# 🇫🇷 Marseille Ping & Network Latency (MRS) | AS203314

## 🇫🇷 Marseille (MRS)

Marseille serves as the strategic submarine cable landing hub bridging Europe to Africa, the Middle East, and Asia. Hats Network's PoP offers diverse path options via multiple cable systems.

Measurement round: `2026-08-16T04:07:28Z`.

This page provides round-trip time (RTT) latency metrics between the **Marseille (MRS)** PoP and every other node on the Hats Network (AS203314) global backbone.

### PoP Highlights

* **Location:** Marseille, France (Europe)
* **Region:** Europe
* **Role:** Backbone interconnection point for Hats Network (AS203314)
* **Public city reference:** [GeoNames 2995469](https://www.geonames.org/2995469) — 43.29695, 5.38107
* **Coverage:** RTT measurements to 19 other PoPs across 5 continents

### Facility & Interconnection

* **Facility:** MRS1

> Interactive content is available on the canonical HTML page.

### Latency Summary

| Metric                | Value                                                                                                       |
| --------------------- | ----------------------------------------------------------------------------------------------------------- |
| Fastest Route         | [🇫🇷 Marseille (MRS) → 🇫🇷 Paris (PAR)](/docs/network/latency/pairs/mrs-par-rtt) (**8.9 ms**) — Ultra-Low |
| Slowest Route         | [🇫🇷 Marseille (MRS) → 🇦🇺 Sydney (SYD)](/docs/network/latency/pairs/mrs-syd-rtt) (**240.6 ms**)          |
| Average RTT           | **118.4 ms**                                                                                                |
| Average Jitter        | **0.97 ms**                                                                                                 |
| Average Packet Loss   | **0.05%**                                                                                                   |
| Best Fiber Efficiency | **77.6%**                                                                                                   |
| Intra-Region Peers    | 6 PoPs in Europe                                                                                            |
| Total Routes          | 19 outbound / 19 inbound (100% coverage)                                                                    |

> Jitter, packet-loss, percentile, and trend fields are estimated comparative indicators. Read the [theoretical fiber latency methodology](/docs/network/latency/theoretical-fiber-latency).

## Geographic & Network Position

Marseille sits on Hats Network's **Europe** backbone. Measured round-trip times to its 6 intra-region peers range from **8.9 ms** (Paris (PAR)) to **49.6 ms** (Moscow (MOW)).

Marseille is France's Mediterranean submarine cable landing hub, where systems from Africa, the Middle East and Asia come ashore.

## Outbound Latency from Marseille (MRS)

Round-trip time in milliseconds **from Marseille** to all other backbone PoPs, sorted fastest to slowest.

| Destination             | RTT (ms)                                             | Fiber Floor | Efficiency | Jitter  | Loss  | Tier      |
| ----------------------- | ---------------------------------------------------- | ----------- | ---------- | ------- | ----- | --------- |
| 🇫🇷 Paris (PAR)        | **[8.9](/docs/network/latency/pairs/mrs-par-rtt)**   | 6.47 ms     | 72.7%      | 0.12 ms | 0%    | Ultra-Low |
| 🇩🇪 Frankfurt (FRA)    | **[16](/docs/network/latency/pairs/mrs-fra-rtt)**    | 7.82 ms     | 48.9%      | 0.18 ms | 0.15% | Ultra-Low |
| 🇬🇧 London (LON)       | **[18.9](/docs/network/latency/pairs/mrs-lon-rtt)**  | 9.82 ms     | 51.9%      | 0.15 ms | 0.09% | Ultra-Low |
| 🇳🇱 Amsterdam (AMS)    | **[20.3](/docs/network/latency/pairs/mrs-ams-rtt)**  | 9.89 ms     | 48.7%      | 0.17 ms | 0.16% | Ultra-Low |
| 🇩🇪 Berlin (BER)       | **[20.6](/docs/network/latency/pairs/mrs-ber-rtt)**  | 11.62 ms    | 56.4%      | 0.24 ms | 0%    | Ultra-Low |
| 🇷🇺 Moscow (MOW)       | **[49.6](/docs/network/latency/pairs/mrs-mow-rtt)**  | 26.23 ms    | 52.9%      | 0.45 ms | 0.07% | Excellent |
| 🇺🇸 New York (NYC)     | **[79.8](/docs/network/latency/pairs/mrs-nyc-rtt)**  | 61.91 ms    | 77.6%      | 0.78 ms | 0%    | Excellent |
| 🇺🇸 Ashburn (IAD)      | **[85.7](/docs/network/latency/pairs/mrs-iad-rtt)**  | 65.34 ms    | 76.2%      | 0.46 ms | 0.05% | Good      |
| 🇺🇸 Miami (MIA)        | **[112.4](/docs/network/latency/pairs/mrs-mia-rtt)** | 75.92 ms    | 67.5%      | 0.96 ms | 0.1%  | Good      |
| 🇸🇬 Singapore (SIN)    | **[139.6](/docs/network/latency/pairs/mrs-sin-rtt)** | 103.79 ms   | 74.3%      | 1.19 ms | 0%    | Good      |
| 🇺🇸 Seattle (SEA)      | **[141.7](/docs/network/latency/pairs/mrs-sea-rtt)** | 85.27 ms    | 60.2%      | 1.19 ms | 0.12% | Good      |
| 🇺🇸 Los Angeles (LAX)  | **[144.5](/docs/network/latency/pairs/mrs-lax-rtt)** | 95.09 ms    | 65.8%      | 0.78 ms | 0%    | Good      |
| 🇭🇰 Hong Kong (HKG)    | **[166.4](/docs/network/latency/pairs/mrs-hkg-rtt)** | 95.48 ms    | 57.4%      | 1.49 ms | 0%    | Fair      |
| 🇹🇼 Taipei (TPE)       | **[180.9](/docs/network/latency/pairs/mrs-tpe-rtt)** | 98.2 ms     | 54.3%      | 1.86 ms | 0%    | Fair      |
| 🇧🇷 São Paulo (GRU)    | **[186.7](/docs/network/latency/pairs/mrs-gru-rtt)** | 89.24 ms    | 47.8%      | 1.4 ms  | 0.05% | Fair      |
| 🇿🇦 Johannesburg (JNB) | **[198.5](/docs/network/latency/pairs/mrs-jnb-rtt)** | 78.71 ms    | 39.7%      | 1.78 ms | 0%    | Fair      |
| 🇯🇵 Tokyo (TYO)        | **[203.8](/docs/network/latency/pairs/mrs-tyo-rtt)** | 99.03 ms    | 48.6%      | 2.03 ms | 0.18% | Fair      |
| 🇦🇺 Melbourne (MEL)    | **[234.2](/docs/network/latency/pairs/mrs-mel-rtt)** | 162.36 ms   | 69.3%      | 1.1 ms  | 0%    | Fair      |
| 🇦🇺 Sydney (SYD)       | **[240.6](/docs/network/latency/pairs/mrs-syd-rtt)** | 165.41 ms   | 68.7%      | 2.14 ms | 0%    | Fair      |

## Fastest Routes to Marseille (MRS)

The 10 fastest measured routes **to Marseille**, ranked by average RTT. Min / max / stdev are computed from the same measurement round (50 ICMP echo probes per route, 100 ms interval); routes without a published per-sample round show the measured average RTT only.

| Rank | Source               | Avg RTT                                                 | Min       | Max       | Stdev   |
| ---- | -------------------- | ------------------------------------------------------- | --------- | --------- | ------- |
| 1    | 🇫🇷 Paris (PAR)     | **[8.9 ms](/docs/network/latency/pairs/par-mrs-rtt)**   | 8.59 ms   | 9.69 ms   | 0.26 ms |
| 2    | 🇩🇪 Frankfurt (FRA) | **[16.4 ms](/docs/network/latency/pairs/fra-mrs-rtt)**  | 15.96 ms  | 17.74 ms  | 0.37 ms |
| 3    | 🇬🇧 London (LON)    | **[17.9 ms](/docs/network/latency/pairs/lon-mrs-rtt)**  | 17.02 ms  | 21.69 ms  | 0.84 ms |
| 4    | 🇳🇱 Amsterdam (AMS) | **[19.6 ms](/docs/network/latency/pairs/ams-mrs-rtt)**  | 19.03 ms  | 20.95 ms  | 0.5 ms  |
| 5    | 🇩🇪 Berlin (BER)    | **[20.2 ms](/docs/network/latency/pairs/ber-mrs-rtt)**  | 19.92 ms  | 21.65 ms  | 0.31 ms |
| 6    | 🇷🇺 Moscow (MOW)    | **[49.9 ms](/docs/network/latency/pairs/mow-mrs-rtt)**  | 47.94 ms  | 53.35 ms  | 1.46 ms |
| 7    | 🇺🇸 New York (NYC)  | **[80.2 ms](/docs/network/latency/pairs/nyc-mrs-rtt)**  | 78.94 ms  | 85.06 ms  | 1.32 ms |
| 8    | 🇺🇸 Ashburn (IAD)   | **[86.5 ms](/docs/network/latency/pairs/iad-mrs-rtt)**  | 81.69 ms  | 97.62 ms  | 3.63 ms |
| 9    | 🇺🇸 Miami (MIA)     | **[113.7 ms](/docs/network/latency/pairs/mia-mrs-rtt)** | 110.36 ms | 126.2 ms  | 3.72 ms |
| 10   | 🇸🇬 Singapore (SIN) | **[140.6 ms](/docs/network/latency/pairs/sin-mrs-rtt)** | 138.21 ms | 150.15 ms | 2.29 ms |

## Inbound Latency to Marseille (MRS)

Round-trip time in milliseconds **from all other PoPs to Marseille**. Network paths may be asymmetric — inbound and outbound RTT can differ for the same city pair.

| Source                  | RTT (ms)                                             | Fiber Floor | Efficiency | Jitter  | Loss  | Tier      |
| ----------------------- | ---------------------------------------------------- | ----------- | ---------- | ------- | ----- | --------- |
| 🇫🇷 Paris (PAR)        | **[8.9](/docs/network/latency/pairs/par-mrs-rtt)**   | 6.47 ms     | 72.7%      | 0.12 ms | 0%    | Ultra-Low |
| 🇩🇪 Frankfurt (FRA)    | **[16.4](/docs/network/latency/pairs/fra-mrs-rtt)**  | 7.82 ms     | 47.7%      | 0.19 ms | 0%    | Ultra-Low |
| 🇬🇧 London (LON)       | **[17.9](/docs/network/latency/pairs/lon-mrs-rtt)**  | 9.82 ms     | 54.8%      | 0.16 ms | 0.14% | Ultra-Low |
| 🇳🇱 Amsterdam (AMS)    | **[19.6](/docs/network/latency/pairs/ams-mrs-rtt)**  | 9.89 ms     | 50.5%      | 0.12 ms | 0.03% | Ultra-Low |
| 🇩🇪 Berlin (BER)       | **[20.2](/docs/network/latency/pairs/ber-mrs-rtt)**  | 11.62 ms    | 57.5%      | 0.16 ms | 0.06% | Ultra-Low |
| 🇷🇺 Moscow (MOW)       | **[49.9](/docs/network/latency/pairs/mow-mrs-rtt)**  | 26.23 ms    | 52.6%      | 0.4 ms  | 0.05% | Excellent |
| 🇺🇸 New York (NYC)     | **[80.2](/docs/network/latency/pairs/nyc-mrs-rtt)**  | 61.91 ms    | 77.2%      | 0.53 ms | 0%    | Good      |
| 🇺🇸 Ashburn (IAD)      | **[86.5](/docs/network/latency/pairs/iad-mrs-rtt)**  | 65.34 ms    | 75.5%      | 0.72 ms | 0%    | Good      |
| 🇺🇸 Miami (MIA)        | **[113.7](/docs/network/latency/pairs/mia-mrs-rtt)** | 75.92 ms    | 66.8%      | 1.23 ms | 0.14% | Good      |
| 🇸🇬 Singapore (SIN)    | **[140.6](/docs/network/latency/pairs/sin-mrs-rtt)** | 103.79 ms   | 73.8%      | 1.48 ms | 0.2%  | Good      |
| 🇺🇸 Seattle (SEA)      | **[141.2](/docs/network/latency/pairs/sea-mrs-rtt)** | 85.27 ms    | 60.4%      | 1.25 ms | 0.04% | Good      |
| 🇺🇸 Los Angeles (LAX)  | **[143.9](/docs/network/latency/pairs/lax-mrs-rtt)** | 95.09 ms    | 66.1%      | 1.05 ms | 0.16% | Good      |
| 🇭🇰 Hong Kong (HKG)    | **[165.4](/docs/network/latency/pairs/hkg-mrs-rtt)** | 95.48 ms    | 57.7%      | 0.85 ms | 0.09% | Fair      |
| 🇿🇦 Johannesburg (JNB) | **[172](/docs/network/latency/pairs/jnb-mrs-rtt)**   | 78.71 ms    | 45.8%      | 1.53 ms | 0%    | Fair      |
| 🇹🇼 Taipei (TPE)       | **[179.5](/docs/network/latency/pairs/tpe-mrs-rtt)** | 98.2 ms     | 54.7%      | 1.4 ms  | 0%    | Fair      |
| 🇧🇷 São Paulo (GRU)    | **[187.7](/docs/network/latency/pairs/gru-mrs-rtt)** | 89.24 ms    | 47.5%      | 1.08 ms | 0.14% | Fair      |
| 🇯🇵 Tokyo (TYO)        | **[205.6](/docs/network/latency/pairs/tyo-mrs-rtt)** | 99.03 ms    | 48.2%      | 1.86 ms | 0.14% | Fair      |
| 🇦🇺 Melbourne (MEL)    | **[233.5](/docs/network/latency/pairs/mel-mrs-rtt)** | 162.36 ms   | 69.5%      | 2.54 ms | 0%    | Fair      |
| 🇦🇺 Sydney (SYD)       | **[240.5](/docs/network/latency/pairs/syd-mrs-rtt)** | 165.41 ms   | 68.8%      | 2.18 ms | 0%    | Fair      |

## Europe Peers

Marseille is one of 7 Hats Network PoPs in **Europe**. Intra-region routes offer the lowest latency and highest path diversity.

| PoP                                     | Code | Country     | RTT (ms) | Tier      |
| --------------------------------------- | ---- | ----------- | -------- | --------- |
| [🇳🇱 Amsterdam (AMS)](./ams-amsterdam) | AMS  | Netherlands | **20.3** | Ultra-Low |
| [🇩🇪 Berlin (BER)](./ber-berlin)       | BER  | Germany     | **20.6** | Ultra-Low |
| [🇩🇪 Frankfurt (FRA)](./fra-frankfurt) | FRA  | Germany     | **16**   | Ultra-Low |
| [🇬🇧 London (LON)](./lon-london)       | LON  | UK          | **18.9** | Ultra-Low |
| [🇷🇺 Moscow (MOW)](./mow-moscow)       | MOW  | Russia      | **49.6** | Excellent |
| [🇫🇷 Paris (PAR)](./par-paris)         | PAR  | France      | **8.9**  | Ultra-Low |

## Frequently Asked Questions

**What is the fastest route to Marseille?**

The fastest measured route to Marseille (MRS) is [Paris (PAR) → Marseille (MRS)](/docs/network/latency/pairs/par-mrs-rtt), averaging **8.9 ms** RTT (Ultra-Low).

**What is the fastest route from Marseille?**

The fastest measured route from Marseille (MRS) is [Marseille (MRS) → Paris (PAR)](/docs/network/latency/pairs/mrs-par-rtt), averaging **8.9 ms** RTT (Ultra-Low).

**How well connected is Marseille to the Hats Network backbone?**

Marseille (MRS) maintains measured routes to all 19 other PoPs across 5 continents. The slowest route, [Marseille (MRS) → Sydney (SYD)](/docs/network/latency/pairs/mrs-syd-rtt), averages **240.6 ms** RTT.

## Open Data

Measured latency data for Marseille (MRS) is published daily under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) as part of the Hats Network open latency dataset:

* **Per-route files** — every directed route from Marseille is published as `mrs-<destination>.pings.{csv,json,yaml}` under [`/opendata/latency/latest/pairs/`](/opendata/latency/latest/pairs/) — for example [mrs-par.pings.csv](/opendata/latency/latest/pairs/mrs-par.pings.csv), the 50-probe ICMP echo round for [Marseille (MRS) → Paris (PAR)](/docs/network/latency/pairs/mrs-par-rtt).
* **Dataset documentation** — [/opendata/latency/](/opendata/latency/) covers file formats, versioning, checksums, and the aggregated full-matrix releases.

***

*Data auto-generated on August 16, 2026. Explore the [full interactive latency matrix](/docs/network/latency) for side-by-side comparison across all 20 PoPs.*

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## License and attribution

- **Canonical source:** [View the human-readable HTML page](https://hatsnet.io/docs/network/latency/mrs-marseille).
- **Original documentation:** © Hats Network Inc., licensed under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/).
- **Public latency data:** © Hats Network Inc., licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
- **Full terms:** [Content and Data License](https://hatsnet.io/docs/legal/content-and-data-license) — includes attribution requirements, third-party material, trademarks, source code and other exclusions.