diff --git a/scripts/gen_multi2.py b/scripts/gen_multi2.py
index 08ff6fc..2c2d58b 100644
--- a/scripts/gen_multi2.py
+++ b/scripts/gen_multi2.py
@@ -35,15 +35,15 @@ from gen_test_data import (
 # When a row has a given snam/cnam, its :ipd is replaced with one of these IPs.
 # This creates realistic topology — servers have high in-degree.
 
-# CDN: 3 edge nodes in different regions
-CDN_IPS  = ['203.0.113.10', '203.0.113.11', '203.0.113.12']
-API_IPS  = ['198.51.100.20', '198.51.100.21']
-MAIL_IPS = ['192.0.2.50', '192.0.2.51']
-AUTH_IPS = ['10.20.30.40']
-SOCIAL_IPS = ['172.16.100.10']
-STORAGE_IPS = ['198.51.100.50']
-STREAM_IPS = ['203.0.113.50', '203.0.113.51']
-SEARCH_IPS = ['198.51.100.60']
+# CDN: 3 edge nodes in different regions (Akamai/Cloudflare ranges)
+CDN_IPS  = ['23.0.5.10', '104.16.100.10', '151.101.1.10']
+API_IPS  = ['52.84.100.20', '54.230.100.20']       # Amazon CloudFront
+MAIL_IPS = ['74.125.200.26', '66.102.1.26']          # Google mail
+AUTH_IPS = ['40.126.32.100']                          # Microsoft Azure
+SOCIAL_IPS = ['157.240.1.35']                         # Facebook
+STORAGE_IPS = ['52.216.100.50']                       # Amazon S3
+STREAM_IPS = ['52.84.200.50', '54.192.200.50']        # Amazon CloudFront
+SEARCH_IPS = ['142.250.80.4']                         # Google
 
 SNAM_TO_IPS = {
     'cdn.example.com':      CDN_IPS,
@@ -75,25 +75,15 @@ ALL_SERVER_IPS = list(set(
     ip for pool in SNAM_TO_IPS.values() for ip in pool
 ))
 
-# Server IP → geo info (so GeoIP columns match when we remap :ipd)
-SERVER_GEO = {
-    '203.0.113.10':  {'lat': 37.77, 'lon': -122.42, 'city': 'San Francisco', 'country': 'us', 'isp': 'AT&T',       'org': 'Amazon.com Inc.'},
-    '203.0.113.11':  {'lat': 40.71, 'lon': -74.00,  'city': 'New York',      'country': 'us', 'isp': 'Verizon',    'org': 'Google LLC'},
-    '203.0.113.12':  {'lat': 51.51, 'lon': -0.13,   'city': 'London',        'country': 'gb', 'isp': 'BT Group',   'org': 'Microsoft Corp.'},
-    '198.51.100.20': {'lat': 31.23, 'lon': 121.47,  'city': 'Shanghai',      'country': 'cn', 'isp': 'China Mobile','org': 'Tencent'},
-    '198.51.100.21': {'lat': 39.90, 'lon': 116.40,  'city': 'Beijing',       'country': 'cn', 'isp': 'China Telecom','org': 'Alibaba Inc.'},
-    '192.0.2.50':    {'lat': 37.77, 'lon': -122.42, 'city': 'San Francisco', 'country': 'us', 'isp': 'AT&T',       'org': 'Google LLC'},
-    '192.0.2.51':    {'lat': 50.11, 'lon': 8.68,    'city': 'Frankfurt',     'country': 'de', 'isp': 'Deutsche Telekom', 'org': 'Meta Platforms'},
-    '10.20.30.40':   {'lat': 40.71, 'lon': -74.00,  'city': 'New York',      'country': 'us', 'isp': 'Verizon',    'org': 'Microsoft Corp.'},
-    '172.16.100.10': {'lat': 37.77, 'lon': -122.42, 'city': 'San Francisco', 'country': 'us', 'isp': 'AT&T',       'org': 'Meta Platforms'},
-    '198.51.100.50': {'lat': 40.71, 'lon': -74.00,  'city': 'New York',      'country': 'us', 'isp': 'Verizon',    'org': 'Amazon.com Inc.'},
-    '203.0.113.50':  {'lat': 1.35,  'lon': 103.82,  'city': 'Singapore',     'country': 'sg', 'isp': 'Singtel',    'org': 'Netflix Inc.'},
-    '203.0.113.51':  {'lat': 35.68, 'lon': 139.76,  'city': 'Tokyo',         'country': 'jp', 'isp': 'NTT',        'org': 'Apple Inc.'},
-    '198.51.100.60': {'lat': 37.77, 'lon': -122.42, 'city': 'San Francisco', 'country': 'us', 'isp': 'AT&T',       'org': 'Google LLC'},
-}
-
-# Proxy IPs — high out-degree (appear as :ips connecting to many servers)
-PROXY_IPS = ['10.0.1.1', '10.0.2.1', '10.0.3.1', '10.0.4.1', '10.0.5.1']
+# Proxy IPs — public IPs resolvable by GeoIP MMDB
+# MUST be public IPs that GeoIP can resolve (no private ranges!)
+PROXY_IPS = [
+    '1.1.1.1',       # Cloudflare DNS — US-West
+    '8.8.8.8',       # Google DNS — US-West
+    '223.5.5.5',     # AliDNS — CN
+    '208.67.222.222', # OpenDNS — US-East
+    '77.88.8.8',     # Yandex DNS — EU
+]
 
 
 def main():
@@ -179,17 +169,7 @@ def main():
                     row[':ips'] = random.choice(PROXY_IPS)
                     row['client-ip'] = row[':ips']
 
-                # Update GeoIP columns to match server location
-                geo = SERVER_GEO.get(dst_ip)
-                if geo:
-                    rlat = round(geo['lat'] + random.uniform(-0.3, 0.3), 6)
-                    rlon = round(geo['lon'] + random.uniform(-0.3, 0.3), 6)
-                    row[':ipd.latd'] = str(rlat)
-                    row[':ipd.lond'] = str(rlon)
-                    row[':ipd.city']  = geo['city']
-                    row[':ipd.ispn'] = geo['isp']
-                    row[':ipd.orgn'] = geo['org']
-                    row['dcnt'] = geo['country']
+                # GeoIP: use MMDB in upload flow; CSV lat/lon are ignored anyway
 
             file_rows.append(row)
             degree_src[row[':ips']] += 1