance. • Connectivity and reachability
• Regression tests for system changes/updates
• Root-cause analysis of performance degradation
• Security: monitor bad traffic
• Current approaches:• Switches report detailed stats:
• Per-queue counters
• Per-packet measurements
• Expensive, power-dissipated, require bandwidth
to ship sensed data, need same vendor
• Our approach:• Telemetry: Sense at the edge and reconstruct
switch/link utilizations
• More scalable: • No per-queue counters or per-packet measurement
• No extra network traffic due to sensed data
• Software-based, no hardware upgrade of network
Ø For each probe:
Ø Combine all probes:
Ø Solve for queueing delays:
System Pipeline
• Network tomography with packet counts
0.00% 5.00%
10.00% 15.00% 20.00% 25.00% 30.00%
Queuelength
Queuebreakdown
Linkutil
Relativeerrorofrecon
10mscounts 5mscounts2mscounts 1mscountsLASSO+perpkt
Method
Forreconof10msinterval
Computationtime
Storagespace
10mscounts 0.13ms 10.6KB
5mscounts 0.26ms 22.0KB
2mscounts 0.58ms 68.6KB
1mscounts 1.14ms 194.1KB
LASSO+perpkt injection
1.8ms(LASSO)40.7ms(Microscopy)
1720.4KB
Network tomography results (Stanford testbed)
Q = argminQ ||D �AQ||22 + ↵||Q||1
Queue length breakdown
Link utilization breakdown
Algorithm Relativeerror
Pseudo-inverse 44%
Linear regression 35%
LASSO regression 9%
ReLU NN 7.4%
Network tomography results (Google testbed)
Ø Use NN to approximate LASSO
• Goal: reduce amount of data needed for
tomography
• Don’t need to keep timestamps of all packets
• Simply use packet/byte counts
Ø Alerts and Replica
Ø Network tomography
