-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathindex.html
More file actions
659 lines (619 loc) · 28.7 KB
/
Copy pathindex.html
File metadata and controls
659 lines (619 loc) · 28.7 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>Ptah: Orchestrating Secure Edge-AI & Post-Quantum Crypto</title>
<!-- Google Analytics Tag -->
<script async src="https://www.googletagmanager.com/gtag/js?id=G-7ZZFF2X8YR"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'G-7ZZFF2X8YR');
</script>
<style>
body { font-family: Arial, sans-serif; margin: 20px; background: #f5f5f5; }
.header { text-align: center; padding: 30px; background: #00274c; color: #fff; border-radius: 8px; }
.header h1 { margin: 0 0 10px; font-size: 2.5em; }
.header p { margin: 5px 0; }
.logos img { height: 60px; margin: 0 15px; }
.badge { display: inline-block; padding: 5px 12px; border-radius: 12px; background: #ff6f00; color: #fff; margin: 0 5px; }
.section { margin: 30px 0; padding: 20px; background: #fff; border-radius: 8px; box-shadow: 0 2px 5px rgba(0,0,0,0.1); }
.section h2 { color: #00274c; margin-bottom: 10px; }
ul { margin-left: 1.2em; }
.button { display: inline-block; background: #0055a4; color: #fff; padding: 10px 20px; text-decoration: none; border-radius: 5px; }
.button:hover { background: #004080; }
</style>
</head>
<body>
<div class="header">
<h1>Ptah: Secure Edge-AI & Post-Quantum Crypto in Space Systems</h1>
<p>Dr. Mohamed El-Hadedy • RSCL @ Cal Poly Pomona</p>
<p><a href="mailto:mealy@cpp.edu" style="color:#fff;">mealy@cpp.edu</a> | 909-869-2594</p>
<div class="logos">
<img src="assets/RSCL_logo.png" alt="RSCL Logo">
<img src="assets/Ptah_eye_logo.png" alt="Ptah Logo">
</div>
<p>
<span class="badge">NASA MINDS 2019</span>
<span class="badge">NASA MINDS 2020</span>
<span class="badge">NASA MINDS 2021</span>
<span class="badge">NASA MINDS 2022</span>
<span class="badge">NASA MINDS 2023</span>
<span class="badge">NASA MINDS 2024</span>
<span class="badge">NASA MINDS 2025</span>
</p>
</div>
<div class="section">
<h2>Project Description</h2>
<p>
The <strong>Ptah</strong> project is an innovative curriculum and reference implementation designed for the emerging field of
secure edge-AI in space and terrestrial applications. Combining hardware diversity—RISC-V accelerators, Raspberry Pi clusters,
and NVIDIA edge GPUs—with state-of-the-art cryptography (post-quantum lattice schemes and lightweight AEAD), Ptah
demonstrates how to architect resilient, future-proof systems under stringent power, weight, and environmental constraints.
Participants will learn to deploy containerized microservices across heterogeneous clusters, orchestrate workloads with
K3s, instrument telemetry pipelines with PQC signatures, and perform real-time monitoring using Prometheus and Grafana.
Over a 15-week course, students engage in hands-on labs, benchmarking, and system integration, culminating in a comprehensive
final quiz covering cryptography, orchestration, hardware design, and performance evaluation.
</p>
</div>
<div class="section">
<h2>🔐 Post-Quantum Cryptography (PQC)</h2>
<p>
Imagine a future where quantum computers render today’s encryption obsolete in minutes. To safeguard critical spacecraft and edge-computing nodes against that threat, we turn to <strong>Post-Quantum Cryptography (PQC)</strong>. Algorithms like
<strong>CRYSTALS-Dilithium</strong> and <strong>CRYSTALS-Kyber</strong> are built on mathematically rigorous lattice problems—challenges so complex that even a million-qubit quantum computer would take centuries to solve them.
</p>
<p>
In the harsh environment of space, where remote satellites and deep-space probes cannot be patched on the fly, PQC ensures that firmware updates remain authentic and unforgeable for decades. On terrestrial edge systems—drones, unmanned rovers, and IoT sensors—“harvest-now, decrypt-later” attacks become futile because every telemetry packet, command stream, and key exchange is secured against future quantum decryption.
</p>
<p>
<strong>Why Lattices?</strong>
Lattice-based schemes provide compact keys and fast operations without sacrificing security.
- <em>Dilithium</em> delivers robust digital signatures, so every software bundle, sensor reading, or inter-device handshake bears an unbreakable quantum-resistant stamp.
- <em>Kyber</em> enables ultra-secure key-exchange, allowing ground stations to establish shared secrets with spacecraft or edge nodes in a way that remains confidential even under quantum attack.
</p>
<p>
By integrating PQC into our Ptah framework, we not only future-proof critical systems but do so with performance tuned for power-and-weight-constrained platforms. The result is a security foundation that remains unshakable in the quantum era—because in space, tomorrow’s threats demand today’s unbreakable cryptography.
</p>
</div>
<div class="section">
<h2>🔒 Lightweight Cryptography</h2>
<p>
While traditional ciphers like AES excel in data centers, they’re too heavy for tiny, battery-powered edge nodes.
<strong>Lightweight cryptography</strong> fills that gap by delivering strong security with minimal footprint—CPU cycles, RAM, and power.
</p>
<!-- Comparison: AEAD vs Block Ciphers -->
<h3>AEAD vs. Block Cipher Comparison</h3>
<table>
<thead>
<tr>
<th style="text-align:left; padding:8px;">Feature</th>
<th style="text-align:center; padding:8px;">AEAD (e.g., Ascon)</th>
<th style="text-align:center; padding:8px;">Block Cipher + MAC (e.g., AES-GCM)</th>
</tr>
</thead>
<tbody>
<tr>
<td style="padding:8px;">Encryption + Authentication</td>
<td style="text-align:center; padding:8px;">Single pass (atomic)</td>
<td style="text-align:center; padding:8px;">Two steps (encrypt, then tag)</td>
</tr>
<tr>
<td style="padding:8px;">Code Size</td>
<td style="text-align:center; padding:8px;">≈ 2 – 5 kB</td>
<td style="text-align:center; padding:8px;">≈ 10 – 20 kB</td>
</tr>
<tr>
<td style="padding:8px;">RAM Usage</td>
<td style="text-align:center; padding:8px;">≈ 200 – 500 bytes</td>
<td style="text-align:center; padding:8px;">≈ 1 – 2 kB</td>
</tr>
<tr>
<td style="padding:8px;">Throughput (cycles/byte)</td>
<td style="text-align:center; padding:8px;">2 – 5</td>
<td style="text-align:center; padding:8px;">10 – 15</td>
</tr>
<tr>
<td style="padding:8px;">Security Goal</td>
<td style="text-align:center; padding:8px;">Confidentiality & Authenticity</td>
<td style="text-align:center; padding:8px;">Confidentiality & Authenticity</td>
</tr>
</tbody>
</table>
<!-- ASCON Deep Dive -->
<h3>ASCON Internals</h3>
<img src="assets/Ascon_sponge_diagram.png" alt="Ascon Sponge Diagram" style="display:block;margin:10px auto;max-width:400px;">
<table>
<thead>
<tr>
<th style="text-align:left; padding:8px;">Property</th>
<th style="text-align:center; padding:8px;">Value</th>
</tr>
</thead>
<tbody>
<tr>
<td style="padding:8px;">Permutation Size</td>
<td style="text-align:center; padding:8px;">320 bits (5 × 64-bit lanes)</td>
</tr>
<tr>
<td style="padding:8px;">Rate</td>
<td style="text-align:center; padding:8px;">64 bits / 8 bytes per absorption/squeeze</td>
</tr>
<tr>
<td style="padding:8px;">Initialization Rounds</td>
<td style="text-align:center; padding:8px;">12</td>
</tr>
<tr>
<td style="padding:8px;">Intermediate Rounds</td>
<td style="text-align:center; padding:8px;">6</td>
</tr>
<tr>
<td style="padding:8px;">Finalization Rounds</td>
<td style="text-align:center; padding:8px;">12</td>
</tr>
<tr>
<td style="padding:8px;">Key Size</td>
<td style="text-align:center; padding:8px;">128 bits (optional 256 bits)</td>
</tr>
<tr>
<td style="padding:8px;">Nonce Size</td>
<td style="text-align:center; padding:8px;">128 bits</td>
</tr>
<tr>
<td style="padding:8px;">Tag Size</td>
<td style="text-align:center; padding:8px;">128 bits</td>
</tr>
<tr>
<td style="padding:8px;">Performance (Cortex-M4)</td>
<td style="text-align:center; padding:8px;">≈ 1 MB/s</td>
</tr>
</tbody>
</table>
<p>
ASCON’s design is built around a <em>sponge construction</em>, where data and keys are absorbed into an internal state
that is repeatedly permuted. This single-pass approach (absorb-permute-squeeze) gives both encryption and
authentication in one go, cutting code size and RAM needs by up to 50% compared with AES-GCM on the same hardware.
</p>
<!-- Security Comparison -->
<h3>Security Strength vs. Block Ciphers</h3>
<table>
<thead>
<tr>
<th style="text-align:left; padding:8px;">Security Aspect</th>
<th style="text-align:center; padding:8px;">ASCON (128-bit key)</th>
<th style="text-align:center; padding:8px;">AES-128 (GCM)</th>
</tr>
</thead>
<tbody>
<tr>
<td style="padding:8px;">Bit-security</td>
<td style="text-align:center; padding:8px;">≥ 128 bits</td>
<td style="text-align:center; padding:8px;">128 bits</td>
</tr>
<tr>
<td style="padding:8px;">Integrity Bound</td>
<td style="text-align:center; padding:8px;">2⁶⁴ forgery bound</td>
<td style="text-align:center; padding:8px;">2⁶⁴ forgery bound</td>
</tr>
<tr>
<td style="padding:8px;">Side-Channel Resistance</td>
<td style="text-align:center; padding:8px;">Simple permutation – easier to mask</td>
<td style="text-align:center; padding:8px;">Complex S-boxes – harder to mask</td>
</tr>
</tbody>
</table>
<p>
By choosing ASCON for Ptah’s edge modules, we ensure each micro-controller—or even a small FPGA slice—can
authenticate and encrypt telemetry with minimal overhead, leaving headroom for sensor processing and control loops.
</p>
</div>
<div class="section">
<h2>⚙️ Orchestration Frameworks</h2>
<p>
Managing a distributed Edge-AI/PQC cluster requires a lightweight yet powerful orchestrator. Below we compare three leading container orchestration platforms on footprint, feature set, and resource utilization—then dive deeper into how GPU scheduling and CPU allocation work in K3s for drones and UGVs.
</p>
<!-- Topology Diagram Placeholder -->
<p style="text-align: center;">
<img src="assets/orchestration_topology.png" alt="Cluster Topology Diagram" style="max-width:600px; width:100%;">
</p>
<!-- Feature & Footprint Comparison -->
<h3>Feature & Footprint Comparison</h3>
<table>
<thead>
<tr>
<th style="text-align:left; padding:8px;">Framework</th>
<th style="text-align:center; padding:8px;">Binary Size</th>
<th style="text-align:center; padding:8px;">Memory Overhead<sup>1</sup></th>
<th style="text-align:center; padding:8px;">Supported APIs</th>
<th style="text-align:center; padding:8px;">Ideal Use Case</th>
</tr>
</thead>
<tbody>
<tr>
<td style="padding:8px;"><strong>Docker Swarm</strong></td>
<td style="text-align:center; padding:8px;">~200 MB</td>
<td style="text-align:center; padding:8px;">~150 MB</td>
<td style="text-align:center; padding:8px;">Core Swarm, Stacks</td>
<td style="text-align:center; padding:8px;">Simple clusters & rapid prototyping</td>
</tr>
<tr>
<td style="padding:8px;"><strong>K3s</strong></td>
<td style="text-align:center; padding:8px;">~50 MB</td>
<td style="text-align:center; padding:8px;">~70 MB</td>
<td style="text-align:center; padding:8px;">Kubernetes v1.x (core)</td>
<td style="text-align:center; padding:8px;">Edge/IoT & power-constrained nodes</td>
</tr>
<tr>
<td style="padding:8px;"><strong>Kubernetes</strong></td>
<td style="text-align:center; padding:8px;">~1 GB+</td>
<td style="text-align:center; padding:8px;">~1 GB+</td>
<td style="text-align:center; padding:8px;">Full k8s API</td>
<td style="text-align:center; padding:8px;">Enterprise datacenters</td>
</tr>
</tbody>
</table>
<p><sup>1</sup> Memory measured as RSS of control-plane components on a baseline Pi 4.</p>
<!-- CPU & GPU Scheduling -->
<h3>CPU & GPU Resource Allocation</h3>
<p>
In K3s, you can label nodes with <code>cpu</code> and <code>gpu</code> capacity, then request them in your Pod specs. Below is an example of how a PQC service and an AI inference service would request resources:
</p>
<pre><code># PQC signature service (runs on any CPU node)
resources:
requests:
cpu: "0.5"
memory: "256Mi"
limits:
cpu: "1"
memory: "512Mi"
# AI inference service (runs on GPU-enabled node)
resources:
limits:
nvidia.com/gpu: 1
memory: "1Gi"
</code></pre>
<!-- Performance Estimates -->
<h3>Performance Estimates</h3>
<table>
<thead>
<tr>
<th style="text-align:left; padding:8px;">Node Type</th>
<th style="text-align:center; padding:8px;">CPU Cores</th>
<th style="text-align:center; padding:8px;">Clock (GHz)</th>
<th style="text-align:center; padding:8px;">GPU Cores</th>
<th style="text-align:center; padding:8px;">Approx. Throughput</th>
</tr>
</thead>
<tbody>
<tr>
<td style="padding:8px;">Raspberry Pi CM4</td>
<td style="text-align:center; padding:8px;">4</td>
<td style="text-align:center; padding:8px;">1.50</td>
<td style="text-align:center; padding:8px;">–</td>
<td style="padding:8px;">~200 Dilithium ops/sec</td>
</tr>
<tr>
<td style="padding:8px;">TRK1 (Rockchip RK3588)</td>
<td style="text-align:center; padding:8px;">8</td>
<td style="text-align:center; padding:8px;">2.40</td>
<td style="text-align:center; padding:8px;">–</td>
<td style="padding:8px;">~1 200 Dilithium ops/sec</td>
</tr>
<tr>
<td style="padding:8px;">Jetson Nano</td>
<td style="text-align:center; padding:8px;">4</td>
<td style="text-align:center; padding:8px;">1.43</td>
<td style="text-align:center; padding:8px;">128 (Maxwell)</td>
<td style="padding:8px;">
• GPU: ~500 ASCON ops/sec<br>
• CPU: ~400 Dilithium ops/sec
</td>
</tr>
<tr>
<td style="padding:8px;">Jetson Orin NX</td>
<td style="text-align:center; padding:8px;">6</td>
<td style="text-align:center; padding:8px;">2.20</td>
<td style="text-align:center; padding:8px;">1024 (Ampere)</td>
<td style="padding:8px;">
• GPU: ~5 000 ASCON ops/sec<br>
• CPU: ~800 Dilithium ops/sec
</td>
</tr>
</tbody>
</table>
<!-- When to use which -->
<h3>Which to Choose?</h3>
<ul>
<li>
<strong>Drone Swarms (ClusterHat):</strong> K3s on Pi Zero W can run ultra-light pods (<code>cpu: "0.1"</code>) with Ascon AEAD for telemetry, preserving battery life.
</li>
<li>
<strong>UGV / Rover Platforms (TuringPi + Orin NX):</strong> K3s with GPU scheduling enables offloading neural nets to Orin NX while still hosting PQC services on CM4 nodes.
</li>
<li>
<strong>Hybrid Deployments:</strong> Leverage <code>nodeSelector</code> and <code>affinity</code> to ensure heavy workloads land on TRK1/Orin, and lightweight tasks run on Pi-class nodes.
</li>
</ul>
<p>
By using K3s with fine-grained resource requests and node labels, you can orchestrate a heterogeneous cluster that maximizes both performance and power-efficiency—crucial attributes for computer architects designing next-generation edge-AI & space systems.
</p>
</div>
<div class="section">
<h2>Hardware Architectures</h2>
<div class="logos">
<img src="assets/TuringPi_logo.png" alt="TuringPi">
<img src="assets/TRK1_logo.png" alt="TRK1">
<img src="assets/CM4_logo.png" alt="Raspberry Pi CM4">
<img src="assets/JetsonNano_logo.png" alt="Jetson Nano">
<img src="assets/OrinNX_logo.png" alt="Jetson Orin NX">
<img src="assets/ClusterHat_logo.png" alt="ClusterHat 2.5">
</div>
<ul>
<li><strong>TuringPi</strong> cluster of CM4/Jetson modules with built-in switch.</li>
<li><strong>TRK1</strong> (Rockchip RK3588 RISC-V) NPU-accelerated compute.</li>
<li><strong>Pi CM4</strong> nodes for general compute.</li>
<li><strong>Jetson Nano/Orin NX</strong> for GPU-accelerated AI inference.</li>
<li><strong>ClusterHat 2.5</strong> to simulate drone swarms on Pi Zero W nodes.</li>
</ul>
</div>
<div class="section">
<h2>🛰️ Telemetry & GPS Integration</h2>
<p>
Robust, low-latency telemetry and precise positioning are critical for autonomous drones, rovers, and space systems. In Ptah, each node—whether a Pi Zero W, CM4, TRK1, or Orin NX—connects to a GNSS receiver (GPS+GLONASS+Beidou) via UART or USB. A dedicated <em>telemetry pod</em> under K3s executes this pipeline:
</p>
<ol>
<li><strong>Acquisition:</strong> Multi-constellation fixes at 1–10 Hz (HDOP ≤ 3).</li>
<li><strong>Parsing & Filtering:</strong> Normalize NMEA sentences, drop low-accuracy fixes, correct drift.</li>
<li><strong>Cryptographic Protection:</strong>
<ul>
<li>Dilithium signature (~2 ms on CM4, < 0.5 ms on Orin NX)</li>
<li>Kyber KEM encapsulation (~1 ms on CM4)</li>
</ul>
</li>
<li><strong>Publication & QoS:</strong>
<ul>
<li>MQTT (QoS 2) for exactly-once delivery</li>
<li>HTTP/2 + TLS-PQC for sub-10 ms end-to-end latency</li>
</ul>
</li>
<li><strong>Self-Healing:</strong> K3s probes restart any failed pod within seconds.</li>
</ol>
<h3>Performance & Accuracy Metrics</h3>
<table>
<thead>
<tr>
<th style="text-align:left; padding:8px;">Metric</th>
<th style="text-align:center; padding:8px;">Pi Zero W</th>
<th style="text-align:center; padding:8px;">Compute Module 4</th>
<th style="text-align:center; padding:8px;">TRK1 / Orin NX</th>
</tr>
</thead>
<tbody>
<tr>
<td style="padding:8px;">GNSS Fix Rate (Hz)</td>
<td style="text-align:center; padding:8px;">1</td>
<td style="text-align:center; padding:8px;">5</td>
<td style="text-align:center; padding:8px;">10</td>
</tr>
<tr>
<td style="padding:8px;">Dilithium Sign Latency (ms)</td>
<td style="text-align:center; padding:8px;">8.0</td>
<td style="text-align:center; padding:8px;">2.1</td>
<td style="text-align:center; padding:8px;"><0.5</td>
</tr>
<tr>
<td style="padding:8px;">Kyber KEM Latency (ms)</td>
<td style="text-align:center; padding:8px;">6.3</td>
<td style="text-align:center; padding:8px;">1.2</td>
<td style="text-align:center; padding:8px;">0.3</td>
</tr>
<tr>
<td style="padding:8px;">End-to-End Delay (ms)</td>
<td style="text-align:center; padding:8px;">20.5</td>
<td style="text-align:center; padding:8px;">8.4</td>
<td style="text-align:center; padding:8px;">3.2</td>
</tr>
</tbody>
</table>
<h3>Deployment Profiles</h3>
<ul>
<li><strong>Drone Swarms (ClusterHAT):</strong> Pi Zero pods at < 10 % CPU, < 50 MiB RAM for secure telemetry.</li>
<li><strong>UGVs & Rovers (TuringPi + CM4):</strong> Dual-pod setups at 5–10 Hz for redundant, signed location streams.</li>
<li><strong>Space-Grade Emulation:</strong> Orin NX pods with Kalman filtering and PQC signing for deep-space comms.</li>
</ul>
</div>
<div class="section">
<h2>📦 Pods & Container Deployment</h2>
<p>
In Ptah, every core function—post-quantum signing/encryption, telemetry acquisition, and monitoring—is packaged as a self-contained Docker image and deployed as a <em>pod</em> under K3s. This approach yields:
</p>
<ul>
<li>
<strong>Scalability:</strong> Define <code>replicaCount</code> in your Helm chart to scale a POD from 1 to N instances (e.g., running multiple Dilithium signers in parallel).
</li>
<li>
<strong>Resilience & Self-Healing:</strong> Liveness and readiness probes restart crashed containers automatically. For example, if a telemetry pod loses its GPS connection, K3s will recreate it within seconds.
</li>
<li>
<strong>Resource-Aware Scheduling:</strong>
<ul>
<li>Use <code>resources.requests</code> and <code>resources.limits</code> to reserve CPU/RAM exactly—for example, <code>0.5 CPU</code> and <code>256Mi</code> for a PQC service on CM4.</li>
<li>Leverage <code>nodeSelector</code> or <code>affinity</code> rules to pin GPU-intensive pods to Jetson Orin NX (requesting <code>nvidia.com/gpu: 1</code>), while lightweight ASCON pods run on Pi Zero nodes.</li>
</ul>
</li>
<li>
<strong>Sidecar & Init Containers:</strong>
<ul>
<li>An init container can wait for hardware readiness (e.g., ensure the GPS serial port is available before starting the telemetry app).</li>
<li>A sidecar can run a small heartbeat exporter, feeding health metrics to Prometheus without modifying the main application.</li>
</ul>
</li>
<li>
<strong>Rolling Updates & Canary Deployments:</strong>
<ul>
<li>Set <code>strategy.type: RollingUpdate</code> so PQC libraries can be patched without downtime—K3s will bring up new pods with the updated container image and gracefully retire old ones.</li>
<li>Use <code>maxSurge</code> and <code>maxUnavailable</code> to control the pace of updates, crucial when running on mission-critical UGVs or drone networks.</li>
</ul>
</li>
</ul>
<h3>Example Pod Spec</h3>
<pre><code>apiVersion: v1
kind: Pod
metadata:
name: pqc-signer
labels:
app: pqc
spec:
initContainers:
- name: wait-for-gps
image: busybox
command: ["sh", "-c", "until test -e /dev/ttyUSB0; do sleep 1; done"]
volumeMounts:
- mountPath: /dev/ttyUSB0
name: gps-device
containers:
- name: signer
image: rscl/pqc-signer:latest
resources:
requests:
cpu: "0.5"
memory: "256Mi"
limits:
cpu: "1"
memory: "512Mi"
volumeMounts:
- mountPath: /dev/ttyUSB0
name: gps-device
livenessProbe:
exec:
command: ["pgrep", "signer"]
initialDelaySeconds: 10
periodSeconds: 30
volumes:
- name: gps-device
hostPath:
path: /dev/ttyUSB0
nodeSelector:
kubernetes.io/hostname: cm4-node-01
</code></pre>
<p>
This spec ensures the signer pod only runs on a CM4 node, waits for its GPS device, reserves half a CPU core, and restarts if the process dies—demonstrating the full power of K3s pod orchestration in Ptah’s heterogeneous cluster.
</p>
</div>
<div class="section">
<h2>📈 Performance Monitoring</h2>
<p>
To maintain operational excellence across a heterogeneous Ptah cluster, we employ a best-in-class monitoring stack:
</p>
<ol>
<li>
<strong>Metrics Collection (Prometheus):</strong>
• <em>Node Exporter</em> on each Linux node (CM4, TRK1, Jetsons, Pi Zeros) scrapes CPU, memory, filesystem, and temperature.
• <em>cAdvisor</em> or <em>kubelet metrics</em> expose container-level stats: CPU throttling, memory usage, network I/O.
• Custom <em>PQC Exporter</em> in each crypto pod emits counters (signatures/sec, KEM ops/sec) and histograms (latency distribution).
</li>
<li>
<strong>Storage & Retention:</strong>
• Prometheus TSDB stores high-resolution (1s scrape) data for 24 h, then down-samples to 1 min resolution for 30 days.
• Remote write to long-term storage (e.g., Thanos or Cortex) for 1 year of historical analysis.
</li>
<li>
<strong>Visualization (Grafana):</strong>
• Dashboards for each hardware class:
– CPU & Memory Utilization vs. Crypto Throughput (ops/sec)
– Network Bandwidth & Packet Loss for telemetry streams
– GPU Utilization and Temperature on Jetson modules
• Alert rules:
– CPU >90 % for >1 min triggers High-Load alert
– Signature latency >5 ms on CM4 triggers Performance-degradation alert
– Missing telemetry heartbeat (>3 scrapes) triggers Pod-restart action
</li>
<li>
<strong>Sample PromQL Queries:</strong>
<pre><code># CPU usage on CM4 nodes
avg(rate(node_cpu_seconds_total{instance=~"cm4-.*",mode!="idle"}[1m])) by (instance)
# PQC ops per second
rate(pqc_signatures_total[30s])
# Telemetry packet latency
histogram_quantile(0.95, rate(telemetry_latency_seconds_bucket[5m]))
</code></pre>
</li>
<li>
<strong>Scalability & Federation:</strong>
• Shard scraping across multiple Prometheus replicas for large swarms (>100 nodes).
• Use Prometheus Federation to centralize critical metrics (e.g., overall cluster health) while preserving local dashboards.
</li>
</ol>
<p>
This comprehensive monitoring framework not only provides real-time visibility into resource usage and cryptographic performance but also enables automated alerting and long-term trend analysis—ensuring that Ptah deployments remain robust, performant, and mission-ready.
</p>
</div>
<div class="section">
<h2>🎥 Video Resources</h2>
<p>For deeper insights and demonstrations, explore our curated video playlist. Each link includes an overview of Ptah concepts, hands-on labs, and system walkthroughs:</p>
<ul>
<li><strong>Ptah Framework Overview:</strong> A comprehensive introduction to Secure Edge-AI & Quantum-Proof Crypto.<br>
<a href="https://www.youtube.com/watch?v=131QKl_34bk" target="_blank">https://www.youtube.com/watch?v=131QKl_34bk</a>
</li>
<li><strong>Post-Quantum Crypto Lab:</strong> Live demonstration of Dilithium & Kyber implementations.<br>
<a href="https://www.youtube.com/watch?v=8pVTYIt6LCA" target="_blank">https://www.youtube.com/watch?v=8pVTYIt6LCA</a>
</li>
<li><strong>Lightweight Cryptography Deep Dive:</strong> ASCON sponge and AEAD usage on microcontrollers.<br>
<a href="https://www.youtube.com/watch?v=0ujsGjr43ig&feature=youtu.be" target="_blank">https://www.youtube.com/watch?v=0ujsGjr43ig&feature=youtu.be</a>
</li>
<li><strong>Distributed Orchestration Demo:</strong> K3s deployment across heterogeneous nodes.<br>
<a href="https://www.youtube.com/watch?v=QuFMe6n7Z-Y" target="_blank">https://www.youtube.com/watch?v=QuFMe6n7Z-Y</a>
</li>
</ul>
</div>
<div class="section">
<h2>15-Week Course Flow</h2>
<ul class="timeline">
<li><span>Weeks 1–3</span> Project intro, PQC & edge fundamentals, hardware setup.</li>
<li><span>Weeks 4–6</span> Orchestration evaluation & K3s rollout.</li>
<li><span>Weeks 7–9</span> PQC services (Dilithium & Kyber) containerization & benchmarking.</li>
<li><span>Weeks 10–11</span> ASCON implementation, GPS telemetry pipeline, Helm charts.</li>
<li><span>Weeks 12–13</span> Monitoring stack (Prometheus/Grafana), metrics dashboards.</li>
<li><span>Week 14</span> Integration tests, drone/vehicle demos, optimization.</li>
<li><span>Week 15</span> Final report, live demo, quizzes on key topics.</li>
</ul>
</div>
<div class="section">
<h2>Final Quiz</h2>
<p>When you’re ready, dive into the comprehensive 40-question quiz covering every module. You’ll get instant feedback on each answer—all on one page.</p>
<p style="text-align:center;"><a href="assignment.html" class="button">Take the Final Quiz</a></p>
</div>
<div class="section">
<h2>🤝 Acknowledgments</h2>
<div class="logos" style="display:flex;justify-content:center;align-items:center;flex-wrap:wrap;gap:20px;margin:20px 0;">
<div style="text-align:center;">
<img src="assets/NASA_logo.png" alt="NASA" style="height:60px;"><br>
<small>NASA MINDS</small>
</div>
<div style="text-align:center;">
<img src="assets/USNavy_logo.png" alt="U.S. Navy" style="height:60px;"><br>
<small>U.S. Navy</small>
</div>
<div style="text-align:center;">
<img src="assets/AMD_Xilinx_logo.png" alt="AMD/Xilinx" style="height:60px;"><br>
<small>AMD/Xilinx</small>
</div>
<div style="text-align:center;">
<img src="assets/NVIDIA_logo.png" alt="NVIDIA" style="height:60px;"><br>
<small>NVIDIA</small>
</div>
<div style="text-align:center;">
<img src="assets/AFRL_logo.png" alt="AFRL" style="height:60px;"><br>
<small>AFRL</small>
</div>
<div style="text-align:center;">
<img src="assets/RSCL_logo.png" alt="RSCL" style="height:60px;"><br>
<small>RSCL</small>
</div>
</div>
<p style="text-align:center;">
Special thanks to all our partners for hardware, funding, and expertise that made this course possible.
</p>
</div>
</body>
</html>