Network Working Group                                            W. Prue
Request for Comments:  1046                                    J. Postel
                                                                    ISI
                                                          February 1988


     A Queuing Algorithm to Provide Type-of-Service for IP Links

Status of this Memo

  This memo is intended to explore how Type-of-Service might be
  implemented in the Internet.  The proposal describes a method of
  queuing which can provide the different classes of service.  The
  technique also prohibits one class of service from consuming
  excessive resources or excluding other classes of service.  This is
  an "idea paper" and discussion is strongly encouraged.  Distribution
  of this memo is unlimited.

Introduction

  The Type-of-Service (TOS) field in IP headers allows one to chose
  from none to all the following service types; low delay, high
  throughput, and high reliability.  It also has a portion allowing a
  priority selection from 0-7.  To date, there is nothing describing
  what should be done with these parameters.  This discussion proposes
  an approach to providing the different classes of service and
  priorities requestable in the TOS field.

Desired Attributes

  We should first consider how we want these services to perform.  We
  must first assume that there is a demand for service that exceeds
  current capabilities.  If not, significant queues do not form and
  queuing algorithms become superfluous.

  The low delay class of service should have the ability to pass data
  through the net faster than regular data.  If a request is for low
  delay class of service only, not high throughput or high reliability,
  the Internet should provide low delay for relatively less throughput,
  with less than high reliability.  The requester is more concerned
  with promptness of delivery than guaranteed delivery.  The Internet
  should provide a Maximum Guaranteed Delay (MGD) per node, or better,
  if the datagram successfully traverses the Internet.  In the worst
  case, a datagram's arrival will be MGD times the number of nodes
  traversed.  A node is any packet switching element, including IP
  gateways and ARPANET IMP's.  The MGD bound will not be affected by
  the amount of traffic in the net.  During non-busy hours, the delay
  provided should be better than the guarantee.  If the delay a



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  satellite link introduces is less than the MGD, that link should be
  considered in the route.  If however, the MGD is less than the
  satellite link can provide, it should not be used.  For this
  discussion it is assumed that delay for individual links are low
  enough that a sending node can provide the MGD service.

  Low delay class of service is not the same as low Round Trip Time
  (RTT).  Class of service is unidirectional.  The datagrams responding
  to low delay traffic (i.e., Acking the data) might be sent with a
  high reliability class of service, but not low delay.

  The performance of TCP might be significantly improved with an
  accurate estimate of the round trip time and the retransmission
  timeout.  The TCP retransmission timeout could be set to the maximum
  delay for the current route (if the current route could be
  determined).  The timeout value would have to be redetermined when
  the number of hops in the route changes.

  High throughput class of service should get a large volume of data
  through the Internet.  Requesters of this class are less concerned
  with the delay the datagrams have crossing the Internet and the
  reliability of their delivery.  This type of traffic might be served
  well by a satellite link, especially if the bandwidth is high.
  Another attribute this class might have is consistent one way
  traversal time for a given burst of datagrams.  This class of service
  will have its traversal times affected by the amount of Internet
  load.  As the Internet load goes up, the throughput for each source
  will go down.

  High reliability class of service should see most of its datagrams
  delivered if the Internet is not too heavily loaded.  Source Quenches
  (SQ) should not be sent only when datagrams are discarded.  SQs
  should be sent well before the queues become full, to advise the
  sender of the rate that can be currently supported.

  Priority service should allow data that has a higher priority to be
  queued ahead of other lower priority data.  It is important to limit
  the amount of priority data.  The amount of preemption a lower
  priority datagram suffers must also be limited.

  It is assumed that a queuing algorithm provides these classes of
  service.  For one facility to be used over another, that is, making
  different routing decisions based upon the TOS, requires a more
  sophisticated routing algorithm and larger routing database.  These
  issues are not discussed in this document.






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Applications for Class of Service

  The following are examples of how classes of service might be used.
  They do not necessarily represent the best choices, but are presented
  only to illustrate how the different classes of service might be used
  to advantage.

  Interactive timesharing access using a line-at-a-time or character-
  at-a-time terminal (TTY) type of access is typically low volume
  typing speed input with low or high volume output.  Some Internet
  applications use echoplex or character by character echoing of user
  input by the destination host.  PC devices also have local files that
  may be uploaded to remote hosts in a streaming mode.  Supporting such
  traffic can require several types of service.  User keyboard input
  should be forwarded with low delay.  If echoplex is used, all user
  characters sent and echoed should be low delay to minimize the
  echoing delay.  The computer responses should be regular or high
  throughput depending upon the volume of data sent and the speed of
  the output device.  If the computer response is a single datagram of
  data, the user should get low delay for the response, to minimize the
  human/computer interaction time.  If however the output takes a while
  to read and digest, low delay computer responses are a waste of
  Internet resources.  When streaming input is being sent the data
  should be sent requesting high throughput or regular class of
  service.

  The IBM 3270 class of terminals typically have traffic volumes
  greater than TTY access.  Echoplex is not needed.  The output devices
  usually handle higher speed output streams and most sites do not have
  the ability to stream input.  Input is typically a screen at a time,
  but some PC implementations of 3270 use a variation of the protocol
  to effectively stream in volumes of data.  Low delay for low volume
  input and output is appropriate.  High throughput is appropriate for
  the higher volume traffic.

  Applications that transfer high volumes of data are typically
  streaming in one direction only, with acks for the data, on the
  return path.  The data transfer should be high throughput and the
  acks should probably be regular class of service.  Transfer
  initiation and termination might be served best with low delay class
  of service.

  Requests to, and responses from a time service might use low delay
  class of service effectively.

  These suggestions for class of service usage implies that the
  application sets the service based on the knowledge it has during the
  session.  Thus, the application should have control of this setting



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  dynamically for each send data request, not just on a per
  session/conversation/transaction basis.  It would be possible for the
  transport level protocol to guess (i.e., TCP), but it would be sub-
  optimal.

Algorithm

  When we provide class of service queuing, one class may be more
  desirable than the others.  We must limit the amount of resources
  each class consumes when there is contention, so the other classes
  may also operate effectively.  To be fair, the algorithm provides the
  requested service by reducing the other service attributes.  A
  request for multiple classes of service is an OR type of request not
  an AND request.  For example, one can not get low delay and high
  throughput unless there is no contention for the available resources.

Low Delay Queuing

  To support low delay, use a limited queue so requests will not wait
  longer than the MGD on the queue.  The low delay queue should be
  serviced at a lower rate than other classes of service, so low delay
  requests will not consume excessive resources.  If the number of low
  delay datagrams exceeds the queue limit, discard the datagrams.  The
  service rate should be low enough so that other data can still get
  through. (See discussion of service rates below.)  Make the queue
  limit small enough so that, if the datagram is queued, it will have a
  guaranteed transit time (MGD).  It seems unlikely that Source Quench
  flow control mechanisms will be an effective method of flow control
  because of the small size of the queue.  It should not be done for
  this class of service.  Instead, datagrams should just be discarded
  as required.  If the bandwidth or percentage allocated to low delay
  is such that a large queue is possible (see formula below), SQs
  should be reconsidered.

  The maximum delay a datagram with low delay class of service will
  experience (MGD), can be determined with the following information:

     N = Queue size for low delay queue
     P = Percentage of link resources allocated to low delay
     R = Link rate (in datagrams/sec.)
                     N
     Max Delay =   -----
                   P * R

  If Max Delay is held fixed, then as P and R go up, so does N.  It is
  probable that low delay service datagrams will prove to be, on the
  average, smaller than other traffic.  This means that the number of
  datagrams that can be sent in the allocated bandwidth can be larger.



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High Reliability Queuing

  To support high reliability class of service, use a queue that is
  longer than normal (longer queue means higher potential delay).  Send
  SQ earlier (smaller percentage of max queue length) and don't discard
  datagrams until the queue is full.  This queue should have a lower
  service rate than high throughput class of service.

  Users of this class of service should specify a Time-to-Live (TTL)
  which is made appropriately longer so that it will survive longer
  queueing times for this class of service.

  This queuing procedure will only be effective for Internet
  unreliability due to congestion.  Other Internet unreliability
  problems such as high error rate links or reliability features such
  as forward error correcting modems must be dealt with by more
  sophisticated routing algorithms.

High Throughput Queuing

  To support high throughput class of service have a queue that is
  treated like current IP queuing.  It should have the highest service
  rate.  It will experience higher average through node delay than low
  delay because of the larger queue size.

  Another thing that might be done, is to keep datagrams of the same
  burst together when possible.  This must be done in a way that will
  not block other traffic.  The idea is to deliver all the data to the
  other end in a contiguous burst.  This could be an advantage by
  allowing piggybacking acks for the whole burst at one time.  This
  makes some assumptions about the overlying protocol which may be
  inappropriate.

Regular Service Queuing

  For datagrams which request none of the three classes of service,
  queue the datagrams on the queue representing the least delay between
  the two queues, the high throughput queue or the high reliability
  queue.  If one queue becomes full, queue on the other.  If both
  queues are full, follow the source quench procedure for regular class
  of service (see RFC-1016), not the procedure for the queue the
  datagram failed to attain.

  In the discussion of service rates described below, it is proposed
  that the high throughput queue get service three times for every two
  times for the high reliability queue.  Therefore, the queue length of
  the high reliability queue should be increased by 50% (in this
  example) to compare the lengths of the two queues more accurately.  A



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  simplification to this method is to just queue new data on the queue
  that is the shortest.  The slower service rate queue will quickly
  exceed the size of the faster service rate queue and new data will go
  on the proper queue.  This however, would lead to more packet
  reordering than the first method.


Service Rates

  In this discussion, a higher service rate means that a queue, when
  non-empty, will consume a larger percentage of the available
  bandwidth than a lower service rate queue.  It will not block a lower
  service rate queue even if it is always full.

  For example, the service pattern could be; send low delay 17% of the
  time, high throughput 50% of the time, and high reliability 33% of
  the time.  Throughput requires the most bandwidth and high
  reliability requires medium bandwidth.  One could achieve this split
  using a pattern of L, R,R, T,T,T, where low delay is "L", high
  reliability is "R", and high throughput is "T'.  We want to keep the
  high throughput datagrams together.  We therefore send all of the
  high throughput data at one time, that is, not interspersed with the
  other classes of service.  By keeping all of the high throughput data
  together, we may help higher level protocols, such as TCP, as
  described above.  This would still be done in a way to not exceed the
  allowed service rate of the available bandwidth.

  These service rates are suggestions.  Some simplifications can be
  considered, such as having only two routing classes; low delay, and
  other.

Priority

  There is the ability to select 8 levels of priority 0-7, in addition
  to the class of service selected.  To provide this without blocking
  the least priority requests, we must give preempted datagrams
  frustration points every time a higher priority request cuts in line
  in front of it.  Thus if a datagram with low priority waits, it will
  always get through even when competing against the highest priority
  requests.  This assumes the TTL (Time-to-Live) field does not expire.

  When a datagram with priority arrives at a node, the node will queue
  the datagram on the appropriate queue ahead of all datagrams with
  lower priority.  Each datagram that was preempted gets its priority
  raised (locally).  The priority data will not bump a lower priority
  datagram off its queue, discarding the data.  If the queue is full,
  the newest data (priority or not) will be discarded.  The priority
  preemption will preempt only within the class of service queue to



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  which the priority data is targeted.  A request specifying regular
  class of service, will contend on the queue where it is placed, high
  throughput or high reliability.

  An implementation strategy is to multiply the requested priority by 2
  or 4, then store the value in a buffer overhead area.  Each time the
  datagram is preempted, increment the value by one.  Looking at an
  example, assume we use a multiplier of 2.  A priority 6 buffer will
  have an initial local value of 12.  A new priority 7 datagram would
  have a local value of 14.  If 2 priority 7 datagrams arrive,
  preempting the priority 6 datagram, its local value is incremented to
  14.  It can no longer be preempted.  After that, it has the same
  local value as a priority 7 datagram and will no longer be preempted
  within this node.  In our example, this means that a priority 0
  datagram can be preempted by no more than 14 higher priority
  datagrams.  The priority is raised only locally in the node.  The
  datagram could again be preempted in the next node on the route.

  Priority queuing changes the effects we were obtaining with the low
  delay queuing described above.  Once a buffer was queued, the delay
  that a datagram would see could be determined.  When we accepted low
  delay data, we could guarantee a certain maximum delay.  With this
  addition, if the datagram requesting low delay does not also request
  high priority, the guaranteed delay can vary a lot more.  It could be
  1 up to 28 times as much as without priority queuing.

Discussion and Details

  If a low delay queue is for a satellite link (or any high delay
  link), the max queue size should be reduced by the number of
  datagrams that can be forwarded from the queue during the one way
  delay for the link.  That is, if the service rate for the low delay
  queue is L datagrams per second, the delay added by the high delay
  link is D seconds and M is the max delay per node allowed (MGD) in
  seconds, then the maximum queue size should be:

        Max Queue Size = L ( M - D),  M > D
                       = 0         ,  M <= D

  If the result is negative (M is less than the delay introduced by the
  link), then the maximum queue size should be zero because the link
  could never provide a delay less than the guaranteed M value.  If the
  bandwidth is high (as in T1 links), the delay introduced by a
  terrestrial link and the terminating equipment could be significant
  and greater than the average service time for a single datagram on
  the low delay queue.  If so, this formula should be used to reduce
  the queue size as well.  Note that this is reducing the queue size
  and is not the same as the allocated bandwidth.  Even though the



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  queue size is reduced, the chit scheme described below will give low
  delay requesters a chance to use the allocated bandwidth.

  If a datagram requests multiple classes of service, only one class
  can be provided.  For example, when both low delay and high
  reliability classes are requested, and if the low delay queue is
  full, queue the data on the high reliability queue instead.  If we
  are able to queue the data on the low delay queue, then the datagram
  gets part of the high reliability service it also requested, because,
  once data is queued, data will not be discarded.  However, the
  datagram will be routed as a low delay request.  The same scheme is
  used for any other combinations of service requested.  The order of
  selection for classes of service when more than one is requested
  would be low delay, high throughput, then high reliability.  If a
  block of datagrams request multiple classes of service, it is quite
  possible that datagram reordering will occur.  If one queue is full
  causing the other queue to be used for some of the data, data will be
  forwarded at different service rates.  Requesting multiple classes of
  service gives the data a better chance of making it through the net
  because they have multiple chances of getting on a service queue.
  However, the datagrams pay the penalty of possible reordering and
  more variability in the one way transmission times.

  Besides total buffer consumption, individual class of service queue
  sizes should be used to SQ those asking for service except as noted
  above.

  A request for regular class of service is handled by queuing to the
  high reliability or high throughput queues evenly (proportional to
  the service rates of queue).  The low delay queue should only receive
  data with the low delay service type.  Its queue is too small to
  accept other traffic.

  Because of the small queue size for low delay suggested above, it is
  difficult for low delay service requests to consume the bandwidth
  allocated.  To do so, low delay users must keep the small queue
  continuously non-empty.  This is hard to do with a small queue.
  Traffic flow has been shown to be bursty in nature.  In order for the
  low delay queue to be able to consume the allocated bandwidth, a
  count of the various types being forwarded should be kept.  The
  service rate should increase if the actual percentage falls too low
  for the low delay queue.  The measure of service rates would have to
  be smoothed over time.

  While this does sound complicated, a reasonably efficient way can be
  described.  Every Q seconds, where Q is less than or equal to the
  MGD, each class gets N M P chits proportional to their allowed
  percentage.  Send data for the low delay queue up to the number of



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  chits it receives decrementing the chits as datagrams are sent.  Next
  send from the high reliability queue as many as it has chits for.
  Finally, send from the high throughput queue.  At this point, each
  queue gets N M P chits again.  If the low delay queue does not
  consume all of its chits, when a low delay datagram arrives, before
  chit replenishment, send from the low delay queue immediately.  This
  provides some smoothing of the actual bandwidth made available for
  low delay traffic.  If operational experience shows that low delay
  requests are experiencing excessive congestion loss but still not
  consuming the classes allocated bandwidth, adjustments should be
  made.  The service rates should be made larger and the queue sizes
  adjusted accordingly.  This is more important on lower speed links
  where the above formula makes the queue small.

  What we should see during the Q seconds is that low delay data will
  be sent as soon as possible (as long as the volume is below the
  allowed percentage).  Also, the tendency will be to send all the high
  throughput datagrams contiguously.  This will give a more regular
  measured round trip time for bursts of datagrams.  Classes of service
  will tend to be grouped together at each intermediate node in the
  route.  If all of the queues with datagrams have consumed all of
  their allocated chits, but one or more classes with empty queues have
  unused chits then a percentage of these left over chits should be
  carried over.  Divide the remaining chit counts by two (with round
  down), then add in the refresh chit counts.  This allows a 50% carry
  over for the next interval.  The carry over is self limiting to less
  than or equal to the refresh chit count.  This prevents excessive
  build up.  It provides some smoothing of the percentage allocation
  over time but will not allow an unused queue to build up chits
  indefinitely.  No timer is required.

  If only a simple subset of the described algorithm is to be
  implemented, then low delay queuing would be the best choice.  One
  should use a small queue.  Service the queue with a high service rate
  but restrict the bandwidth to a small reasonable percentage of the
  available bandwidth.  Currently, wide area networks with high traffic
  volumes do not provide low delay service unless low delay requests
  are able to preempt other traffic.

Applicability

  When the output speed and volume match the input speed and volume,
  queues don't get large.  If the queues never grow large enough to
  exceed the guaranteed low delay performance, no queuing algorithm
  other than first in, first out, should be used.

  The algorithm could be turned on when the main queue size exceeds a
  certain threshold.  The routing node can periodically check for queue



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  build up.  This queuing algorithm can be turned on when the maximum
  delays will exceed the allowed nodal delay for low delay class of
  service.  It can also be turned off when queue sizes are no longer a
  problem.

Issues

  Several issues need to be addressed before type of service queuing as
  described should be implemented.  What percentage of the bandwidth
  should each class of service consume assuming an infinite supply of
  each class of service datagrams?  What maximum delay (MGD) should be
  guaranteed per node for low delay datagrams?

  It is possible to provide a more optimal route if the queue sizes for
  each class of service are considered in the routing decision.  This,
  however, adds additional overhead and complexity to each routing
  node.  This may be an unacceptable additional complexity.

  How are we going to limit the use of more desirable classes of
  service and higher priorities?  The algorithm limits use of the
  various classes by restricting queue sizes especially the low delay
  queue size.  This helps but it seems likely we will want to
  instrument the number of datagrams requesting each Type-of-Service
  and priority.  When a datagram requests multiple classes of service,
  increment the instrumentation count once based upon the queue
  actually used, selecting, low delay, high throughput, high
  reliability, then regular.  If instrumentation reveals an excessive
  imbalance, Internet operations can give this to administrators to
  handle.  This instrumentation will show the distribution for types of
  service requested by the Internet users.  This information can be
  used to tune the Internet to service the user demands.

  Will the routing algorithms in use today have problems when routing
  data with this algorithm?  Simulation tests need to be done to model
  how the Internet will react.  If, for example, an application
  requests multiple classes of service, round trip times may fluctuate
  significantly.  Would TCP have to be more sophisticated in its round
  trip time estimator?

  An objection to this type of queuing algorithm is that it is making
  the routing and queuing more complicated.  There is current interest
  in high speed packet switches which have very little protocol
  overhead when handling/routing packets.  This algorithm complicates
  not simplifies the protocol.  The bandwidth being made available is
  increasing.  More T1 (1.5 Mbps) and higher speed links are being used
  all the time.  However, in the history of communications, it seems
  that the demand for bandwidth has always exceeded the supply.  When
  there is wide spread use of optical fiber we may temporarily



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  experience a glut of capacity.  As soon as 1 gigabit optical fiber
  link becomes reasonably priced, new applications will be created to
  consume it all.  A single full motion high resolution color image
  system can consume, as an upper limit, nearly a gigabit per second
  channel (30 fps X 24 b/pixel X 1024 X 1024 pixels).

  In the study of one gateway, Dave Clark discovered that the per
  datagram processing of the IP header constituted about 20% of the
  processing time.  Much of the time per datagram was spent on
  restarting input, starting output and queuing datagrams.  He thought
  that a small additional amount of processing to support Type-of-
  Service would be reasonable.  He suggests that even if the code does
  slow the gateway down, we need to see if TOS is good for anything, so
  this experiment is valuable.  To support the new high speed
  communications of the near future, Dave wants to see switches which
  will run one to two orders of magnitude faster.  This can not be done
  by trimming a few instructions here or there.

  From a practical perspective, the problem this algorithm is trying to
  solve is the lack of low delay service through the Internet today.
  Implementing only the low delay queuing portion of this algorithm
  would allow the Internet to provide a class of service it otherwise
  could not provide.  Requesters of this class of service would not get
  it for free.  Low delay class of datagram streams get low delay at
  the cost of reliability and throughput.


























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