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diff --git a/content/en/blog/news/20191006-new-site.md b/content/en/blog/news/20191006-new-site.md deleted file mode 100644 index c04ff2d..0000000 --- a/content/en/blog/news/20191006-new-site.md +++ /dev/null @@ -1,7 +0,0 @@ ---- -date: 2019-10-06 -title: "New Website" -linkTitle: "New Ouroboros website" -description: "Announcing the new website" -author: Dimitri Staessens ---- diff --git a/content/en/blog/news/20200116-hn.md b/content/en/blog/news/20200116-hn.md deleted file mode 100644 index b80a7bd..0000000 --- a/content/en/blog/news/20200116-hn.md +++ /dev/null @@ -1,30 +0,0 @@ ---- -date: 2020-01-16 -title: "Getting back to work" -linkTitle: "Getting back to work" -description: "Show HN - Ouroboros" -author: Dimitri Staessens ---- - -Yesterday there was a bit of an unexpected spike in interest in -Ouroboros following a [post on -HN](https://news.ycombinator.com/item?id=22052416). I'm really -humbled by the response and grateful to all the people that show -genuine interest in this project. - -I fully understand that people would like to know a lot more details -about Ouroboros than the current site provides. It was the top -priority on the todo list, and this new interest gives me some -additional motivation to get to it. There's a lot to Ouroboros that's -not so trivial, which makes writing clear documentation a tricky -thing to do. - -I will also tackle some of the questions from the HN in a series of -blog posts in the next few days, replacing the (very old and outdated) -FAQ section. I hope these will be useful. - -Again thank you for your interest. - -Sincerely, - -Dimitri diff --git a/content/en/blog/news/20200212-ecmp.md b/content/en/blog/news/20200212-ecmp.md deleted file mode 100644 index 019b40d..0000000 --- a/content/en/blog/news/20200212-ecmp.md +++ /dev/null @@ -1,68 +0,0 @@ ---- -date: 2020-02-12 -title: "Equal-Cost Multipath (ECMP)" -linkTitle: "Adding Equal-Cost multipath (ECMP)" -description: "ECMP is coming to Ouroboros (finally)" -author: Dimitri Staessens ---- - -Some recent news -- Multi-Path TCP (MPTCP) implementation is [landing -in mainstream Linux kernel -5.6](https://www.phoronix.com/scan.php?page=news_item&px=Linux-5.6-Starts-Multipath-TCP) --- finally got me to integrate the equal-cost multipath (ECMP) -implementation from [Nick Aerts's master -thesis](https://lib.ugent.be/nl/catalog/rug01:002494958) into -Ouroboros. And working on the ECMP implementation in gives me an -excuse to rant a little bit about MPTCP. - -The first question that comes to mind is: _Why is it called -multi-**path** TCP_? IP is routing packets, not TCP, and there are -equal-cost multipath options for IP in both [IS-IS and -OSPF](https://tools.ietf.org/html/rfc2991). Maybe _multi-flow TCP_ -would be a better name? This would also be more transparent to the -fact that running MPTCP over longer hops will make less sense, since -the paths are more likely to converge over the same link. - -So _why is there a need for multi-path TCP_? The answer, of course, is -that the Internet Protocol routes packets between IP endpoints, which -are _interfaces_, not _hosts_. So, if a server is connected over 4 -interfaces, ECMP routing will not be of any help if one of them goes -down. The TCP connections will time out. Multipath TCP, however, is -actually establishing 4 subflows, each over a different interface. If -an interface goes down, MPTCP will still have 3 subflows ready. The -application is listening the the main TCP connection, and will not -notice a TCP-subflow timing out[^1]. - -This brings us, of course, to the crux of the problem. IP names the -[point of attachment](https://tools.ietf.org/html/rfc1498); IP -addresses are assigned to interfaces. Another commonly used workaround -is a virtual IP interface on the loopback, but then you need a lot of -additional configuration (and if that were the perfect solution, one -wouldn't need MPTCP!). MPTCP avoids the network configuration mess, -but does require direct modification in the application using -[additions to the sockets -API](https://tools.ietf.org/html/draft-hesmans-mptcp-socket-03) in the -form of a bunch of (ugly) setsockopts. - -Now this is a far from ideal situation, but given its constraints, -MPTCP is a workable engineering solution that will surely see its -uses. It's strange that it took years for MPTCP to get to this stage. - -Now, of course, Ouroboros does not assign addresses to -points-of-attachments ( _flow endpoints_). It doesn't even assign -addresses to hosts/nodes! Instead, the address is derived from the -forwarding protocol machines inside each node. (For the details, see -the [article](https://arxiv.org/pdf/2001.09707.pdf)). The net effect -is that an ECMP routing algorithm can cleanly handle hosts with -multiple interfaces. Details about the routing algorithm are not -exposed to application APIs. Instead, Ouroboros applications request -an implementation-independent _service_. - -The ECMP patch for Ouroboros is coming _soon_. Once it's available I -will also add a couple of tutorials on it. - -Peace. - -Dimitri - -[^1]: Question: Why are the subflows not UDP? That would avoid a lot of duplicated overhead (sequence numbers etc)... Would it be too messy on the socket API side?
\ No newline at end of file diff --git a/content/en/blog/news/20200216-ecmp.md b/content/en/blog/news/20200216-ecmp.md deleted file mode 100644 index ce632c9..0000000 --- a/content/en/blog/news/20200216-ecmp.md +++ /dev/null @@ -1,118 +0,0 @@ ---- -date: 2020-02-16 -title: "Equal-Cost Multipath (ECMP) routing" -linkTitle: "Equal-Cost multipath (ECMP) example" -description: "A very quick example of ECMP" -author: Dimitri Staessens ---- - -As promised, I added equal cost multipath routing to the Ouroboros -unicast IPCP. I will add some more explanations later when it's fully -tested and merge into the master branch, but you can already try it. -You will need to pull the _be_ branch. You will also need to have -_fuse_ installed to monitor the flows from _/tmp/ouroboros/_. The -following script will bootstrap a 4-node unicast network on your -machine that routes using ECMP: - -```bash -#!/bin/bash - -# create a local IPCP. This emulates the "Internet" -irm i b t local n local l local - -#create the first unicast IPCP with ecmp -irm i b t unicast n uni.a l net routing ecmp - -#bind the unicast IPCP to the names net and uni.a -irm b i uni.a n net -irm b i uni.a n uni.a - -#register these 2 names in the local IPCP -irm n r net l local -irm n r uni.a l local - -#create 3 more unicast IPCPs, and enroll them with the first -irm i e t unicast n uni.b l net -irm b i uni.b n net -irm b i uni.b n uni.b -irm n r uni.b l local - -irm i e t unicast n uni.c l net -irm b i uni.c n net -irm b i uni.c n uni.c -irm n r uni.c l local - -irm i e t unicast n uni.d l net -irm b i uni.d n net -irm b i uni.d n uni.d -irm n r uni.d l local - -#connect uni.b to uni.a this creates a DT flow and a mgmt flow -irm i conn name uni.b dst uni.a - -#now do the same for the others, creating a square -irm i conn name uni.c dst uni.b -irm i conn name uni.d dst uni.c -irm i conn name uni.d dst uni.a - -#register the oping application at 4 different locations -#this allows us to check the multipath implementation -irm n r oping.a i uni.a -irm n r oping.b i uni.b -irm n r oping.c i uni.c -irm n r oping.d i uni.d - -#bind oping program to oping names -irm b prog oping n oping.a -irm b prog oping n oping.b -irm b prog oping n oping.c -irm b prog oping n oping.d - -#good to go! -``` - -In order to test the setup, start an irmd (preferably in a terminal so -you can see what's going on). In another terminal, run the above -script and then start an oping server: - -```bash -$ ./ecmpscript -$ oping -l -Ouroboros ping server started. -``` - -This single server program will accept all flows for oping from any of -the unicast IPCPs. Ouroboros _multi-homing_ in action. - -Open another terminal, and type the following command: - -```bash -$ watch -n 1 'grep "sent (packets)" /tmp/ouroboros/uni.a/dt.*/6* | sed -n -e 1p -e 7p' -``` - -This will show you the packet statistics from the 2 data transfer -flows from the first IPCP (uni.a). - -On my machine it looks like this: - -``` -Every 1,0s: grep "sent (packets)" /tmp/ouroboros/uni.a/dt.*/6* | sed -n -e 1p -e 7p - -/tmp/ouroboros/uni.a/dt.1896199821/65: sent (packets): 10 -/tmp/ouroboros/uni.a/dt.1896199821/67: sent (packets): 6 -``` - -Now, from yet another terminal, run connect an oping client to oping.c -(the client should attach to the first IPCP, so oping.c should be the -one with 2 equal cost paths) and watch both counters increase: - -```bash -oping -n oping.c -i 100ms -``` - -When you do this to the other destinations (oping.b and oping.d) you -should see only one of the flow counters increasing. - -Hope you enjoyed this little demo! - -Dimitri diff --git a/content/en/blog/news/20200502-frcp.md b/content/en/blog/news/20200502-frcp.md deleted file mode 100644 index 28c5794..0000000 --- a/content/en/blog/news/20200502-frcp.md +++ /dev/null @@ -1,236 +0,0 @@ ---- -date: 2020-05-02 -title: "Flow and Retransmission Control Protocol (FRCP) implementation" -linkTitle: "Flow and Retransmission Control Protocol (FRCP)" -description: "A quick demo of FRCP" -author: Dimitri Staessens ---- - -With the longer weekend I had some fun implementing (parts of) the -[Flow and Retransmission Control Protocol (FRCP)](/docs/concepts/protocols/#flow-and-retransmission-control-protocol-frcp) -to the point that it's stable enough to bring you a very quick demo of it. - -FRCP is the Ouroboros alternative to TCP / QUIC / LLC. It assures -delivery of packets when the network itself isn't very reliable. - -The setup is simple: we run Ouroboros over the Ethernet loopback -adapter _lo_, -``` -systemctl restart ouroboros -irm i b t eth-dix l dix n dix dev lo -``` -to which we add some impairment -[_qdisc_](http://man7.org/linux/man-pages/man8/tc-netem.8.html): - -``` -$ sudo tc qdisc add dev lo root netem loss 8% duplicate 3% reorder 10% delay 1 -``` - -This causes the link to lose, duplicate and reorder packets. - -We can use the oping tool to uses different [QoS -specs](https://ouroboros.rocks/cgit/ouroboros/tree/include/ouroboros/qos.h) -and watch the behaviour. Quality-of-Service (QoS) specs are a -technology-agnostic way to request a network service (current -status - not finalized yet). I'll also capture tcpdump output. - -We start an oping server and tell Ouroboros for it to listen to the _name_ "oping": -``` -#bind the program oping to the name oping -irm b prog oping n oping -#register the name oping in the Ethernet layer that is attached to the loopback -irm n r oping l dix -#run the oping server -oping -l -``` - -We'll now send 20 pings. If you try this, it can be that the flow -allocation fails, due to the loss of a flow allocation packet (a bit -similar to TCP losing the first SYN). The oping client currently -doesn't retry flow allocation. The default payload for oping is 64 -bytes (of zeros); oping waits 2 seconds for all packets it has -sent. It doesn't detect duplicates. - -Let's first look at the _raw_ QoS cube. That's like best-effort -UDP/IP. In Ouroboros, however, it doesn't require a packet header at -all. - -First, the output of the client using a _raw_ QoS cube: -``` -$ oping -n oping -c 20 -i 200ms -q raw -Pinging oping with 64 bytes of data (20 packets): - -64 bytes from oping: seq=0 time=0.880 ms -64 bytes from oping: seq=1 time=0.742 ms -64 bytes from oping: seq=4 time=1.303 ms -64 bytes from oping: seq=6 time=0.739 ms -64 bytes from oping: seq=6 time=0.771 ms [out-of-order] -64 bytes from oping: seq=6 time=0.789 ms [out-of-order] -64 bytes from oping: seq=7 time=0.717 ms -64 bytes from oping: seq=8 time=0.759 ms -64 bytes from oping: seq=9 time=0.716 ms -64 bytes from oping: seq=10 time=0.729 ms -64 bytes from oping: seq=11 time=0.720 ms -64 bytes from oping: seq=12 time=0.718 ms -64 bytes from oping: seq=13 time=0.722 ms -64 bytes from oping: seq=14 time=0.700 ms -64 bytes from oping: seq=16 time=0.670 ms -64 bytes from oping: seq=17 time=0.712 ms -64 bytes from oping: seq=18 time=0.716 ms -64 bytes from oping: seq=19 time=0.674 ms -Server timed out. - ---- oping ping statistics --- -20 packets transmitted, 18 received, 2 out-of-order, 10% packet loss, time: 6004.273 ms -rtt min/avg/max/mdev = 0.670/0.765/1.303/0.142 ms -``` - -The _netem_ did a good job of jumbling up the traffic! There were a -couple out-of-order, duplicates, and quite some packets lost. - -Let's dig into an Ethernet frame captured from the "wire". The most -interesting thing its small total size: 82 bytes. - -``` -13:37:25.875092 00:00:00:00:00:00 (oui Ethernet) > 00:00:00:00:00:00 (oui Ethernet), ethertype Unknown (0xa000), length 82: - 0x0000: 0042 0040 0000 0001 0000 0011 e90c 0000 .B.@............ - 0x0010: 0000 0000 203f 350f 0000 0000 0000 0000 .....?5......... - 0x0020: 0000 0000 0000 0000 0000 0000 0000 0000 ................ - 0x0030: 0000 0000 0000 0000 0000 0000 0000 0000 ................ - 0x0040: 0000 0000 -``` - -The first 12 bytes are the two MAC addresses (all zeros), then 2 bytes -for the "Ethertype" (the default for an Ouroboros layer is 0xa000, so -you can create more layers and seperate them by Ethertype[^1]. The -Ethernet Payload is thus 68 bytes. The Ouroboros _ipcpd-eth-dix_ adds -and extra header of 4 bytes with 2 extra "fields". The first field we -needed to take over from our [Data -Transfer](/docs/concepts/protocols/) protocol: the Endpoint Identifier -that identifies the flow. The _ipcpd-eth-dix_ has two endpoints, one -for the client and one for the server. 0x0042 (66) is the destination -EID of the server, 0x0043 (67) is the destination EID of the client. -The second field is the _length_ of the payload in octets, 0x0040 = -64. This is needed because Ethernet II has a minimum frame size of 64 -bytes and pads smaller frames (called _runt frames_)[^2]. The -remaining 64 bytes are the oping payload, giving us an 82 byte packet. - -That's it for the raw QoS. The next one is _voice_. A voice service -usually requires packets to be delivered with little delay and jitter -(i.e. ASAP). Out-of-order packets are rejected since they cause -artifacts in the audio output. The voice QoS will enable FRCP, because -it needs to track sequence numbers. - -``` -$ oping -n oping -c 20 -i 200ms -q voice -Pinging oping with 64 bytes of data (20 packets): - -64 bytes from oping: seq=0 time=0.860 ms -64 bytes from oping: seq=2 time=0.704 ms -64 bytes from oping: seq=3 time=0.721 ms -64 bytes from oping: seq=4 time=0.706 ms -64 bytes from oping: seq=5 time=0.721 ms -64 bytes from oping: seq=6 time=0.710 ms -64 bytes from oping: seq=7 time=0.721 ms -64 bytes from oping: seq=8 time=0.691 ms -64 bytes from oping: seq=10 time=0.691 ms -64 bytes from oping: seq=12 time=0.702 ms -64 bytes from oping: seq=13 time=0.730 ms -64 bytes from oping: seq=14 time=0.716 ms -64 bytes from oping: seq=15 time=0.725 ms -64 bytes from oping: seq=16 time=0.709 ms -64 bytes from oping: seq=17 time=0.703 ms -64 bytes from oping: seq=18 time=0.693 ms -64 bytes from oping: seq=19 time=0.666 ms -Server timed out. - ---- oping ping statistics --- -20 packets transmitted, 17 received, 0 out-of-order, 15% packet loss, time: 6004.243 ms -rtt min/avg/max/mdev = 0.666/0.716/0.860/0.040 ms -``` - -As you can see, packets are delivered in-order, and some packets are -missing. Nothing fancy. Let's look at a data packet: - -``` -14:06:05.607699 00:00:00:00:00:00 (oui Ethernet) > 00:00:00:00:00:00 (oui Ethernet), ethertype Unknown (0xa000), length 94: - 0x0000: 0045 004c 0100 0000 eb1e 73ad 0000 0000 .E.L......s..... - 0x0010: 0000 0000 0000 0012 a013 0000 0000 0000 ................ - 0x0020: 705c e53a 0000 0000 0000 0000 0000 0000 p\.:............ - 0x0030: 0000 0000 0000 0000 0000 0000 0000 0000 ................ - 0x0040: 0000 0000 0000 0000 0000 0000 0000 0000 ................ - -``` - -The same 18-byte header is present. The flow endpoint ID is a -different one, and the length is also different. The packet is 94 -bytes, the payload length for the _ipcp-eth_dix_ is 0x004c = 76 -octets. So the FRCP header adds 12 bytes, the total overhead is 30 -bytes. Maybe a bit more detail on the FRCP header contents (more depth -is available the protocol documentation). The first 2 bytes are the -FLAGS (0x0100). There are only 7 flags, it's 16 bits for memory -alignment. This packet only has the DATA bit set. Then follows the -flow control window, which is 0 (not implemented yet). Then we have a -4 byte sequence number (eb1e 73ae = 3944641454)[^3] and a 4 byte ACK -number, which is 0. The remaining 64 bytes are the oping payload. - -Next, the data QoS: - -``` -$ oping -n oping -c 20 -i 200ms -q data -Pinging oping with 64 bytes of data (20 packets): - -64 bytes from oping: seq=0 time=0.932 ms -64 bytes from oping: seq=1 time=0.701 ms -64 bytes from oping: seq=2 time=200.949 ms -64 bytes from oping: seq=3 time=0.817 ms -64 bytes from oping: seq=4 time=0.753 ms -64 bytes from oping: seq=5 time=0.730 ms -64 bytes from oping: seq=6 time=0.726 ms -64 bytes from oping: seq=7 time=0.887 ms -64 bytes from oping: seq=8 time=0.878 ms -64 bytes from oping: seq=9 time=0.883 ms -64 bytes from oping: seq=10 time=0.865 ms -64 bytes from oping: seq=11 time=401.192 ms -64 bytes from oping: seq=12 time=201.047 ms -64 bytes from oping: seq=13 time=0.872 ms -64 bytes from oping: seq=14 time=0.966 ms -64 bytes from oping: seq=15 time=0.856 ms -64 bytes from oping: seq=16 time=0.849 ms -64 bytes from oping: seq=17 time=0.843 ms -64 bytes from oping: seq=18 time=0.797 ms -64 bytes from oping: seq=19 time=0.728 ms - ---- oping ping statistics --- -20 packets transmitted, 20 received, 0 out-of-order, 0% packet loss, time: 4004.491 ms -rtt min/avg/max/mdev = 0.701/40.864/401.192/104.723 ms -``` - -With the data spec, we have no packet loss, but some packets have been -retransmitted (hence the higher latency). The reason for the very high -latency is that the current implementation only ACKs on data packets, -this will be fixed soon. - -Looking at an Ethernet frame, it's again 94 bytes: - -``` -14:35:42.612066 00:00:00:00:00:00 (oui Ethernet) > 00:00:00:00:00:00 (oui Ethernet), ethertype Unknown (0xa000), length 94: - 0x0000: 0044 004c 0700 0000 81b8 0259 e2f3 eb59 .D.L.......Y...Y - 0x0010: 0000 0000 0000 0012 911a 0000 0000 0000 ................ - 0x0020: 86b3 273b 0000 0000 0000 0000 0000 0000 ..';............ - 0x0030: 0000 0000 0000 0000 0000 0000 0000 0000 ................ - 0x0040: 0000 0000 0000 0000 0000 0000 0000 0000 ................ - -``` - -The main difference is that it has 2 flags set (DATA + ACK), and it -thus contains both a sequence number (81b8 0259) and an -acknowledgement (e2f3 eb59). - -That's about it for now. More to come soon. - -Dimitri - -[^1]: Don't you love standards? One of the key design objectives for Ouroboros is exactly to avoid such shenanigans. Modify/abuse a header and Ouroboros should reject it because it _cannot work_, not because some standard says one shouldn't do it. -[^2]: Lesser known fact: Gigabit Ethernet has a 512 byte minimum frame size; but _carrier extension_ handles this transparently. -[^3]: In _network byte order_.
\ No newline at end of file diff --git a/content/en/blog/news/20200507-python-lb.png b/content/en/blog/news/20200507-python-lb.png Binary files differdeleted file mode 100644 index 89e710e..0000000 --- a/content/en/blog/news/20200507-python-lb.png +++ /dev/null diff --git a/content/en/blog/news/20200507-python.md b/content/en/blog/news/20200507-python.md deleted file mode 100644 index d4b3504..0000000 --- a/content/en/blog/news/20200507-python.md +++ /dev/null @@ -1,74 +0,0 @@ ---- -date: 2020-05-07 -title: "A Python API for Ouroboros" -linkTitle: "Python" -description: "Python" -author: Dimitri Staessens ---- - -Support for other programming languages than C/C++ has been on my todo -list for quite some time. The initial approach was using -[SWIG](http://www.swig.org), but we found the conversion always -clunky, it didn't completely work as we wanted to, and a while back we -just decided to deprecate it. Apart from C/C++ we only had a [rust -wrapper](https://github.com/chritchens/ouroboros-rs). - -Until now! I finally took the time to sink my teeth into the bindings -for Python. I had some brief looks at the -[ctypes](https://docs.python.org/3/library/ctypes.html) library a -while back, but this time I looked into -[cffi](https://cffi.readthedocs.io/en/latest/) and I was amazed at how -simple it was to wrap the more difficult functions that manipulate -blocks of memory (flow\_read, but definitely the async fevent() call). -And now there is path towards a 'nice' Python API. - -Here is a taste of what the -[oecho](https://ouroboros.rocks/cgit/ouroboros/tree/src/tools/oecho/oecho.c) -tool looks like in Python: - -```Python -from ouroboros import * -import argparse - - -def client(): - f = flow_alloc("oecho") - f.writeline("Hello, PyOuroboros!") - print(f.readline()) - f.dealloc() - - -def server(): - print("Starting the server.") - while True: - f = flow_accept() - print("New flow.") - line = f.readline() - print("Message from client is " + line) - f.writeline(line) - f.dealloc() - - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description='A simple echo client/server') - parser.add_argument('-l', '--listen', help='run as a server', action='store_true') - args = parser.parse_args() - if args.listen is True: - server() - else: - client() -``` - -I have more time in the next couple of days, so I expect this to be -released after the weekend. - -Oh, and here is a picture of Ouroboros load-balancing between the C (top right) -and Python (top left) implementations using the C and Python clients: - -{{<figure width="60%" src="/blog/news/20200507-python-lb.png">}} - -Can't wait to get the full API done! - -Cheers, - -Dimitri diff --git a/content/en/blog/news/20201212-congestion-avoidance.md b/content/en/blog/news/20201212-congestion-avoidance.md deleted file mode 100644 index f395a4f..0000000 --- a/content/en/blog/news/20201212-congestion-avoidance.md +++ /dev/null @@ -1,358 +0,0 @@ ---- -date: 2020-12-12 -title: "Congestion avoidance in Ouroboros" -linkTitle: "Congestion avoidance" -description: "API for congestion avoidance and the Ouroboros MB-ECN algorithm" -author: Dimitri Staessens ---- - -The upcoming 0.18 version of the prototype has a bunch of big -additions coming in, but the one that I'm most excited about is the -addition of congestion avoidance. Now that the implementation is -reaching its final shape, I just couldn't wait to share with the world -what it looks like, so here I'll talk a bit about how it works. - -# Congestion avoidance - -Congestion avoidance is a mechanism for a network to avoid situations -where the where the total traffic offered on a network element (link -or node) systemically exceeds its capacity to handle this traffic -(temporary overload due to traffic burstiness is not -congestion). While bursts can be handled with adding buffers to -network elements, the solution to congestion is to reduce the ingest -of traffic at the network endpoints that are sources for the traffic -over the congested element(s). - -I won't be going into too many details here, but there are two classes -of mechanisms to inform traffic sources of congestion. One is Explicit -Congestion Notification (ECN), where information is sent to the sender -that its traffic is traversing a congested element. This is a solution -that is, for instance, used by -[DataCenter TCP (DCTCP)](https://tools.ietf.org/html/rfc8257), -and is also supported by -[QUIC](https://www.ietf.org/archive/id/draft-ietf-quic-recovery-33.txt). -The other mechanism is implicit congestion detection, for instance by -inferring congestion from packet loss (most TCP flavors) or increases -in round-trip-time (TCP vegas). - -Once the sender is aware that its traffic is experiencing congestion, -it has to take action. A simple (proven) way is the AIMD algorithm -(Additive Increase, Multiplicative Decrease). When there is no sign of -congestion, senders will steadily increase the amount of traffic they -are sending (Additive Increase). When congestion is detected, they -will quickly back off (Multiplicative Decrease). Usually this is -augmented with a Slow Start (Multiplicative Increase) phase when the -senders begins to send, to reach the maximum bandwidth more -quickly. AIMD is used by TCP and QUIC (among others), and Ouroboros is -no different. It's been proven to work mathematically. - -Now that the main ingredients are known, we can get to the -preparation of the course. - -# Ouroboros congestion avoidance - -Congestion avoidance is in a very specific location in the Ouroboros -architecture: at the ingest point of the network; it is the -responsibility of the network, not the client application. In -OSI-layer terminology, we could say that in Ouroboros, it's in "Layer -3", not in "Layer 4". - -Congestion has to be dealt with for each individual traffic -source/destination pair. In TCP this is called a connection, in -Ouroboros we call it a _flow_. - -Ouroboros _flows_ are abstractions for the most basic of packet flows. -A flow is defined by two endpoints and all that a flow guarantees is -that there exist strings of bytes (packets) that, when offered at the -ingress endpoint, have a non-zero chance of emerging at the egress -endpoint. I say 'there exist' to allow, for instance, for maximum -packet lengths. If it helps, think of flow endpoints as an IP:UDP -address:port pair (but emphatically _NOT_ an IP:TCP address:port -pair). There is no protocol assumed for the packets that traverse the -flow. To the ingress and egress point, they are just a bunch of bytes. - -Now this has one major implication: We will need to add some -information to these packets to infer congestion indirectly or -explicitly. It should be obvious that explicit congestion notification -is the simplest solution here. The Ouroboros prototype (currently) -allows an octet for ECN. - -# Functional elements of the congestion API - -This section glances over the API in an informal way. A reference -manual for the actual C API will be added after 0.18 is in the master -branch of the prototype. The most important thing to keep in mind is -that the architecture dictates this API, not any particular algorithm -for congestion that we had in mind. In fact, to be perfectly honest, -up front I wasn't 100% sure that congestion avoidance was feasible -without adding additional fields fields to the DT protocol, such as a -packet counter, or sending some feedback for measuring the Round-Trip -Time (RTT). But as the algorithm below will show, it can be done. - -When flows are created, some state can be stored, which we call the -_congestion context_. For now it's not important to know what state is -stored in that context. If you're familiar with the inner workings of -TCP, think of it as a black-box generalization of the _tranmission -control block_. Both endpoints of a flow have such a congestion -context. - -At the sender side, the congestion context is updated for each packet -that is sent on the flow. Now, the only information that is known at -the ingress is 1) that there is a packet to be sent, and 2) the length -of this packet. The call at the ingress is thus: - -``` - update_context_at_sender <packet_length> -``` - -This function has to inform when it is allowed to actually send the -packet, for instance by blocking for a certain period. - -At the receiver flow endpoint, we have a bit more information, 1) that -a packet arrived, 2) the length of this packet, and 3) the value of -the ECN octet associated with this packet. The call at the egress is -thus: - -``` - update_context_at_receiver <packet_length, ecn> -``` - -Based on this information, receiver can decide if and when to update -the sender. We are a bit more flexible in what can be sent, at this -point, the prototype allows sending a packet (which we call -FLOW_UPDATE) with a 16-bit Explicit Congestion Experienced (ECE) field. - -This implies that the sender can get this information from the -receiver, so it knows 1) that such a packet arrived, and 2) the value -of the ECE field. - -``` - update_context_at_sender_ece <ece> -``` - -That is the API for the endpoints. In each Ouroboros IPCP (think -'router'), the value of the ECN field is updated. - -``` - update_ecn_in_router <ecn> -``` - -That's about as lean as as it gets. Now let's have a look at the -algorithm that I designed and -[implemented](https://ouroboros.rocks/cgit/ouroboros/tree/src/ipcpd/unicast/pol/ca-mb-ecn.c?h=be) -as part of the prototype. - -# The Ouroboros multi-bit Forward ECN (MB-ECN) algorithm - -The algorithm is based on the workings of DataCenter TCP -(DCTCP). Before I dig into the details, I will list the main -differences, without any judgement. - -* The rate for additive increase is the same _constant_ for all flows - (but could be made configurable for each network layer if - needed). This is achieved by having a window that is independent of - the Round-Trip Time (RTT). This may make it more fair, as congestion - avoidance in DCTCP (and in most -- if not all -- TCP variants), is - biased in favor of flows with smaller RTT[^1]. - -* Because it is operating at the _flow_ level, it estimates the - _actual_ bandwidth sent, including retransmissions, ACKs and what - not from protocols operating on the flow. DCTCP estimates bandwidth - based on which data offsets are acknowledged. - -* The algorithm uses 8 bits to indicate the queue depth in each - router, instead of a single bit (due to IP header restrictions) for - DCTCP. - -* MB-ECN sends a (small) out-of-band FLOW_UPDATE packet, DCTCP updates - in-band TCP ECN/ECE bits in acknowledgment (ACK) packets. Note that - DCTCP sends an immediate ACK with ECE set at the start of - congestion, and sends an immediate ACK with ECE not set at the end - of congestion. Otherwise, the ECE is set accordingly for any - "regular" ACKs. - -* The MB-ECN algorithm can be implemented without the need for - dividing numbers (apart from bit shifts). At least in the linux - kernel implementation, DCTCP has a division for estimating the - number of bytes that experienced congestion from the received acks - with ECE bits set. I'm not sure this can be avoided[^2]. - -Now, on to the MB-ECN algorithm. The values for some constants -presented here have only been quickly tested; a _lot_ more scientific -scrutiny is definitely needed here to make any statements about the -performance of this algorithm. I will just explain the operation, and -provide some very preliminary measurement results. - -First, like DCTCP, the routers mark the ECN field based on the -outgoing queue depth. The current minimum queue depth to trigger and -ECN is 16 packets (implemented as a bit shift of the queue size when -writing a packet). We perform a logical OR with the previous value of -the packet. If the width of the ECN field would be a single bit, this -operation would be identical to DCTCP. - -At the _receiver_ side, the context maintains two state variables. - -* The floating sum (ECE) of the value of the (8-bit) ECN field over the -last 2<sup>N</sup> packets is maintained (currently N=5, so 32 -packets). This is a value between 0 and 2<sup>8 + 5</sup> - 1. - -* The number of packets received during a period of congestion. This - is just for internal use. - -If th ECE value is 0, no actions are performed at the receiver. - -If this ECE value becomes higher than 0 (there is some indication of -start of congestion), an immediate FLOW_UPDATE is sent with this -value. If a packet arrives with ECN = 0, the ECE value is _halved_. - -For every _increase_ in the ECE value, an immediate update is sent. - -If the ECE value remains stable or decreases, an update is sent only -every M packets (currently, M = 8). This is what the counter is for. - -If the ECE value returns to 0 after a period of congestion, an -immediate FLOW_UPDATE with the value 0 is sent. - -At the _sender_ side, the context keeps track of the actual congestion -window. The sender keeps track of: - -* The current sender ECE value, which is updated when receiving a - FLOW_UPDATE. - -* A bool indicating Slow Start, which is set to false when a - FLOW_UPDATE arrives. - -* A sender_side packet counter. If this exceeds the value of N, the - ECE is reset to 0. This protects the sender from lost FLOW_UPDATES - that signal the end of congestion. - -* The window size multiplier W. For all flows, the window starts at a - predetermined size, 2<sup>W</sup> ns. Currently W = 24, starting at - about 16.8ms. The power of 2 allows us to perform operations on the - window boundaries using bit shift arithmetic. - -* The current window start time (a single integer), based on the - multiplier. - -* The number of packets sent in the current window. If this is below a - PKT_MIN threshold before the start of a window period, the new - window size is doubled. If this is above a PKT_MAX threshold before - the start of a new window period, the new window size is halved. The - thresholds are currently set to 8 and 64, scaling the window width - to average sending ~36 packets in a window. When the window scales, - the value for the allowed bytes to send in this window (see below) - scales accordingly to keep the sender bandwidth at the same - level. These values should be set with the value of N at the - receiver side in mind. - -* The number bytes sent in this window. This is updated when sending - each packet. - -* The number of allowed bytes in this window. This is calculated at - the start of a new window: doubled at Slow Start, multiplied by a - factor based on sender ECE when there is congestion, and increased - by a fixed (scaled) value when there is no congestion outside of - Slow Start. Currently, the scaled value is 64KiB per 16.8ms. - -There is one caveat: what if no FLOW_UPDATE packets arrive at all? -DCTCP (being TCP) will timeout at the Retransmission TimeOut (RTO) -value (since its ECE information comes from ACK packets), but this -algorithm has no such mechanism at this point. The answer is that we -currently do not monitor flow liveness from the flow allocator, but a -Keepalive or Bidirectional Forwarding Detection (BFD)-like mechanism -for flows should be added for QoS maintenance, and can serve to -timeout the flow and reset it (meaning a full reset of the -context). - -# MB-ECN in action - -From version 0.18 onwards[^3], the state of the flow -- including its -congestion context -- can be monitored from the flow allocator -statics: - -```bash -$ cat /tmp/ouroboros/unicast.1/flow-allocator/66 -Flow established at: 2020-12-12 09:54:27 -Remote address: 99388 -Local endpoint ID: 2111124142794211394 -Remote endpoint ID: 4329936627666255938 -Sent (packets): 1605719 -Sent (bytes): 1605719000 -Send failed (packets): 0 -Send failed (bytes): 0 -Received (packets): 0 -Received (bytes): 0 -Receive failed (packets): 0 -Receive failed (bytes): 0 -Congestion avoidance algorithm: Multi-bit ECN -Upstream congestion level: 0 -Upstream packet counter: 0 -Downstream congestion level: 48 -Downstream packet counter: 0 -Congestion window size (ns): 65536 -Packets in this window: 7 -Bytes in this window: 7000 -Max bytes in this window: 51349 -Current congestion regime: Multiplicative dec -``` - -I ran a quick test using the ocbr tool (modified to show stats every -100ms) on a jFed testbed using 3 Linux servers (2 clients and a -server) in star configuration with a 'router' (a 4th Linux server) in -the center. The clients are connected to the 'router' over Gigabit -Ethernet, the link between the 'router' and server is capped to 100Mb -using ethtool[^4]. - -Output from the ocbr tool: - -``` -Flow 64: 998 packets ( 998000 bytes)in 101 ms => 9880.8946 pps, 79.0472 Mbps -Flow 64: 1001 packets ( 1001000 bytes)in 101 ms => 9904.6149 pps, 79.2369 Mbps -Flow 64: 999 packets ( 999000 bytes)in 101 ms => 9882.8697 pps, 79.0630 Mbps -Flow 64: 998 packets ( 998000 bytes)in 101 ms => 9880.0143 pps, 79.0401 Mbps -Flow 64: 999 packets ( 999000 bytes)in 101 ms => 9887.6627 pps, 79.1013 Mbps -Flow 64: 999 packets ( 999000 bytes)in 101 ms => 9891.0891 pps, 79.1287 Mbps -New flow. -Flow 64: 868 packets ( 868000 bytes)in 102 ms => 8490.6583 pps, 67.9253 Mbps -Flow 65: 542 packets ( 542000 bytes)in 101 ms => 5356.5781 pps, 42.8526 Mbps -Flow 64: 540 packets ( 540000 bytes)in 101 ms => 5341.5105 pps, 42.7321 Mbps -Flow 65: 534 packets ( 534000 bytes)in 101 ms => 5285.6111 pps, 42.2849 Mbps -Flow 64: 575 packets ( 575000 bytes)in 101 ms => 5691.4915 pps, 45.5319 Mbps -Flow 65: 535 packets ( 535000 bytes)in 101 ms => 5291.0053 pps, 42.3280 Mbps -Flow 64: 561 packets ( 561000 bytes)in 101 ms => 5554.3455 pps, 44.4348 Mbps -Flow 65: 533 packets ( 533000 bytes)in 101 ms => 5272.0079 pps, 42.1761 Mbps -Flow 64: 569 packets ( 569000 bytes)in 101 ms => 5631.3216 pps, 45.0506 Mbps -``` - -With only one client running, the flow is congestion controlled to -about ~80Mb/s (indicating the queue limit at 16 packets may be a bit -too low a bar). When the second client starts sending, both flows go -quite quickly (at most 100ms) to a fair state of about 42 Mb/s. - -The IO graph from wireshark shows a reasonably stable profile (i.e. no -big oscillations because of AIMD), when switching the flows on the -clients on and off which is on par with DCTCP and not unexpected -keeping in mind the similarities between the algorithms: - -{{<figure width="60%" src="/blog/news/20201212-congestion.png">}} - -The periodic "gaps" were not seen at the ocbr endpoint applicationand -may have been due to tcpdump not capturing everything that those -points, or possibly a bug somewhere. - -As said, a lot more work is needed analyzing this algorithm in terms -of performance and stability[^5]. But I am feeling some excitement about its -simplicity and -- dare I say it? -- elegance. - -Stay curious! - -Dimitri - -[^1]: Additive Increase increases the window size with 1 MSS each - RTT. Slow Start doubles the window size each RTT. - -[^2]: I'm pretty sure the kernel developers would if they could. -[^3]: Or the current "be" branch for the less patient. -[^4]: Using Linux traffic control (```tc```) to limit traffic adds - kernel queues and may interfere with MB-ECN. -[^5]: And the prototype implementation as a whole! diff --git a/content/en/blog/news/20201212-congestion.png b/content/en/blog/news/20201212-congestion.png Binary files differdeleted file mode 100644 index 8e5b89f..0000000 --- a/content/en/blog/news/20201212-congestion.png +++ /dev/null diff --git a/content/en/blog/news/20201219-congestion-avoidance.md b/content/en/blog/news/20201219-congestion-avoidance.md deleted file mode 100644 index 7391091..0000000 --- a/content/en/blog/news/20201219-congestion-avoidance.md +++ /dev/null @@ -1,313 +0,0 @@ ---- -date: 2020-12-19 -title: "Exploring Ouroboros with wireshark" -linkTitle: "Exploring Ouroboros with wireshark " -description: "" -author: Dimitri Staessens ---- - -I recently did some -[quick tests](/blog/2020/12/12/congestion-avoidance-in-ouroboros/#mb-ecn-in-action) -with the new congestion avoidance implementation, and thought to -myself that it was a shame that Wireshark could not identify the -Ouroboros flows, as that could give me some nicer graphs. - -Just to be clear, I think generic network tools like tcpdump and -wireshark -- however informative and nice-to-use they are -- are a -symptom of a lack of network security. The whole point of Ouroboros is -that it is _intentionally_ designed to make it hard to analyze network -traffic. Ouroboros is not a _network stack_[^1]: one can't simply dump -a packet from the wire and derive the packet contents all the way up -to the application by following identifiers for protocols and -well-known ports. Using encryption to hide the network structure from -the packet is shutting the door after the horse has bolted. - -To write an Ouroboros dissector, one needs to know the layered -structure of the network at the capturing point at that specific point -in time. It requires information from the Ouroboros runtime on the -capturing machine and at the exact time of the capture, to correctly -analyze traffic flows. I just wrote a dissector that works for my -specific setup[^2]. - -## Congestion avoidance test - -First, a quick refresh on the experiment layout, it's the the same -4-node experiment as in the -[previous post](/blog/2020/12/12/congestion-avoidance-in-ouroboros/#mb-ecn-in-action) - -{{<figure width="80%" src="/blog/news/20201219-exp.svg">}} - -I tried to draw the setup as best as I can in the figure above. - -There are 4 rack mounted 1U servers, connected over Gigabit Ethernet -(GbE). Physically there is a big switch connecting all of them, but -each "link" is separated as a port-based VLAN, so there are 3 -independent Ethernet segments. We create 3 ethernet _layers_, drawn -in a lighter gray, with a single unicast layer -- consisting of 4 -unicast IPC processes (IPCPs) -- on top, drawn in a darker shade of -gray. The link between the router and server has been capped to 100 -megabit/s using ```ethtool```[^3], and traffic is captured on the -Ethernet NIC at the "Server" node using ```tcpdump```. All traffic is -generated with our _constant bit rate_ ```ocbr``` tool trying to send -about 80 Mbit/s of application-level throughput over the unicast -layer. - -{{<figure width="80%" src="/blog/news/20201219-congestion.png">}} - -The graph above shows the bandwidth -- as captured on the congested -100Mbit Ethernet link --, separated for each traffic flow, from the -same pcap capture as in my previous post. A flow can be identified by -a (destination address, endpoint ID)-pair, and since the destination -is all the same, I could filter out the flows by simply selecting them -based on the (64-bit) endpoint identifier. - -What you're looking at is that first, a flow (green starts), at around -T=14s, a new flow enters (red) that stops at around T=24s. At around -T=44s, another flow enters (blue) for about 14 seconds, and finally, a -fourth (orange) flow enters at T=63s. The first (green) flow exits at -around T=70s, leaving all the available bandwidth for the orange flow. - -The most important thing that I wanted to check is that when there are -multiple flows, _if_ and _how fast_ they would converge to the same -bandwidth. I'm not dissatisfied with the initial result: the answers -seem to be _yes_ and _pretty fast_, with no observable oscillation to -boot[^4] - -## Protocol overview - -Now, the wireshark dissector can be used to present some more details -about the Ouroboros protocols in a familiar setting -- make it more -accessible to some -- so, let's have a quick look. - -The Ouroboros network protocol has -[5 fields](/docs/concepts/protocols/#network-protocol): - -``` -| DST | TTL | QOS | ECN | EID | -``` - -which we had to map to the Ethernet II protocol for our ipcpd-eth-dix -implementation. The basic Ethernet II MAC (layer-2) header is pretty -simple. It has 2 6-byte addresses (dst, src) and a 2-byte Ethertype. - -Since Ethernet doesn't do QoS or congestion, the main missing field -here is the EID. We could have mapped it to the Ethertype, but we -noticed that a lot of routers and switches drop unknown Ethertypes -(and, for the purposes of this blog post here: it would have all but -prevented to write the dissector). So we made the ethertype -configurable per layer (so it can be set to a value that is not -blocked by the network), and added 2 16-bit fields after the Ethernet -MAC header for an Ouroboros layer: - -* Endpoint ID **eid**, which works just like in the unicast layer, to - identify the N+1 application (in our case: a data transfer flow and - a management flow for a unicast IPC process). - -* A length field **len**, which is needed because Ethernet NICs pad - frames that are smaller than 64 bytes in length with trailing zeros - (and we receive these zeros in our code). A length field is present - in Ethernet type I, but since most "Layer 3" protocols also had a - length field, it was re-purposed as Ethertype in Ethernet II. The - value of the **len** field is the length of the **data** payload. - -The Ethernet layer that spans that 100Mbit link has Ethertype 0xA000 -set (which is the Ouroboros default), the Ouroboros plugin hooks into -that ethertype. - -On top of the Ethernet layer, we have a unicast, layer with the 5 -fields specified above. The dissector also shows the contents of the -flow allocation messages, which are (currently) sent to EID = 0. - -So, the protocol header as analysed in the experiment is, starting -from the "wire": - -``` -+---------+---------+-----------+-----+-----+------ -| dst MAC | src MAC | Ethertype | eid | len | data /* ETH LAYER */ -+---------+---------+-----------+-----+-----+------ - - <IF eid != 0 > /* eid == 0 -> ipcpd-eth flow allocator, */ - /* this is not analysed */ - -+-----+-----+-----+-----+-----+------ -| DST | QOS | TTL | ECN | EID | DATA /* UNICAST LAYER */ -+-----+-----+-----+-----+-----+------ - - <IF EID == 0> /* EID == 0 -> flow allocator */ - -+-----+-------+-------+------+------+-----+-------------+ -| SRC | R_EID | S_EID | CODE | RESP | ECE | ... QOS ....| /* FA */ -+-----+-------+-------+------+------+-----+-------------+ -``` - -## The network protocol - -{{<figure width="80%" src="/blog/news/20201219-ws-0.png">}} - -We will first have a look at packets captured around the point in time -where the second (red) flow enters the network, about 14 seconds into -the capture. The "N+1 Data" packets in the image above all belong to -the green flow. The ```ocbr``` tool that we use sends 1000-byte data -units that are zeroed-out. The packet captured on the wire is 1033 -bytes in length, so we have a protocol overhead of 33 bytes[^5]. We -can break this down to: - -``` - ETHERNET II HEADER / 14 / - 6 bytes Ethernet II dst - 6 bytes Ethernet II src - 2 bytes Ethernet II Ethertype - OUROBOROS ETH-DIX HEADER / 4 / - 2 bytes eid - 2 byte len - OUROBOROS UNICAST NETWORK HEADER / 15 / - 4 bytes DST - 1 byte QOS - 1 byte TTL - 1 byte ECN - 8 bytes EID - --- TOTAL / 33 / - 33 bytes -``` - -The **Data (1019 bytes)** reported by wireshark is what Ethernet II -sees as data, and thus includes the 19 bytes for the two Ouroboros -headers. Note that DST length is configurable, currently up to 64 -bits. - -Now, let's have a brief look at the values for these fields. The -**eid** is 65, this means that the _data-transfer flow_ established -between the unicast IPCPs on the router and the server (_uni-r_ and -_uni-s_ in our experiment figure) is identified by endpoint id 65 in -the eth-dix IPCP on the Server machine. The **len** is 1015. Again, no -surprises, this is the length of the Ouroboros unicast network header -(15 bytes) + the 1000 bytes payload. - -**DST**, the destination address is 4135366193, a 32-bit address -that was randomly assigned to the _uni-s_ IPCP. The QoS cube is 0, -which is the default best-effort QoS class. *TTL* is 59. The starting -TTL is configurable for a layer, the default is 60, and it was -decremented by 1 in the _uni-r_ process on the router node. The packet -experienced no congestion (**ECN** is 0), and the endpoint ID is a -64-bit random number, 475...56. This endpoint ID identifies the flow -endpoint for the ```ocbr``` server. - -## The flow request - -{{<figure width="80%" src="/blog/news/20201219-ws-1.png">}} - -The first "red" packet that was captured is the one for the flow -allocation request, **FLOW REQUEST**[^6]. As mentioned before, the -endpoint ID for the flow allocator is 0. - -A rather important remark is in place here: Ouroboros does not allow a -UDP-like _datagram service_ from a layer. With which I mean: fabricate -a packet with the correct destination address and some known EID and -dump it in the network. All traffic that is offered to an Ouroboros -layer requires a _flow_ to be allocated. This keeps the network layer -in control its resources; the protocol details inside a layer are a -secret to that layer. - -Now, what about that well-known EID=0 for the flow allocator (FA)? And -the directory (Distributed Hash Table, DHT) for that matter, which is -currently on EID=1? Doesn't that contradict the "no datagram service" -statement above? Well, no. These components are part of the layer and -are thus inside the layer. The DHT and FA are internal -components. They are direct clients of the Data Transfer component. -The globally known EID for these components is an absolute necessity -since they need to be able to reach endpoints more than a hop -(i.e. a flow in a lower layer) away. - -Let's now look inside that **FLOW REQUEST** message. We know it is a -request from the **msg code** field[^7]. - -This is the **only** packet that contains the source (and destination) -address for this flow. There is a small twist, this value is decoded -with different _endianness_ than the address in the DT protocol output -(probably a bug in my dissector). The source address 232373199 in the -FA message corresponds to the address 3485194509 in the DT protocol -(and in the experiment image at the top): the source of our red flow -is the "Client 2" node. Since this is a **FLOW REQUEST**, the remote -endpoint id is not yet known, and set to 0[^8. The source endpoint ID --- a 64-bit randomly generated value unique to the source IPC -process[^9] -- is sent to the remote. The other fields are not -relevant for this message. - -## The flow reply - -{{<figure width="80%" src="/blog/news/20201219-ws-2.png">}} - -Now, the **FLOW REPLY** message for our request. It originates our -machine, so you will notice that the TTL is the starting value of 60. -The destination address is what we sent in our original **FLOW -REQUEST** -- add some endianness shenanigans. The **FLOW REPLY** -mesage response sends the newly generated source endpoint[^10] ID, and -this packet is the **only** packet that contains both endpoint IDs -for this flow. - -## Congestion / flow update - -{{<figure width="80%" src="/blog/news/20201219-ws-3.png">}} - -Now a quick look at the congestion avoidance mechanisms. The -information for the Additive Increase / Multiple Decrease algorithm is -gathered from the **ECN** field in the packets. When both flows are -active, they experience congestion since the requested bandwidth from -the two ```ocbr``` clients (180Mbit) exceeds the 100Mbit link, and the -figure above shows a packet marked with an ECN value of 11. - -{{<figure width="80%" src="/blog/news/20201219-ws-4.png">}} - -When the packets on a flow experience congestion, the flow allocator -at the endpoint (the one our _uni-s_ IPCP) will update the sender with -an **ECE** _Explicit Congestion Experienced_ value; in this case, 297. -The higher this value, the quicker the sender will decrease its -sending rate. The algorithm is explained a bit in my previous -post. - -That's it for today's post, I hope it provides some new insights how -Ouroboros works. As always, stay curious. - -Dimitri - -[^1]: Neither is RINA, for that matter. - -[^2]: This quick-and-dirty dissector is available in the - ouroboros-eth-uni branch on my - [github](https://github.com/dstaesse/wireshark/) - -[^3]: The prototype is able to handle Gigabit Ethernet, this is mostly - to make the size of the capture files somewhat manageable. - -[^4]: Of course, this needs more thorough evaluation with more - clients, distributions on the latency, different configurations - for the FRCP protocol in the N+1 and all that jazz. I have, - however, limited amounts of time to spare and am currently - focusing on building and documenting the prototype and tools so - that more thorough evaluations can be done if someone feels like - doing them. - -[^5]: A 4-byte Ethernet Frame Check Sequence (FCS) is not included in - the 'bytes on the wire'. As a reference, the minimum overhead - for this kind of setup using UDP/IPv4 is 14 bytes Ethernet + 20 - bytes IPv4 + 8 bytes UDP = 42 bytes. - -[^6]: Actually, in a larger network there could be some DHT traffic - related to resolving the address, but in such a small network, - the DHT is basically a replicated database between all 4 nodes. - -[^7]: The reason it's not the first field in the protocol has to to - with performance of memory alignment in x86 architectures. - -[^8]: We haven't optimised the FA protocol not to send fields it - doesn't need for that particular message type -- yet. - -[^9]: Not the host machine, but that particular IPCP on the host - machine. You can have multiple IPCPs for the same layer on the - same machine, but in this case, expect correlation between their - addresses. 64-bits / IPCP should provide some security against - remotes trying to hack into another service on the same host by - guessing EIDs. - -[^10]: This marks the point in space-time where I notice the - misspelling in the dissector.
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