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Akbar Bespalov
Akbar Bespalov

Leaky Bucket Algorithm In Computer Networks Pdf !!HOT!! Download

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.

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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,[note 1] to defined limits on bandwidth and burstiness (a measure of the variations in the traffic flow).

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.

Two different methods of applying this leaky bucket analogy are described in the literature.[2][3][4][5] 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 of applying the analogy, the analogue of the bucket is a counter or variable, separate from the flow of traffic or schedule of events.[2][4][5] 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 analogous 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. So in this version, the analogue of the water is carried by the packets or the events, added to the bucket on their arriving or occurring, and then leaks away. This version is referred to here as the leaky bucket as a meter.

In the second version, the analogue of the bucket is a queue in the flow of traffic.[3] 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. As a result, the rate at which the queue is serviced directly controls the onward transmission rate of the traffic. Thus it imposes conformance rather than checking it, and where the queue 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. It is not clear how this version of applying the analogy might be used to check the rates of discrete events. This version is referred to here as ' the 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.[6]

Jonathan S. Turner is credited[7] with the original description of the leaky bucket algorithm and describes it as follows: "A counter associated with each user transmitting on a connection is incremented whenever the user sends a packet and is decremented periodically. If the counter exceeds a threshold upon being incremented, the network discards the packet. The user specifies the rate at which the counter is decremented (this determines the average bandwidth) and the value of the threshold (a measure of burstiness)".[2] The bucket (analogous to the counter) is in this case used as a meter to test the conformance of packets, rather than as a queue to directly control them.

Another description of what is essentially the same meter version of the algorithm, the generic cell rate algorithm, is given by the ITU-T in recommendation I.371 and in the ATM Forum's UNI Specification.[4][5] The description, in which the term cell is equivalent to packet in Turner's description[2] is given by the ITU-T as follows: "The continuous-state leaky bucket can be viewed as a finite capacity bucket whose real-valued content drains out at a continuous rate of 1 unit of content per time unit and whose content is increased by the increment T for each conforming cell... If at a cell arrival the content of the bucket is less than or equal to the limit value τ, then the cell is conforming; otherwise, the cell is non-conforming. The capacity of the bucket (the upper bound of the counter) is (T + τ)".[5] These specifications also state that, due to its finite capacity, if the contents of the bucket at the time the conformance is tested is greater than the limit value, and hence the cell is non-conforming, then the bucket is left unchanged; that is, the water is simply not added if it would make the bucket overflow.

David E. McDysan and Darrel L. Spohn provide a commentary on the description given by the ITU-T/ATM Forum. In this, they state "In the leaky bucket analogy, the [ATM] cells do not actually flow through the bucket; only the check for conforming admission does".[6] However, uncommonly in the descriptions in the literature, McDysan and Spohn also refer to the leaky bucket algorithm as a queue, going on "Note that one implementation of traffic shaping is to actually have the cells flow through the bucket".[6]

In describing the operation of the ITU-T's version of the algorithm, McDysan and Spohn invoke a "notion commonly employed in queueing theory of a fictional 'gremlin'".[6] This gremlin inspects the level in the bucket and takes action if the level is above the limit value τ : in policing (figure 2), it pulls open a trap door, which causes the arriving packet to be dropped and stops its water from entering the bucket; in shaping (figure 3), it pushes up a flap, which delays the arriving packet and prevents it from delivering its water, until the water level in the bucket falls below τ.

Neither Turner nor the ITU-T addresses the issue of variable-length packets. However, the description according to the ITU-T is for ATM cells, which are fixed length packets, and Turner does not specifically exclude variable length packets. Hence, in both cases, if the amount by which the bucket content or counter is incremented for a conforming packet is proportional to the packet length, they will both account for the length and allow the algorithm to limit the bandwidth of the traffic explicitly rather than limiting the packet rate. For example, shorter packets would add less to the bucket, and could thus arrive at smaller intervals; whereas, longer packets would add more and thus have to be separated by proportionally larger intervals to conform.

The leaky bucket as a meter can be used in either traffic shaping or traffic policing. For example, in ATM networks, in the form of the generic cell rate algorithm, it is used to compare the bandwidth and burstiness of traffic on a Virtual Channel (VC) or Virtual Path (VP) against the specified limits on the rate at which cells may arrive and the maximum jitter, or variation in inter-arrival intervals, for the VC or VP. In traffic policing, cells that do not conform to these limits (nonconforming cells) may be discarded (dropped) or may be reduced in priority (for downstream traffic management functions to drop if there is congestion). In traffic shaping, cells are delayed until they conform. Traffic policing and traffic shaping are commonly used in UPC/NPC to protect the network against excess or excessively bursty traffic, see bandwidth management and congestion avoidance. Traffic shaping is commonly used in the network interfaces in hosts to prevent transmissions from exceeding the bandwidth or jitter limits and being discarded by traffic management functions in the network. (See scheduling (computing) and network scheduler.)

The leaky bucket algorithm as a meter can also be used in a leaky bucket counter to measure the rate of random or stochastic processes. A Leaky bucket counter can be used to detect when the average or peak rate of events increases above some, acceptable, background level, i.e. when the bucket overflows.[8] However, events that do not cause an overflow, i.e. have sufficiently low rates and are well distributed over time, can be ignored. For example, such a leaky bucket counter can be used to detect when there is a sudden burst of correctable memory errors or when there has been a gradual, but significant, increase in the average rate, which may indicate an impending failure,[9] etc.


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