The leaky bucket is an algorithm based on an analogy of how a bucket with a constant leak will overflow if either the average rate at which water is poured in exceeds the rate at which the bucket leaks or if more water than the capacity of the bucket is poured in all at once. It can be used to determine whether some sequence of discrete events conforms to defined limits on their average and peak rates or frequencies, e.g. to limit the actions associated with these events to these rates or delay them until they do conform to the rates. It may also be used to check conformance or limit to an average rate alone, i.e. remove any variation from the average. It is used in packet-switched computer networks and telecommunications networks in both the traffic policing, traffic shaping and scheduling of data transmissions, in the form of packets, to defined limits on bandwidth and burstiness (a measure of the variations in the traffic flow). A version of the leaky bucket, the generic cell rate algorithm, is recommended for Asynchronous Transfer Mode (ATM) networks in UPC and NPC at user–network interfaces or inter-network interfaces or network-to-network interfaces to protect a network from excessive traffic levels on connections routed through it. The generic cell rate algorithm, or an equivalent, may also be used to shape transmissions by a network interface card onto an ATM network. At least some implementations of the leaky bucket are a mirror image of the token bucket algorithm and will, given equivalent parameters, determine exactly the same sequence of events to conform or not conform to the same limits.
Overview Two different methods of applying this leaky bucket analogy are described in the literature. These give what appear to be two different algorithms, both of which are referred to as the leaky bucket algorithm and generally without reference to the other method. This has resulted in confusion about what the leaky bucket algorithm is and what its properties are. In one version, the bucket is a counter or variable separate from the flow of traffic or schedule of events. This counter is used only to check that the traffic or events conform to the limits: The counter is incremented as each packet arrives at the point where the check is being made or an event occurs, which is equivalent to the way water is added intermittently to the bucket. The counter is also decremented at a fixed rate, equivalent to the way the water leaks out of the bucket. As a result, the value in the counter represents the level of the water in the bucket. If the counter remains below a specified limit value when a packet arrives or an event occurs, i.e., the bucket does not overflow, that indicates its conformance to the bandwidth and burstiness limits or the average and peak rate event limits. This version is referred to here as the leaky bucket as a meter. In the second version, the bucket is a queue in the flow of traffic. This queue is used to directly control that flow: Packets are entered into the queue as they arrive, equivalent to the water being added to the bucket. These packets are then removed from the queue (first come, first served), usually at a fixed rate, e.g., for onward transmission, equivalent to water leaking from the bucket. This configuration imposes conformance rather than checking it, and where the output is serviced at a fixed rate (and where the packets are all the same length), the resulting traffic stream is necessarily devoid of burstiness or jitter. So in this version, the traffic itself is the analogue of the water passing through the bucket. This version is referred to here as leaky bucket as a queue. The leaky bucket as a meter is exactly equivalent to (a mirror image of) the token bucket algorithm, i.e. the process of adding water to the leaky bucket exactly mirrors that of removing tokens from the token bucket when a conforming packet arrives, the process of leaking of water from the leaky bucket exactly mirrors that of regularly adding tokens to the token bucket, and the test that the leaky bucket will not overflow is a mirror of the test that the token bucket contains enough tokens and will not underflow. Thus, given equivalent parameters, the two algorithms will see the same traffic as conforming or nonconforming. The leaky bucket as a queue can be seen as a special case of the leaky bucket as a meter.
As a meter
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