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RFC 3290

An Informal Management Model for Diffserv Routers

Pages: 56
Informational
Part 2 of 3 – Pages 19 to 35
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Top   ToC   RFC3290 - Page 19   prevText

5. Meters

Metering is defined in [DSARCH]. Diffserv network providers may choose to offer services to customers based on a temporal (i.e., rate) profile within which the customer submits traffic for the service. In this event, a meter might be used to trigger real-time traffic conditioning actions (e.g., marking) by routing a non- conforming packet through an appropriate next-stage action element. Alternatively, by counting conforming and/or non-conforming traffic using a Counter element downstream of the Meter, it might also be used to help in collecting data for out-of-band management functions such as billing applications. Meters are logically 1:N (fan-out) devices (although a multiplexor can be used in front of a meter). Meters are parameterized by a temporal profile and by conformance levels, each of which is associated with a meter's output. Each output can be connected to another functional element. Note that this model of a meter differs slightly from that described in [DSARCH]. In that description the meter is not a datapath element but is instead used to monitor the traffic stream and send control signals to action elements to dynamically modulate their behavior based on the conformance of the packet. This difference in the description does not change the function of a meter. Figure 4 illustrates a meter with 3 levels of conformance. In some Diffserv examples (e.g., [AF-PHB]), three levels of conformance are discussed in terms of colors, with green representing conforming, yellow representing partially conforming and red representing non-conforming. These different conformance levels may be used to trigger different queuing, marking or dropping treatment later on in the processing. Other example meters use a binary notion of conformance; in the general case N levels of conformance can be supported. In general there is no constraint on the type of functional datapath element following a meter output, but care must be taken not to inadvertently configure a datapath that results in packet reordering that is not consistent with the requirements of the relevant PHB specification.
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      unmetered              metered
      traffic                traffic
                +---------+
                |         |--------> conformance A
      --------->|  meter  |--------> conformance B
                |         |--------> conformance C
                +---------+

      Figure 4. A Generic Meter

   A meter, according to this model, measures the rate at which packets
   making up a stream of traffic pass it, compares the rate to some set
   of thresholds, and produces some number of potential results (two or
   more):  a given packet is said to be "conformant" to a level of the
   meter if, at the time that the packet is being examined, the stream
   appears to be within the rate limit for the profile associated with
   that level.  A fuller discussion of conformance to meter profiles
   (and the associated requirements that this places on the schedulers
   upstream) is provided in Appendix A.

5.1. Examples

The following are some examples of possible meters.

5.1.1. Average Rate Meter

An example of a very simple meter is an average rate meter. This type of meter measures the average rate at which packets are submitted to it over a specified averaging time. An average rate profile may take the following form: Meter1: Type: AverageRate Profile: Profile1 ConformingOutput: Queue1 NonConformingOutput: Counter1 Profile1: Type: AverageRate AverageRate: 120 kbps Delta: 100 msec A Meter measuring against this profile would continually maintain a count that indicates the total number and/or cumulative byte-count of packets arriving between time T (now) and time T - 100 msecs. So long as an arriving packet does not push the count over 12 kbits in the last 100 msec, the packet would be deemed conforming. Any packet
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   that pushes the count over 12 kbits would be deemed non-conforming.
   Thus, this Meter deems packets to correspond to one of two
   conformance levels: conforming or non-conforming, and sends them on
   for the appropriate subsequent treatment.

5.1.2. Exponential Weighted Moving Average (EWMA) Meter

The EWMA form of Meter is easy to implement in hardware and can be parameterized as follows: avg_rate(t) = (1 - Gain) * avg_rate(t') + Gain * rate(t) t = t' + Delta For a packet arriving at time t: if (avg_rate(t) > AverageRate) non-conforming else conforming "Gain" controls the time constant (e.g., frequency response) of what is essentially a simple IIR low-pass filter. "Rate(t)" measures the number of incoming bytes in a small fixed sampling interval, Delta. Any packet that arrives and pushes the average rate over a predefined rate AverageRate is deemed non-conforming. An EWMA Meter profile might look something like the following: Meter2: Type: ExpWeightedMovingAvg Profile: Profile2 ConformingOutput: Queue1 NonConformingOutput: AbsoluteDropper1 Profile2: Type: ExpWeightedMovingAvg AverageRate: 25 kbps Delta: 10 usec Gain: 1/16

5.1.3. Two-Parameter Token Bucket Meter

A more sophisticated Meter might measure conformance to a token bucket (TB) profile. A TB profile generally has two parameters, an average token rate, R, and a burst size, B. TB Meters compare the arrival rate of packets to the average rate specified by the TB profile. Logically, tokens accumulate in a bucket at the average
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   rate, R, up to a maximum credit which is the burst size, B.  When a
   packet of length L arrives, a conformance test is applied.  There are
   at least two such tests in widespread use:

   Strict conformance
      Packets of length L bytes are considered conforming only if there
      are sufficient tokens available in the bucket at the time of
      packet arrival for the complete packet (i.e., the current depth is
      greater than or equal to L): no tokens may be borrowed from future
      token allocations.  For examples of this approach, see [SRTCM] and
      [TRTCM].

   Loose conformance
      Packets of length L bytes are considered conforming if any tokens
      are available in the bucket at the time of packet arrival: up to L
      bytes may then be borrowed from future token allocations.

   Packets are allowed to exceed the average rate in bursts up to the
   burst size.  For further discussion of loose and strict conformance
   to token bucket profiles, as well as system and implementation
   issues, see Appendix A.

   A two-parameter TB meter has exactly two possible conformance levels
   (conforming, non-conforming).  Such a meter might appear as follows:

      Meter3:
      Type:                SimpleTokenBucket
      Profile:             Profile3
      ConformanceType:     loose
      ConformingOutput:    Queue1
      NonConformingOutput: AbsoluteDropper1

      Profile3:
      Type:                SimpleTokenBucket
      AverageRate:         200 kbps
      BurstSize:           100 kbytes

5.1.4. Multi-Stage Token Bucket Meter

More complicated TB meters might define multiple burst sizes and more conformance levels. Packets found to exceed the larger burst size are deemed non-conforming. Packets found to exceed the smaller burst size are deemed partially-conforming. Packets exceeding neither are deemed conforming. Some token bucket meters designed for Diffserv networks are described in more detail in [SRTCM, TRTCM]; in some of these references, three levels of conformance are discussed in terms of colors with green representing conforming, yellow representing partially conforming, and red representing non-conforming. Note that
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   these multiple-conformance-level meters can sometimes be implemented
   using an appropriate sequence of multiple two-parameter TB meters.

   A profile for a multi-stage TB meter with three levels of conformance
   might look as follows:

      Meter4:
      Type:                TwoRateTokenBucket
      ProfileA:            Profile4
      ConformanceTypeA:    strict
      ConformingOutputA:   Queue1

      ProfileB:            Profile5
      ConformanceTypeB:    strict
      ConformingOutputB:   Marker1
      NonConformingOutput: AbsoluteDropper1

      Profile4:
      Type:                SimpleTokenBucket
      AverageRate:         100 kbps
      BurstSize:           20 kbytes

      Profile5:
      Type:                SimpleTokenBucket
      AverageRate:         100 kbps
      BurstSize:           100 kbytes

5.1.5. Null Meter

A null meter has only one output: always conforming, and no associated temporal profile. Such a meter is useful to define in the event that the configuration or management interface does not have the flexibility to omit a meter in a datapath segment. Meter5: Type: NullMeter Output: Queue1

6. Action Elements

The classifiers and meters described up to this point are fan-out elements which are generally used to determine the appropriate action to apply to a packet. The set of possible actions that can then be applied include: - Marking - Absolute Dropping
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   -    Multiplexing

   -    Counting

   -    Null action - do nothing

   The corresponding action elements are described in the following
   sections.

6.1. DSCP Marker

DSCP Markers are 1:1 elements which set a codepoint (e.g., the DSCP in an IP header). DSCP Markers may also act on unmarked packets (e.g., those submitted with DSCP of zero) or may re-mark previously marked packets. In particular, the model supports the application of marking based on a preceding classifier match. The mark set in a packet will determine its subsequent PHB treatment in downstream nodes of a network and possibly also in subsequent processing stages within this router. DSCP Markers for Diffserv are normally parameterized by a single parameter: the 6-bit DSCP to be marked in the packet header. Marker1: Type: DSCPMarker Mark: 010010

6.2. Absolute Dropper

Absolute Droppers simply discard packets. There are no parameters for these droppers. Because this Absolute Dropper is a terminating point of the datapath and has no outputs, it is probably desirable to forward the packet through a Counter Action first for instrumentation purposes. AbsoluteDropper1: Type: AbsoluteDropper Absolute Droppers are not the only elements than can cause a packet to be discarded: another element is an Algorithmic Dropper element (see Section 7.1.3). However, since this element's behavior is closely tied the state of one or more queues, we choose to distinguish it as a separate functional datapath element.
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6.3. Multiplexor

It is occasionally necessary to multiplex traffic streams into a functional datapath element with a single input. A M:1 (fan-in) multiplexor is a simple logical device for merging traffic streams. It is parameterized by its number of incoming ports. Mux1: Type: Multiplexor Output: Queue2

6.4. Counter

One passive action is to account for the fact that a data packet was processed. The statistics that result might be used later for customer billing, service verification or network engineering purposes. Counters are 1:1 functional datapath elements which update a counter by L and a packet counter by 1 every time a L-byte sized packet passes through them. Counters can be used to count packets about to be dropped by an Absolute Dropper or to count packets arriving at or departing from some other functional element. Counter1: Type: Counter Output: Queue1

6.5. Null Action

A null action has one input and one output. The element performs no action on the packet. Such an element is useful to define in the event that the configuration or management interface does not have the flexibility to omit an action element in a datapath segment. Null1: Type: Null Output: Queue1

7. Queuing Elements

Queuing elements modulate the transmission of packets belonging to the different traffic streams and determine their ordering, possibly storing them temporarily or discarding them. Packets are usually stored either because there is a resource constraint (e.g., available bandwidth) which prevents immediate forwarding, or because the queuing block is being used to alter the temporal properties of a traffic stream (i.e., shaping). Packets are discarded for one of the following reasons:
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      -  because of buffering limitations.
      -  because a buffer threshold is exceeded (including when shaping
         is performed).
      -  as a feedback control signal to reactive control protocols such
         as TCP.
      -  because a meter exceeds a configured profile (i.e., policing).

   The queuing elements in this model represent a logical abstraction of
   a queuing system which is used to configure PHB-related parameters.
   The model can be used to represent a broad variety of possible
   implementations.  However, it need not necessarily map one-to-one
   with physical queuing systems in a specific router implementation.
   Implementors should map the configurable parameters of the
   implementation's queuing systems to these queuing element parameters
   as appropriate to achieve equivalent behaviors.

7.1. Queuing Model

Queuing is a function which lends itself to innovation. It must be modeled to allow a broad range of possible implementations to be represented using common structures and parameters. This model uses functional decomposition as a tool to permit the needed latitude. Queuing systems perform three distinct, but related, functions: they store packets, they modulate the departure of packets belonging to various traffic streams and they selectively discard packets. This model decomposes queuing into the component elements that perform each of these functions: Queues, Schedulers, and Algorithmic Droppers, respectively. These elements may be connected together as part of a TCB, as described in section 8. The remainder of this section discusses FIFO Queues: typically, the Queue element of this model will be implemented as a FIFO data structure. However, this does not preclude implementations which are not strictly FIFO, in that they also support operations that remove or examine packets (e.g., for use by discarders) other than at the head or tail. However, such operations must not have the effect of reordering packets belonging to the same microflow. Note that the term FIFO has multiple different common usages: it is sometimes taken to mean, among other things, a data structure that permits items to be removed only in the order in which they were inserted or a service discipline which is non-reordering.
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7.1.1. FIFO Queue

In this model, a FIFO Queue element is a data structure which at any time may contain zero or more packets. It may have one or more thresholds associated with it. A FIFO has one or more inputs and exactly one output. It must support an enqueue operation to add a packet to the tail of the queue and a dequeue operation to remove a packet from the head of the queue. Packets must be dequeued in the order in which they were enqueued. A FIFO has a current depth, which indicates the number of packets and/or bytes that it contains at a particular time. FIFOs in this model are modeled without inherent limits on their depth - obviously this does not reflect the reality of implementations: FIFO size limits are modeled here by an algorithmic dropper associated with the FIFO, typically at its input. It is quite likely that every FIFO will be preceded by an algorithmic dropper. One exception might be the case where the packet stream has already been policed to a profile that can never exceed the scheduler bandwidth available at the FIFO's output - this would not need an algorithmic dropper at the input to the FIFO. This representation of a FIFO allows for one common type of depth limit, one that results from a FIFO supplied from a limited pool of buffers, shared between multiple FIFOs. In an implementation, packets are presumably stored in one or more buffers. Buffers are allocated from one or more free buffer pools. If there are multiple instances of a FIFO, their packet buffers may or may not be allocated out of the same free buffer pool. Free buffer pools may also have one or more thresholds associated with them, which may affect discarding and/or scheduling. Other than this, buffering mechanisms are implementation specific and not part of this model. A FIFO might be represented using the following parameters: Queue1: Type: FIFO Output: Scheduler1 Note that a FIFO must provide triggers and/or current state information to other elements upstream and downstream from it: in particular, it is likely that the current depth will need to be used by Algorithmic Dropper elements placed before or after the FIFO. It will also likely need to provide an implicit "I have packets for you" signal to downstream Scheduler elements.
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7.1.2. Scheduler

A scheduler is an element which gates the departure of each packet that arrives at one of its inputs, based on a service discipline. It has one or more inputs and exactly one output. Each input has an upstream element to which it is connected, and a set of parameters that affects the scheduling of packets received at that input. The service discipline (also known as a scheduling algorithm) is an algorithm which might take any of the following as its input(s): a) static parameters such as relative priority associated with each of the scheduler's inputs. b) absolute token bucket parameters for maximum or minimum rates associated with each of the scheduler's inputs. c) parameters, such as packet length or DSCP, associated with the packet currently present at its input. d) absolute time and/or local state. Possible service disciplines fall into a number of categories, including (but not limited to) first come, first served (FCFS), strict priority, weighted fair bandwidth sharing (e.g., WFQ), rate- limited strict priority, and rate-based. Service disciplines can be further distinguished by whether they are work-conserving or non- work-conserving (see Glossary). Non-work-conserving schedulers can be used to shape traffic streams to match some profile by delaying packets that might be deemed non-conforming by some downstream node: a packet is delayed until such time as it would conform to a downstream meter using the same profile. [DSARCH] defines PHBs without specifying required scheduling algorithms. However, PHBs such as the class selectors [DSFIELD], EF [EF-PHB] and AF [AF-PHB] have descriptions or configuration parameters which strongly suggest the sort of scheduling discipline needed to implement them. This document discusses a minimal set of queue parameters to enable realization of these PHBs. It does not attempt to specify an all-embracing set of parameters to cover all possible implementation models. A minimal set includes: a) a minimum service rate profile which allows rate guarantees for each traffic stream as required by EF and AF without specifying the details of how excess bandwidth between these traffic streams is shared. Additional parameters to control this behavior should be made available, but are dependent on the particular scheduling algorithm implemented.
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   b) a service priority, used only after the minimum rate profiles of
      all inputs have been satisfied, to decide how to allocate any
      remaining bandwidth.

   c) a maximum service rate profile, for use only with a non-work-
      conserving service discipline.

   Any one of these profiles is composed, for the purposes of this
   model, of both a rate (in suitable units of bits, bytes or larger
   chunks in some unit of time) and a burst size, as discussed further
   in Appendix A.

   By way of example, for an implementation of the EF PHB using a strict
   priority scheduling algorithm that assumes that the aggregate EF rate
   has been appropriately bounded by upstream policing to avoid
   starvation of other BAs, the service rate profiles are not used: the
   minimum service rate profile would be defaulted to zero and the
   maximum service rate profile would effectively be the "line rate".
   Such an implementation, with multiple priority classes, could also be
   used for the Diffserv class selectors [DSFIELD].

   Alternatively, setting the service priority values for each input to
   the scheduler to the same value enables the scheduler to satisfy the
   minimum service rates for each input, so long as the sum of all
   minimum service rates is less than or equal to the line rate.

   For example, a non-work-conserving scheduler, allocating spare
   bandwidth equally between all its inputs, might be represented using
   the following parameters:

      Scheduler1:
      Type:           Scheduler2Input

      Input1:
      MaxRateProfile: Profile1
      MinRateProfile: Profile2
      Priority:       none

      Input2:
      MaxRateProfile: Profile3
      MinRateProfile: Profile4
      Priority:       none

   A work-conserving scheduler might be represented using the following
   parameters:
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      Scheduler2:
      Type:           Scheduler3Input
      Input1:
      MaxRateProfile: WorkConserving
      MinRateProfile: Profile5
      Priority:       1

      Input2:
      MaxRateProfile: WorkConserving
      MinRateProfile: Profile6
      Priority:       2

      Input3:
      MaxRateProfile: WorkConserving
      MinRateProfile: none
      Priority:       3

7.1.3. Algorithmic Dropper

An Algorithmic Dropper is an element which selectively discards packets that arrive at its input, based on a discarding algorithm. It has one data input and one output. In this model (but not necessarily in a real implementation), a packet enters the dropper at its input and either its buffer is returned to a free buffer pool or the packet exits the dropper at the output. Alternatively, an Algorithmic Dropper can be thought of as invoking operations on a FIFO Queue which selectively remove a packet and return its buffer to the free buffer pool based on a discarding algorithm. In this case, the operation could be modeled as being a side-effect on the FIFO upon which it operated, rather than as having a discrete input and output. This treatment is equivalent and we choose the one described in the previous paragraph for this model. One of the primary characteristics of an Algorithmic Dropper is the choice of which packet (if any) is to be dropped: for the purposes of this model, we restrict the packet selection choices to one of the following and we indicate the choice by the relative positions of Algorithmic Dropper and FIFO Queue elements in the model: a) selection of a packet that is about to be added to the tail of a queue (a "Tail Dropper"): the output of the Algorithmic Dropper element is connected to the input of the relevant FIFO Queue element. b) a packet that is currently at the head of a queue (a "Head Dropper"): the output of the FIFO Queue element is connected to the input of the Algorithmic Dropper element.
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   Other packet selection methods could be added to this model in the
   form of a different type of datapath element.

   The Algorithmic Dropper is modeled as having a single input.  It is
   possible that packets which were classified differently by a
   Classifier in this TCB will end up passing through the same dropper.
   The dropper's algorithm may need to apply different calculations
   based on characteristics of the incoming packet (e.g., its DSCP).  So
   there is a need, in implementations of this model, to be able to
   relate information about which classifier element was matched by a
   packet from a Classifier to an Algorithmic Dropper.  In the rare
   cases where this is required, the chosen model is to insert another
   Classifier element at this point in the flow and for it to feed into
   multiple Algorithmic Dropper elements, each one implementing a drop
   calculation that is independent of any classification keys of the
   packet: this will likely require the creation of a new TCB to contain
   the Classifier and the Algorithmic Dropper elements.

      NOTE: There are many other formulations of a model that could
      represent this linkage that are different from the one described
      above: one formulation would have been to have a pointer from one
      of the drop probability calculation algorithms inside the dropper
      to the original Classifier element that selects this algorithm.
      Another way would have been to have multiple "inputs" to the
      Algorithmic Dropper element fed from the preceding elements,
      leading eventually back to the Classifier elements that matched
      the packet.  Yet another formulation might have been for the
      Classifier to (logically) include some sort of "classification
      identifier" along with the packet along its path, for use by any
      subsequent element.  And yet another could have been to include a
      classifier inside the dropper, in order for it to pick out the
      drop algorithm to be applied.  These other approaches could be
      used by implementations but were deemed to be less clear than the
      approach taken here.

   An Algorithmic Dropper, an example of which is illustrated in Figure
   5, has one or more triggers that cause it to make a decision whether
   or not to drop one (or possibly more than one) packet.  A trigger may
   be internal (the arrival of a packet at the input to the dropper) or
   it may be external (resulting from one or more state changes at
   another element, such as a FIFO Queue depth crossing a threshold or a
   scheduling event).  It is likely that an instantaneous FIFO depth
   will need to be smoothed over some averaging interval before being
   used as a useful trigger.  Some dropping algorithms may require
   several trigger inputs feeding back from events elsewhere in the
   system (e.g., depth-smoothing functions that calculate averages over
   more than one time interval).
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              +------------------+      +-----------+
              | +-------+        |  n   |smoothing  |
              | |trigger|<----------/---|function(s)|
              | |calc.  |        |      |(optional) |
              | +-------+        |      +-----------+
              |     |            |          ^
              |     v            |          |Depth
     Input    | +-------+ no     |      ------------+   to Scheduler
     ---------->|discard|-------------->    |x|x|x|x|------->
              | |   ?   |        |      ------------+
              | +-------+        |           FIFO
              |    |yes          |
              |  | | |           |
              |  | v | count +   |
              |  +---+ bit-bucket|
              +------------------+
              Algorithmic
              Dropper

      Figure 5. Example of Algorithmic Dropper from Tail of a Queue

   A trigger may be a boolean combination of events (e.g., a FIFO depth
   exceeding a threshold OR a buffer pool depth falling below a
   threshold).  It takes as its input some set of dynamic parameters
   (e.g., smoothed or instantaneous FIFO depth), and some set of static
   parameters (e.g., thresholds), and possibly other parameters
   associated with the packet.  It may also have internal state (e.g.,
   history of its past actions).  Note that, although an Algorithmic
   Dropper may require knowledge of data fields in a packet, as
   discovered by a Classifier in the same TCB, it may not modify the
   packet (i.e., it is not a marker).

   The result of the trigger calculation is that the dropping algorithm
   makes a decision on whether to forward or to discard a packet.  The
   discarding function is likely to keep counters regarding the
   discarded packets (there is no appropriate place here to include a
   Counter Action element).

   The example in Figure 5 also shows a FIFO Queue element from whose
   tail the dropping is to take place and whose depth characteristics
   are used by this Algorithmic Dropper.  It also shows where a depth-
   smoothing function might be included: smoothing functions are outside
   the scope of this document and are not modeled explicitly here, we
   merely indicate where they might be added.

   RED, RED-on-In-and-Out (RIO) and Drop-on-threshold are examples of
   dropping algorithms.  Tail-dropping and head-dropping are effected by
   the location of the Algorithmic Dropper element relative to the FIFO
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   Queue element.  As an example, a dropper using a RIO algorithm might
   be represented using 2 Algorithmic Droppers with the following
   parameters:

      AlgorithmicDropper1: (for in-profile traffic)
      Type:                   AlgorithmicDropper
      Discipline:             RED
      Trigger:                Internal
      Output:                 Fifo1
      MinThresh:              Fifo1.Depth > 20 kbyte
      MaxThresh:              Fifo1.Depth > 30 kbyte
      SampleWeight            .002
      MaxDropProb             1%

      AlgorithmicDropper2: (for out-of-profile traffic)
      Type:                   AlgorithmicDropper
      Discipline:             RED
      Trigger:                Internal
      Output:                 Fifo1
      MinThresh:              Fifo1.Depth > 10 kbyte
      MaxThresh:              Fifo1.Depth > 20 kbyte
      SampleWeight            .002
      MaxDropProb             2%

   Another form of Algorithmic Dropper, a threshold-dropper, might be
   represented using the following parameters:

      AlgorithmicDropper3:
      Type:                   AlgorithmicDropper
      Discipline:             Drop-on-threshold
      Trigger:                Fifo2.Depth > 20 kbyte
      Output:                 Fifo1

7.2. Sharing load among traffic streams using queuing

Queues are used, in Differentiated Services, for a number of purposes. In essence, they are simply places to store traffic until it is transmitted. However, when several queues are used together in a queuing system, they can also achieve effects beyond that for given traffic streams. They can be used to limit variation in delay or impose a maximum rate (shaping), to permit several streams to share a link in a semi-predictable fashion (load sharing), or to move variation in delay from some streams to other streams. Traffic shaping is often used to condition traffic, such that packets arriving in a burst will be "smoothed" and deemed conforming by subsequent downstream meters in this or other nodes. In [DSARCH] a shaper is described as a queuing element controlled by a meter which
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   defines its temporal profile.  However, this representation of a
   shaper differs substantially from typical shaper implementations.

   In the model described here, a shaper is realized by using a non-
   work-conserving Scheduler.  Some implementations may elect to have
   queues whose sole purpose is shaping, while others may integrate the
   shaping function with other buffering, discarding, and scheduling
   associated with access to a resource.  Shapers operate by delaying
   the departure of packets that would be deemed non-conforming by a
   meter configured to the shaper's maximum service rate profile.  The
   packet is scheduled to depart no sooner than such time that it would
   become conforming.

7.2.1. Load Sharing

Load sharing is the traditional use of queues and was theoretically explored by Floyd & Jacobson [FJ95], although it has been in use in communications systems since the 1970's. [DSARCH] discusses load sharing as dividing an interface among traffic classes predictably, or applying a minimum rate to each of a set of traffic classes, which might be measured as an absolute lower bound on the rate a traffic stream achieves or a fraction of the rate an interface offers. It is generally implemented as some form of weighted queuing algorithm among a set of FIFO queues i.e., a WFQ scheme. This has interesting side-effects. A key effect sought is to ensure that the mean rate the traffic in a stream experiences is never lower than some threshold when there is at least that much traffic to send. When there is less traffic than this, the queue tends to be starved of traffic, meaning that the queuing system will not delay its traffic by very much. When there is significantly more traffic and the queue starts filling, packets in this class will be delayed significantly more than traffic in other classes that are under-using their available capacity. This form of queuing system therefore tends to move delay and variation in delay from under-used classes of traffic to heavier users, as well as managing the rates of the traffic streams. A side-effect of a WRR or WFQ implementation is that between any two packets in a given traffic class, the scheduler may emit one or more packets from each of the other classes in the queuing system. In cases where average behavior is in view, this is perfectly acceptable. In cases where traffic is very intolerant of jitter and there are a number of competing classes, this may have undesirable consequences.
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7.2.2. Traffic Priority

Traffic Prioritization is a special case of load sharing, wherein a certain traffic class is deemed so jitter-intolerant that if it has traffic present, that traffic must be sent at the earliest possible time. By extension, several priorities might be defined, such that traffic in each of several classes is given preferential service over any traffic of a lower class. It is the obvious implementation of IP Precedence as described in [RFC 791], of 802.1p traffic classes [802.1D], and other similar technologies. Priority is often abused in real networks; people tend to think that traffic which has a high business priority deserves this treatment and talk more about the business imperatives than the actual application requirements. This can have severe consequences; networks have been configured which placed business-critical traffic at a higher priority than routing-protocol traffic, resulting in collapse of the network's management or control systems. However, it may have a legitimate use for services based on an Expedited Forwarding (EF) PHB, where it is absolutely sure, thanks to policing at all possible traffic entry points, that a traffic stream does not abuse its rate and that the application is indeed jitter-intolerant enough to merit this type of handling. Note that, even in cases with well-policed ingress points, there is still the possibility of unexpected traffic loops within an un-policed core part of the network causing such collapse.


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