Why ALN?
ALN applies experience with previous network connections to help tune future network connections. Current Internet transport protocols do not recover context or experience between sessions, and so restart anew for each new connection. ALN applies knowledge about past context to help tune TCP, the dominant transport protocol, to provide enhanced performance.
About TCP
TCP connections maintain state that describes the property of a connection to provide reasonable throughput for a set of network conditions. During a connection, this state evolves and tunes, and the algorithms therein are well understood.
Unfortunately, TCP does not reuse this acquired knowledge for new connections. New connections, even those started during existing connections to the same site, do not apply this knowledge; instead, all connections are started with the same initial state.
There are some cases where TCP has tried to reuse state. Round trip times and maximum packet size are sometimes cached in a table. RFC2140 proposed explicitly sharing state between concurrent connections and reusing state to recurring sites or networks, and the Congestion Manager tried to integrate the state of multiple connections in a single mechanism.
However, much of this state cannot be simply reused. Networks experience daily and weekly patterns of congestion. Some of these patterns also vary with holidays, weather, and a number of other external context. Unfortunately, this can be difficult to capture from within a network alone.
ALN tries to capture and discover these relationships, to allow TCP to start new connections with more appropriate initial conditions. This is based on a few observations about TCP connections:
- TCP does a good job converging on TCB state over time,
but a poor job (actually, none!) of guessing initial conditions - TCP experiences stable net over the connection,
and stable offered load
Item #1 means that we're not using smart predictors to replace TCP's closed-loop feedback system. Experience and analysis shows that it works very well, and the stability of the network requires that connections play the game by the same rules.
Item #2 is a simplifying assumption; we could try to find a predictor for the appropriate initial conditions for each connection. This assumption allows us to assume that the final conditions of a connection are reasonable appropriate initial conditions.
Networks have limited learning
Networks do learn. The Internet as a whole 'learns' reachability, in which ICMP (Internet signalling) redirects packets, blocks dead-ends, and informs about MTU (message transmission unit, i.e., packet size) issues. Connection protocols such as TCP, SCTP, and DCCP learn during the lifetime of a connection. There are other protocols that don't learn (UDP), but they are used very little.
There have been a number of attempts to share the learning of connection protocols. Transaction TCP (T/TCP) [RFC1644], TCP Control Block Sharing [RFC2140], and the Congestion Manager [RFC3124] all explored ways of sharing state across connections. This sharing was limited, considering sharing between connections to the same address or LAN.
Unfortunately, external context about connections has not been reused, and can substantially affect how well previous connections can predict future connections. This is the dimension ALN explores, by applying external context and previous state experience to predict future initial state.
The goal of ALN is to help tune these connection protocols, to make them work better. We already know how to tune particular protocols to particular environments, but the challenge is that we don't always know when to use each optimization; ALN helps tune these protocols so that the right 'variant' is effectively used when needed, based on applying past experience.
Importance of a Solution
TCP is the dominant Internet protocol, affecting 90% of current applications, and an even higher percentage of current network traffic. Future network uses will be dominated by connection-oriented protocols like TCP, some variants of which are emerging (SCTP, DCCP), but all of which operate on the same principles.
Most of the Internet traffic is TCP. Below are (in order) the number of packets and bytes (in bandwidth) on a large Internet backbone.
Most applications use TCP as well. The diagrams below show the same traffic where non-TCP is shown in red and dark red; orange through blue are all TCP. Again, the first graph is by packets, and the second is by bytes.
Approach and Goals
ALN uses learning to continuously accumulate and integrate the context and history of past sessions, and applies that learning to the initial conditions of new sessions. It integrates session-oriented learning with established closed-loop feedback mechanisms. The specific goals are to apply learning, show the benefit, and measure the costs.
TCP Background
TCP uses a number of state variables to describe a connection. Some are independent - the endpoints, the date/time, and the ports that describe the service or application. Others are dependent, based on properties of the network at that time, between those points, for that service, and describe the network and endpoint capabilities. These include:
- CWND: sender side congestion window size
- RCV_WND receiver advertised window size
- SSTHRESH threshold for congestion avoidance
- RTT round trip time
- MTU maximum transmission unit (packet) size
- Buffer sizes
- Options for the connection or endpoint, e.g., NODELAY, SACK, etc., that describe TCP variations
Consider how some of these variables affect how TCP decides how to use the network, notably the 'windowing algorithm'. This algorithm determines how many packets are in-transit between the sender and receiver, i.e., pipelined and awaiting ackhowledgement:
TCP starts with a very small initial window (a few packets) and learns what the network can tolerate over time, ramping up during each new connection:
For most of the connection, TCP underutlizes the network. Each new connection starts from the same initial conditions. A perfect TCP, however, would already know the appropriate window size and use it from the beginning of the connection; the transfer would finish more quickly as a result (perfect in green, and current TCP in red).
ALN will try to approach the profile of perfect TCP, but we expect some differences. ALN will take some time to determine the appropriate initial conditions, so the connection will start a bit later. Further, the prediction may not be perfect; TCP will continue to converge, perhaps on a better value for the window. Shown below are these differences, with ALN's TCP in blue:
