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Versions: (draft-hilt-soc-overload-design) 00 01 02 03 04 05 06 07 08 RFC 6357

SOC Working Group                                                V. Hilt
Internet-Draft                                  Bell Labs/Alcatel-Lucent
Intended status: Informational                                   E. Noel
Expires: September 12, 2011                                    AT&T Labs
                                                                 C. Shen
                                                     Columbia University
                                                              A. Abdelal
                                                          Sonus Networks
                                                          March 11, 2011

  Design Considerations for Session Initiation Protocol (SIP) Overload


   Overload occurs in Session Initiation Protocol (SIP) networks when
   SIP servers have insufficient resources to handle all SIP messages
   they receive.  Even though the SIP protocol provides a limited
   overload control mechanism through its 503 (Service Unavailable)
   response code, SIP servers are still vulnerable to overload.  This
   document discusses models and design considerations for a SIP
   overload control mechanism.

Status of this Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

   Internet-Drafts are working documents of the Internet Engineering
   Task Force (IETF).  Note that other groups may also distribute
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   This Internet-Draft will expire on September 12, 2011.

Copyright Notice

   Copyright (c) 2011 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal

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   Provisions Relating to IETF Documents
   (http://trustee.ietf.org/license-info) in effect on the date of
   publication of this document.  Please review these documents
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Table of Contents

   1.  Introduction . . . . . . . . . . . . . . . . . . . . . . . . .  3
   2.  SIP Overload Problem . . . . . . . . . . . . . . . . . . . . .  4
   3.  Explicit vs. Implicit Overload Control . . . . . . . . . . . .  5
   4.  System Model . . . . . . . . . . . . . . . . . . . . . . . . .  6
   5.  Degree of Cooperation  . . . . . . . . . . . . . . . . . . . .  8
     5.1.  Hop-by-Hop . . . . . . . . . . . . . . . . . . . . . . . .  9
     5.2.  End-to-End . . . . . . . . . . . . . . . . . . . . . . . . 10
     5.3.  Local Overload Control . . . . . . . . . . . . . . . . . . 11
   6.  Topologies . . . . . . . . . . . . . . . . . . . . . . . . . . 11
   7.  Fairness . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
   8.  Performance Metrics  . . . . . . . . . . . . . . . . . . . . . 14
   9.  Explicit Overload Control Feedback . . . . . . . . . . . . . . 15
     9.1.  Rate-based Overload Control  . . . . . . . . . . . . . . . 15
     9.2.  Loss-based Overload Control  . . . . . . . . . . . . . . . 17
     9.3.  Window-based Overload Control  . . . . . . . . . . . . . . 18
     9.4.  Overload Signal-based Overload Control . . . . . . . . . . 19
     9.5.  On-/Off Overload Control . . . . . . . . . . . . . . . . . 19
   10. Implicit Overload Control  . . . . . . . . . . . . . . . . . . 20
   11. Overload Control Algorithms  . . . . . . . . . . . . . . . . . 20
   12. Message Prioritization . . . . . . . . . . . . . . . . . . . . 20
   13. Security Considerations  . . . . . . . . . . . . . . . . . . . 21
   14. IANA Considerations  . . . . . . . . . . . . . . . . . . . . . 21
   15. Informative References . . . . . . . . . . . . . . . . . . . . 21
   Appendix A.  Contributors  . . . . . . . . . . . . . . . . . . . . 22
   Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 23

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1.  Introduction

   As with any network element, a Session Initiation Protocol (SIP)
   [RFC3261] server can suffer from overload when the number of SIP
   messages it receives exceeds the number of messages it can process.
   Overload occurs if a SIP server does not have sufficient resources to
   process all incoming SIP messages.  These resources may include CPU,
   memory, input/output, or disk resources.

   Overload can pose a serious problem for a network of SIP servers.
   During periods of overload, the throughput of a network of SIP
   servers can be significantly degraded.  In fact, overload may lead to
   a situation in which the throughput drops down to a small fraction of
   the original processing capacity.  This is often called congestion

   An overload control mechanism enables a SIP server to perform close
   to its capacity limit during times of overload.  Overload control is
   used by a SIP server if it is unable to process all SIP requests due
   to resource constraints.  There are other failure cases in which a
   SIP server can successfully process incoming requests but has to
   reject them for other reasons.  For example, a PSTN gateway that runs
   out of trunk lines but still has plenty of capacity to process SIP
   messages should reject incoming INVITEs using a response such as 488
   (Not Acceptable Here), as described in [RFC4412].  Similarly, a SIP
   registrar that has lost connectivity to its registration database but
   is still capable of processing SIP messages should reject REGISTER
   requests with a 500 (Server Error) response [RFC3261].  Overload
   control mechanisms do not apply in these cases and SIP provides
   appropriate response codes for them.

   There are cases in which a SIP server runs other services that do not
   involve the processing of SIP messages (e.g., processing of RTP
   packets, database queries, software updates and event handling).
   These services may, or may not, be correlated with the SIP message
   volume.  These services can use up a substantial share of resources
   available on the server (e.g., CPU cycles) and leave the server in a
   condition where it is unable to process all incoming SIP requests.
   In these cases, the SIP server applies SIP overload control
   mechanisms to avoid congestion collapse on the SIP signaling plane.
   However, controlling the number of SIP requests may not significantly
   reduce the load on the server if the resource shortage was created by
   another service.  In these cases, it is to be expected that the
   server uses appropriate methods of controlling the resource usage of
   other services.  The specifics of controlling the resource usage of
   other services and their coordination is out of scope for this

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   The SIP protocol provides a limited mechanism for overload control
   through its 503 (Service Unavailable) response code and the Retry-
   After header.  However, this mechanism cannot prevent overload of a
   SIP server and it cannot prevent congestion collapse.  In fact, it
   may cause traffic to oscillate and to shift between SIP servers and
   thereby worsen an overload condition.  A detailed discussion of the
   SIP overload problem, the problems with the 503 (Service Unavailable)
   response code and the Retry-After header and the requirements for a
   SIP overload control mechanism can be found in [RFC5390].  In
   addition, 503 is used for other situations, not just SIP Server
   overload.  A SIP Overload Control process based on 503 would have to
   specify exactly which cause values trigger the Overload Control.

   This document discusses the models, assumptions and design
   considerations for a SIP overload control mechanism.  The document
   originated in the SIP overload control design team and has been
   further developed by the SOC working group.

2.  SIP Overload Problem

   A key contributor to SIP congestion collapse [RFC5390] is the
   regenerative behavior of overload in the SIP protocol.  When SIP is
   running over the UDP protocol, it will retransmit messages that were
   dropped or excessively delayed by a SIP server due to overload and
   thereby increase the offered load for the already overloaded server.
   This increase in load worsens the severity of the overload condition
   and, in turn, causes more messages to be dropped.  A congestion
   collapse can occur [Hilt et al.], [Noel et al.], [Shen et al.] and
   [Abdelal et al.].

   Regenerative behavior under overload should ideally be avoided by any
   protocol as this would lead to unstable operation under overload.
   However, this is often difficult to achieve in practice.  For
   example, changing the SIP retransmission timer mechanisms can reduce
   the degree of regeneration during overload but will impact the
   ability of SIP to recover from message losses.  Without any
   retransmission each message that is dropped due to SIP server
   overload will eventually lead to a failed transaction.  In case of an
   INVITE transaction, the result will be a failed call.

   For a SIP INVITE transaction to be successful, a minimum of three
   messages need to be forwarded by a SIP server.  Often an INVITE
   transaction consists of five or more SIP messages.  If a SIP server
   under overload randomly discards messages without evaluating them,
   the chances that all messages belonging to a transaction are
   successfully forwarded will decrease as the load increases.  Thus,
   the number of transactions that complete successfully will decrease

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   even if the message throughput of a server remains up and assuming
   the overload behavior is fully non-regenerative.  A SIP server might
   (partially) parse incoming messages to determine if it is a new
   request or a message belonging to an existing transaction.
   Discarding a SIP message after spending the resources to parse it is
   expensive.  The number of successful transactions will therefore
   decline with an increase in load as fewer resources can be spent on
   forwarding messages and more resources are consumed by inspecting
   messages that will eventually be dropped.  The rate of the decline
   depends on the amount of resources spent to inspect each message.

   Another challenge for SIP overload control is controlling the rate of
   the true traffic source.  Overload is often caused by a large number
   of UAs each of which creates only a single message.  However, the sum
   of their traffic can overload a SIP server.  The overload mechanisms
   suitable for controlling a SIP server (e.g., rate control) may not be
   effective for individual UAs.  In some cases, there are other non-SIP
   mechanisms for limiting the load from the UAs.  These may operate
   independently from, or in conjunction with, the SIP overload
   mechanisms described here.  In either case, they are out of scope for
   this document.

3.  Explicit vs. Implicit Overload Control

   The main differences between explicit and implicit overload control
   is the way overload is signaled from a SIP server that is reaching
   overload condition to its upstream neighbors.

   In an explicit overload control mechanism, a SIP server uses an
   explicit overload signal to indicate that it is reaching its capacity
   limit.  Upstream neighbors receiving this signal can adjust their
   transmission rate according to the overload signal to a level that is
   acceptable to the downstream server.  The overload signal enables a
   SIP server to steer the load it is receiving to a rate at which it
   can perform at maximum capacity.

   Implicit overload control uses the absence of responses and packet
   loss as an indication of overload.  A SIP server that is sensing such
   a condition reduces the load it is forwarding a downstream neighbor.
   Since there is no explicit overload signal, this mechanism is robust
   as it does not depend on actions taken by the SIP server running into

   The ideas of explicit and implicit overload control are in fact
   complementary.  By considering implicit overload indications a server
   can avoid overloading an unresponsive downstream neighbor.  An
   explicit overload signal enables a SIP server to actively steer the

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   incoming load to a desired level.

4.  System Model

   The model shown in Figure 1 identifies fundamental components of an
   explicit SIP overload control mechanism:

   SIP Processor:  The SIP Processor processes SIP messages and is the
      component that is protected by overload control.
   Monitor:  The Monitor measures the current load of the SIP processor
      on the receiving entity.  It implements the mechanisms needed to
      determine the current usage of resources relevant for the SIP
      processor and reports load samples (S) to the Control Function.
   Control Function:  The Control Function implements the overload
      control algorithm.  The control function uses the load samples (S)
      and determines if overload has occurred and a throttle (T) needs
      to be set to adjust the load sent to the SIP processor on the
      receiving entity.  The control function on the receiving entity
      sends load feedback (F) to the sending entity.
   Actuator:  The Actuator implements the algorithms needed to act on
      the throttles (T) and ensures that the amount of traffic forwarded
      to the receiving entity meets the criteria of the throttle.  For
      example, a throttle may instruct the Actuator to not forward more
      than 100 INVITE messages per second.  The Actuator implements the
      algorithms to achieve this objective, e.g., using message gapping.
      It also implements algorithms to select the messages that will be
      affected and determine whether they are rejected or redirected.

   The type of feedback (F) conveyed from the receiving to the sending
   entity depends on the overload control method used (i.e., loss-based,
   rate-based, window-based or signal-based overload control; see
   Section 9), the overload control algorithm (see Section 11) as well
   as other design parameters.  The feedback (F) enables the sending
   entity to adjust the amount of traffic forwarded to the receiving
   entity to a level that is acceptable to the receiving entity without
   causing overload.

   Figure 1 depicts a general system model for overload control.  In
   this diagram, one instance of the control function is on the sending
   entity (i.e., associated with the actuator) and one is on the
   receiving entity (i.e., associated with the monitor).  However, a
   specific mechanism may not require both elements.  In this case, one
   of two control function elements can be empty and simply passes along
   feedback.  E.g., if (F) is defined as a loss-rate (e.g., reduce
   traffic by 10%) there is no need for a control function on the
   sending entity as the content of (F) can be copied directly into (T).

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   The model in Figure 1 shows a scenario with one sending and one
   receiving entity.  In a more realistic scenario a receiving entity
   will receive traffic from multiple sending entities and vice versa
   (see Section 6).  The feedback generated by a Monitor will therefore
   often be distributed across multiple Actuators.  A Monitor needs to
   be able to split the load it can process across multiple sending
   entities and generate feedback that correctly adjusts the load each
   sending entity is allowed to send.  Similarly, an Actuator needs to
   be prepared to receive different levels of feedback from different
   receiving entities and throttle traffic to these entities

   In a realistic deployment, SIP messages will flow in both directions,
   from server B to server A as well as server A to server B. The
   overload control mechanisms in each direction can be considered
   independently.  For messages flowing from server A to server B, the
   sending entity is server A and the receiving entity is server B and
   vice versa.  The control loops in both directions operate

          Sending                Receiving
           Entity                  Entity
     +----------------+      +----------------+
     |    Server A    |      |    Server B    |
     |  +----------+  |      |  +----------+  |    -+
     |  | Control  |  |  F   |  | Control  |  |     |
     |  | Function |<-+------+--| Function |  |     |
     |  +----------+  |      |  +----------+  |     |
     |     T |        |      |       ^        |     | Overload
     |       v        |      |       | S      |     | Control
     |  +----------+  |      |  +----------+  |     |
     |  | Actuator |  |      |  | Monitor  |  |     |
     |  +----------+  |      |  +----------+  |     |
     |       |        |      |       ^        |    -+
     |       v        |      |       |        |    -+
     |  +----------+  |      |  +----------+  |     |
   <-+--|   SIP    |  |      |  |   SIP    |  |     |  SIP
   --+->|Processor |--+------+->|Processor |--+->   | System
     |  +----------+  |      |  +----------+  |     |
     +----------------+      +----------------+    -+

           Figure 1: System Model for Explicit Overload Control

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5.  Degree of Cooperation

   A SIP request is usually processed by more than one SIP server on its
   path to the destination.  Thus, a design choice for an explicit
   overload control mechanism is where to place the components of
   overload control along the path of a request and, in particular,
   where to place the Monitor and Actuator.  This design choice
   determines the degree of cooperation between the SIP servers on the
   path.  Overload control can be implemented hop-by-hop with the
   Monitor on one server and the Actuator on its direct upstream
   neighbor.  Overload control can be implemented end-to-end with
   Monitors on all SIP servers along the path of a request and an
   Actuator on the sender.  In this case, the Control Functions
   associated with each Monitor have to cooperate to jointly determine
   the overall feedback for this path.  Finally, overload control can be
   implemented locally on a SIP server if Monitor and Actuator reside on
   the same server.  In this case, the sending entity and receiving
   entity are the same SIP server and Actuator and Monitor operate on
   the same SIP processor (although, the Actuator typically operates on
   a pre-processing stage in local overload control).  Local overload
   control is an internal overload control mechanism as the control loop
   is implemented internally on one server.  Hop-by-hop and end-to-end
   are external overload control mechanisms.  All three configurations
   are shown in Figure 2.

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               +---------+             +------(+)---------+
      +------+ |         |             |       ^          |
      |      | |        +---+          |       |         +---+
      v      | v    //=>| C |          v       |     //=>| C |
   +---+    +---+ //    +---+       +---+    +---+ //    +---+
   | A |===>| B |                   | A |===>| B |
   +---+    +---+ \\    +---+       +---+    +---+ \\    +---+
               ^    \\=>| D |          ^       |     \\=>| D |
               |        +---+          |       |         +---+
               |         |             |       v          |
               +---------+             +------(+)---------+

         (a) hop-by-hop                   (b) end-to-end

                         v |
    +-+      +-+        +---+
    v |      v |    //=>| C |
   +---+    +---+ //    +---+
   | A |===>| B |
   +---+    +---+ \\    +---+
                    \\=>| D |
                         ^ |

           (c) local

    ==> SIP request flow
    <-- Overload feedback loop

              Figure 2: Degree of Cooperation between Servers

5.1.  Hop-by-Hop

   The idea of hop-by-hop overload control is to instantiate a separate
   control loop between all neighboring SIP servers that directly
   exchange traffic.  I.e., the Actuator is located on the SIP server
   that is the direct upstream neighbor of the SIP server that has the
   corresponding Monitor.  Each control loop between two servers is
   completely independent of the control loop between other servers
   further up- or downstream.  In the example in Figure 2(a), three
   independent overload control loops are instantiated: A - B, B - C and
   B - D. Each loop only controls a single hop.  Overload feedback
   received from a downstream neighbor is not forwarded further
   upstream.  Instead, a SIP server acts on this feedback, for example,
   by rejecting SIP messages if needed.  If the upstream neighbor of a

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   server also becomes overloaded, it will report this problem to its
   upstream neighbors, which again take action based on the reported
   feedback.  Thus, in hop-by-hop overload control, overload is always
   resolved by the direct upstream neighbors of the overloaded server
   without the need to involve entities that are located multiple SIP
   hops away.

   Hop-by-hop overload control reduces the impact of overload on a SIP
   network and can avoid congestion collapse.  It is simple and scales
   well to networks with many SIP entities.  An advantage is that it
   does not require feedback to be transmitted across multiple-hops,
   possibly crossing multiple trust domains.  Feedback is sent to the
   next hop only.  Furthermore, it does not require a SIP entity to
   aggregate a large number of overload status values or keep track of
   the overload status of SIP servers it is not communicating with.

5.2.  End-to-End

   End-to-end overload control implements an overload control loop along
   the entire path of a SIP request, from UAC to UAS.  An end-to-end
   overload control mechanism consolidates overload information from all
   SIP servers on the way (including all proxies and the UAS) and uses
   this information to throttle traffic as far upstream as possible.  An
   end-to-end overload control mechanism has to be able to frequently
   collect the overload status of all servers on the potential path(s)
   to a destination and combine this data into meaningful overload

   A UA or SIP server only throttles requests if it knows that these
   requests will eventually be forwarded to an overloaded server.  For
   example, if D is overloaded in Figure 2(b), A should only throttle
   requests it forwards to B when it knows that they will be forwarded
   to D. It should not throttle requests that will eventually be
   forwarded to C, since server C is not overloaded.  In many cases, it
   is difficult for A to determine which requests will be routed to C
   and D since this depends on the local routing decision made by B.
   These routing decisions can be highly variable and, for example,
   depend on call routing policies configured by the user, services
   invoked on a call, load balancing policies, etc.  The fact that a
   previous message to a target has been routed through an overloaded
   server does not necessarily mean the next message to this target will
   also be routed through the same server.

   The main problem of end-to-end overload control is its inherent
   complexity since UAC or SIP servers need to monitor all potential
   paths to a destination in order to determine which requests should be
   throttled and which requests may be sent.  Even if this information
   is available, it is not clear which path a specific request will

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   A variant of end-to-end overload control is to implement a control
   loop between a set of well-known SIP servers along the path of a SIP
   request.  For example, an overload control loop can be instantiated
   between a server that only has one downstream neighbor or a set of
   closely coupled SIP servers.  A control loop spanning multiple hops
   can be used if the sending entity has full knowledge about the SIP
   servers on the path of a SIP message.

   Overload control for SIP servers is different from end-to-end
   congestion control used by transport protocols such as TCP.  The
   traffic exchanged between SIP servers consists of many individual SIP
   messages.  Each SIP message is typically created individually by a
   SIP UA (e.g., to start setting up a call) and each message has its
   own source and destination address.  Even SIP messages containing
   identical SIP URIs (e.g., a SUBSCRIBE and a INVITE message to the
   same SIP URI) can be routed to different destinations.  This is
   different from TCP which controls a large flow of packets (e.g., to
   transmit a file) between a single source and a single destination.
   If congestion occurs in a router, the sources can detect this
   condition and adjust the rate at which new packets are transmitted.

5.3.  Local Overload Control

   The idea of local overload control (see Figure 2(c)) is to run the
   Monitor and Actuator on the same server.  This enables the server to
   monitor the current resource usage and to reject messages that can't
   be processed without overusing the local resources.  The fundamental
   assumption behind local overload control is that it is less resource
   consuming for a server to reject messages than to process them.  A
   server can therefore reject the excess messages it cannot process to
   stop all retransmissions of these messages.  Since rejecting messages
   does consume resources on a SIP server, local overload control alone
   cannot prevent a congestion collapse.

   Local overload control can be used in conjunction with an other
   overload control mechanisms and provides an additional layer of
   protection against overload.  It is fully implemented within a SIP
   server and does not require cooperation between servers.  In general,
   SIP servers should apply other overload control techniques to control
   load before a local overload control mechanism is activated as a
   mechanism of last resort.

6.  Topologies

   The following topologies describe four generic SIP server

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   configurations.  These topologies illustrate specific challenges for
   an overload control mechanism.  An actual SIP server topology is
   likely to consist of combinations of these generic scenarios.

   In the "load balancer" configuration shown in Figure 3(a) a set of
   SIP servers (D, E and F) receives traffic from a single source A. A
   load balancer is a typical example for such a configuration.  In this
   configuration, overload control needs to prevent server A (i.e., the
   load balancer) from sending too much traffic to any of its downstream
   neighbors D, E and F. If one of the downstream neighbors becomes
   overloaded, A can direct traffic to the servers that still have
   capacity.  If one of the servers serves as a backup, it can be
   activated once one of the primary servers reaches overload.

   If A can reliably determine that D, E and F are its only downstream
   neighbors and all of them are in overload, it may choose to report
   overload upstream on behalf of D, E and F. However, if the set of
   downstream neighbors is not fixed or only some of them are in
   overload then A should not activate an overload control since A can
   still forward the requests destined to non-overloaded downstream
   neighbors.  These requests would be throttled as well if A would use
   overload control towards its upstream neighbors.

   In some cases, the servers D, E, and F are in a server farm and
   configured to appear as a single server to their upstream neighbors.
   In this case, server A can report overload on behalf of the server
   farm.  If the load balancer is not a SIP entity, servers D, E, and F
   can report the overall load of the server farm (i.e., the load of the
   virtual server) in their messages.  As an alternative, one of the
   servers (e.g., server E) can report overload on behalf of the server
   farm.  In this case, not all messages contain overload control
   information and it needs to be ensured that all upstream neighbors
   are periodically served by server E to received updated information.

   In the "multiple sources" configuration shown in Figure 3(b), a SIP
   server D receives traffic from multiple upstream sources A, B and C.
   Each of these sources can contribute a different amount of traffic,
   which can vary over time.  The set of active upstream neighbors of D
   can change as servers may become inactive and previously inactive
   servers may start contributing traffic to D.

   If D becomes overloaded, it needs to generate feedback to reduce the
   amount of traffic it receives from its upstream neighbors.  D needs
   to decide by how much each upstream neighbor should reduce traffic.
   This decision can require the consideration of the amount of traffic
   sent by each upstream neighbor and it may need to be re-adjusted as
   the traffic contributed by each upstream neighbor varies over time.
   Server D can use a local fairness policy to determine how much

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   traffic it accepts from each upstream neighbor.

   In many configurations, SIP servers form a "mesh" as shown in
   Figure 3(c).  Here, multiple upstream servers A, B and C forward
   traffic to multiple alternative servers D and E. This configuration
   is a combination of the "load balancer" and "multiple sources"

                   +---+              +---+
                /->| D |              | A |-\
               /   +---+              +---+  \
              /                               \   +---+
       +---+-/     +---+              +---+    \->|   |
       | A |------>| E |              | B |------>| D |
       +---+-\     +---+              +---+    /->|   |
              \                               /   +---+
               \   +---+              +---+  /
                \->| F |              | C |-/
                   +---+              +---+

       (a) load balancer             (b) multiple sources

       | A |---\                        a--\
       +---+-\  \---->+---+                 \
              \/----->| D |             b--\ \--->+---+
       +---+--/\  /-->+---+                 \---->|   |
       | B |    \/                      c-------->| D |
       +---+---\/\--->+---+                       |   |
               /\---->| E |            ...   /--->+---+
       +---+--/   /-->+---+                 /
       | C |-----/                      z--/

             (c) mesh                   (d) edge proxy

                           Figure 3: Topologies

   Overload control that is based on reducing the number of messages a
   sender is allowed to send is not suited for servers that receive
   requests from a very large population of senders, each of which only
   sends a very small number of requests.  This scenario is shown in
   Figure 3(d).  An edge proxy that is connected to many UAs is a
   typical example for such a configuration.  Since each UA typically
   only infrequently sends requests, which are often related to the same
   session, it can't decrease its message rate to resolve the overload.

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   A SIP server that receives traffic from many sources, which each
   contribute only a small number of requests can resort to local
   overload control by rejecting a percentage of the requests it
   receives with 503 (Service Unavailable) responses.  Since it has many
   upstream neighbors it can send 503 (Service Unavailable) to a
   fraction of them to gradually reduce load without entirely stopping
   all incoming traffic.  The Retry-After header can be used in 503
   (Service Unavailable) responses to ask upstream neighbors to wait a
   given number of seconds before trying the request again.  Using 503
   (Service Unavailable) can, however, not prevent overload if a large
   number of sources create requests (e.g., to place calls) at the same

      Note: The requirements of the "edge proxy" topology are different
      from the ones of the other topologies, which may require a
      different method for overload control.

7.  Fairness

   There are many different ways to define fairness between multiple
   upstream neighbors of a SIP server.  In the context of SIP server
   overload, it is helpful to describe two categories of fairness: basic
   fairness and customized fairness.  With basic fairness a SIP server
   treats all requests equally and ensures that each request has the
   same chance of succeeding.  With customized fairness, the server
   allocates resources according to different priorities.  An example
   application of the basic fairness criteria is the "Third caller
   receives free tickets" scenario, where each call attempt should have
   an equal success probability in making calls through an overloaded
   SIP server, irrespective of the service provider where it was
   initiated.  An example of customized fairness would be a server which
   assigns different resource allocations to its upstream neighbors
   (e.g., service providers) as defined in a service level agreement

8.  Performance Metrics

   The performance of an overload control mechanism can be measured
   using different metrics.

   A key performance indicator is the goodput of a SIP server under
   overload.  Ideally, a SIP server will be enabled to perform at its
   capacity limit during periods of overload.  E.g., if a SIP server has
   a processing capacity of 140 INVITE transactions per second then an
   overload control mechanism should enable it to process 140 INVITEs
   per second even if the offered load is much higher.  The delay

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   introduced by a SIP server is another important indicator.  An
   overload control mechanism should ensure that the delay encountered
   by a SIP message is not increased significantly during periods of
   overload.  Significantly increased delay can lead to time-outs, and
   retransmission of SIP messages, making the overload worse.

   Responsiveness and stability are other important performance
   indicators.  An overload control mechanism should quickly react to an
   overload occurrence and ensure that a SIP server does not become
   overloaded even during sudden peaks of load.  Similarly, an overload
   control mechanism should quickly stop rejecting requests if the
   overload disappears.  Stability is another important criteria.  An
   overload control mechanism should not cause significant oscillations
   of load on a SIP server.  The performance of SIP overload control
   mechanisms is discussed in [Noel et al.], [Shen et al.], [Hilt et
   al.] and [Abdelal et al.].

   In addition to the above metrics, there are other indicators that are
   relevant for the evaluation of an overload control mechanism:

   Fairness:  Which types of fairness does the overload control
      mechanism implement?
   Self-limiting:  Is the overload control self-limiting if a SIP server
      becomes unresponsive?
   Changes in neighbor set:  How does the mechanism adapt to a changing
      set of sending entities?
   Data points to monitor:  Which and how many data points does an
      overload control mechanism need to monitor?
   Computational load:  What is the (cpu) load created by the overload
      "monitor" and "actuator"

9.  Explicit Overload Control Feedback

   Explicit overload control feedback enables a receiver to indicate how
   much traffic it wants to receive.  Explicit overload control
   mechanisms can be differentiated based on the type of information
   conveyed in the overload control feedback and whether the control
   function is in the receiving or sending entity (receiver- vs. sender-
   based overload control), or both.

9.1.  Rate-based Overload Control

   The key idea of rate-based overload control is to limit the request
   rate at which an upstream element is allowed to forward traffic to
   the downstream neighbor.  If overload occurs, a SIP server instructs
   each upstream neighbor to send at most X requests per second.  Each
   upstream neighbor can be assigned a different rate cap.

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   An example algorithm for an Actuator in the sending entity is request
   gapping.  After transmitting a request to a downstream neighbor, a
   server waits for 1/X seconds before it transmits the next request to
   the same neighbor.  Requests that arrive during the waiting period
   are not forwarded and are either redirected, rejected or buffered.
   Request gapping only affects requests that are targeted by overload
   control (e.g., requests that initiate a transaction and not
   retransmissions in an ongoing transaction).

   The rate cap ensures that the number of requests received by a SIP
   server never increases beyond the sum of all rate caps granted to
   upstream neighbors.  Rate-based overload control protects a SIP
   server against overload even during load spikes assuming there are no
   new upstream neighbors that start sending traffic.  New upstream
   neighbors need to be considered in the rate caps assigned to all
   upstream neighbors.  The rate assigned to upstream neighbors needs to
   be adjusted when new neighbors join.  During periods when new
   neighbors are joining, overload can occur in extreme cases until the
   rate caps of all servers are adjusted to again match the overall rate
   cap of the server.  The overall rate cap of a SIP server is
   determined by an overload control algorithm, e.g., based on system

   Rate-based overload control requires a SIP server to assign a rate
   cap to each of its upstream neighbors while it is activated.
   Effectively, a server needs to assign a share of its overall capacity
   to each upstream neighbor.  A server needs to ensure that the sum of
   all rate caps assigned to upstream neighbors does not substantially
   oversubscribe its actual processing capacity.  This requires a SIP
   server to keep track of the set of upstream neighbors and to adjust
   the rate cap if a new upstream neighbor appears or an existing
   neighbor stops transmitting.  For example, if the capacity of the
   server is X and this server is receiving traffic from two upstream
   neighbors, it can assign a rate of X/2 to each of them.  If a third
   sender appears, the rate for each sender is lowered to X/3.  If the
   overall rate cap is too high, a server may experience overload.  If
   the cap is too low, the upstream neighbors will reject requests even
   though they could be processed by the server.

   An approach for estimating a rate cap for each upstream neighbor is
   using a fixed proportion of a control variable, X, where X is
   initially equal to the capacity of the SIP server.  The server then
   increases or decreases X until the workload arrival rate matches the
   actual server capacity.  Usually, this will mean that the sum of the
   rate caps sent out by the server (=X) exceeds its actual capacity,
   but enables upstream neighbors who are not generating more than their
   fair share of the work to be effectively unrestricted.  In this
   approach, the server only has to measure the aggregate arrival rate.

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   However, since the overall rate cap is usually higher than the actual
   capacity, brief periods of overload may occur.

9.2.  Loss-based Overload Control

   A loss percentage enables a SIP server to ask an upstream neighbor to
   reduce the number of requests it would normally forward to this
   server by X%.  For example, a SIP server can ask an upstream neighbor
   to reduce the number of requests this neighbor would normally send by
   10%.  The upstream neighbor then redirects or rejects 10% of the
   traffic that is destined for this server.

   An algorithm for the sending entity to implement a loss percentage is
   to draw a random number between 1 and 100 for each request to be
   forwarded.  The request is not forwarded to the server if the random
   number is less than or equal to X.

   An advantage of loss-based overload control is that, the receiving
   entity does not need to track the set of upstream neighbors or the
   request rate it receives from each upstream neighbor.  It is
   sufficient to monitor the overall system utilization.  To reduce
   load, a server can ask its upstream neighbors to lower the traffic
   forwarded by a certain percentage.  The server calculates this
   percentage by combining the loss percentage that is currently in use
   (i.e., the loss percentage the upstream neighbors are currently using
   when forwarding traffic), the current system utilization and the
   desired system utilization.  For example, if the server load
   approaches 90% and the current loss percentage is set to a 50%
   traffic reduction, then the server can decide to increase the loss
   percentage to 55% in order to get to a system utilization of 80%.
   Similarly, the server can lower the loss percentage if permitted by
   the system utilization.

   Loss-based overload control requires that the throttle percentage is
   adjusted to the current overall number of requests received by the
   server.  This is particularly important if the number of requests
   received fluctuates quickly.  For example, if a SIP server sets a
   throttle value of 10% at time t1 and the number of requests increases
   by 20% between time t1 and t2 (t1<t2), then the server will see an
   increase in traffic by 10% between time t1 and t2.  This is even
   though all upstream neighbors have reduced traffic by 10% as told.
   Thus, percentage throttling requires an adjustment of the throttling
   percentage in response to the traffic received and may not always be
   able to prevent a server from encountering brief periods of overload
   in extreme cases.

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9.3.  Window-based Overload Control

   The key idea of window-based overload control is to allow an entity
   to transmit a certain number of messages before it needs to receive a
   confirmation for the messages in transit.  Each sender maintains an
   overload window that limits the number of messages that can be in
   transit without being confirmed.  Window-based overload control is
   inspired by TCP [RFC0793].

   Each sender maintains an unconfirmed message counter for each
   downstream neighbor it is communicating with.  For each message sent
   to the downstream neighbor, the counter is increased.  For each
   confirmation received, the counter is decreased.  The sender stops
   transmitting messages to the downstream neighbor when the unconfirmed
   message counter has reached the current window size.

   A crucial parameter for the performance of window-based overload
   control is the window size.  Each sender has an initial window size
   it uses when first sending a request.  This window size can be
   changed based on the feedback it receives from the receiver.

   The sender adjusts its window size as soon as it receives the
   corresponding feedback from the receiver.  If the new window size is
   smaller than the current unconfirmed message counter, the sender
   stops transmitting messages until more messages are confirmed and the
   current unconfirmed message counter is less than the window size.

   Note that the reception of a 100 Trying response does not provide a
   confirmation for the reception of a message. 100 Trying responses are
   often created by a SIP server very early in processing and do not
   indicate that a message has been successfully processed and cleared
   from the input buffer.  If the downstream neighbor is a stateless
   proxy, it will not create 100 Trying responses at all and instead
   pass through 100 Trying responses created by the next stateful
   server.  Also, 100 Trying responses are typically only created for
   INVITE requests.  Explicit message confirmations do not have these

   Window-based overload control is similar to rate-based overload
   control in that the total available receiver buffer space needs to be
   divided among all upstream neighbors.  However, unlike rate-based
   overload control, window-based overload control is self-limiting and
   can ensure that the receiver buffer does not overflow under normal
   conditions.  The transmission of messages by senders is clocked by
   message confirmations received from the receiver.  A buffer overflow
   can occur in extreme cases when a large number of new upstream
   neighbors arrives at the same time.  However, senders will eventually
   stop transmitting new requests once their initial sending window is

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   In window-based overload control, the number of messages a sender is
   allowed to send can frequently be set to zero.  In this state, the
   sender needs to be informed when it is allowed to send again and the
   receiver window has opened up.  However, since the sender is not
   allowed to transmit messages, the receiver cannot convey the new
   window size by piggybacking it in a response to another message.
   Instead, it needs to inform the sender through another mechanism,
   e.g., by sending a message that contains the new window size.

9.4.  Overload Signal-based Overload Control

   The key idea of overload signal-based overload control is to use the
   transmission of a 503 (Service Unavailable) response as a signal for
   overload in the downstream neighbor.  After receiving a 503 (Service
   Unavailable) response, the sender reduces the load forwarded to the
   downstream neighbor to avoid triggering more 503 (Service
   Unavailable) responses.  The sender keeps reducing the load if more
   503 (Service Unavailable) responses are received.  Note that this
   scheme is based on the use of 503 (Service Unavailable) responses
   without Retry-After header as the Retry-After header would require a
   sender to entirely stop forwarding requests.  It should also be noted
   that 503 responses can be generated for other reasons than overload
   (e.g., server maintenance).

   A sender which has not received 503 (Service Unavailable) responses
   for a while but is still throttling traffic can start to increase the
   offered load.  By slowly increasing the traffic forwarded a sender
   can detect that overload in the downstream neighbor has been resolved
   and more load can be forwarded.  The load is increased until the
   sender again receives another 503 (Service Unavailable) response or
   is forwarding all requests it has.  A possible algorithm for
   adjusting traffic is additive increase/multiplicative decrease

   Overload Signal-based Overload Control is a sender-based overload
   control mechanism.

9.5.  On-/Off Overload Control

   On-/off overload control feedback enables a SIP server to turn the
   traffic it is receiving either on or off.  The 503 (Service
   Unavailable) response with Retry-After header implements on-/off
   overload control.  On-/off overload control is less effective in
   controlling load than the fine grained control methods above.  In
   fact, all above methods can realize on/-off overload control, e.g.,
   by setting the allowed rate to either zero or unlimited.

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10.  Implicit Overload Control

   Implicit overload control ensures that the transmission of a SIP
   server is self-limiting.  It slows down the transmission rate of a
   sender when there is an indication that the receiving entity is
   experiencing overload.  Such an indication can be that the receiving
   entity is not responding within the expected timeframe or is not
   responding at all.  The idea of implicit overload control is that
   senders should try to sense overload of a downstream neighbor even if
   there is no explicit overload control feedback.  It avoids that an
   overloaded server, which has become unable to generate overload
   control feedback, will be overwhelmed with requests.

   Window-based overload control is inherently self-limiting since a
   sender cannot continue without receiving confirmations.  All other
   explicit overload control schemes described above do not have this
   property and require additional implicit controls to limit
   transmissions in case an overloaded downstream neighbor does not
   generate explicit feedback.

11.  Overload Control Algorithms

   An important aspect of the design of an overload control mechanism is
   the overload control algorithm.  The control algorithm determines
   when the amount of traffic to a SIP server needs to be decreased and
   when it can be increased.  In terms of the model described in
   Section 4 the control algorithm takes (S) as an input value and
   generates (T) as a result.

   Overload control algorithms have been studied to a large extent and
   many different overload control algorithms exist.  With many
   different overload control algorithms available, it seems reasonable
   to suggest a baseline algorithm in a specification for a SIP overload
   control mechanism and allow the use of other algorithms if they
   provide the same protocol semantics.  This will also allow the
   development of future algorithms, which may lead to a better
   performance.  Conversely, the overload control mechanism should allow
   the use of different algorithms if they adhere to the defined
   protocol semantics.

12.  Message Prioritization

   Overload control can require a SIP server to prioritize requests and
   select requests to be rejected or redirected.  The selection is
   largely a matter of local policy of the SIP server, the overall
   network, and the services it provides.  As a general rule, SIP server

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   should prioritize requests for ongoing dialogs over requests that set
   up a new dialog.  Targeting requests for ongoing dialogs may prevent
   users from modifying or terminating an ongoing dialog.

   While there are many factors which can affect the prioritization of
   SIP requests, the Resource-Priority header field [RFC4412] is a prime
   candidate for marking the prioritization of SIP requests.  Depending
   on the particular network and the services it offers, a particular
   namespace and priority value in the RPH it could indicate i) a high
   priority request, which should be preserved if possible during
   overload, ii) a low priority request, which should be dropped during
   overload, or iii) a label, which has no impact on message
   prioritization in this network.

   For a number of reasons, responses should not be targeted in order to
   reduce SIP server load.  Responses cannot be rejected and would have
   to be dropped.  This triggers the retransmission of the request plus
   the response, leading to even more load.  In addition, the request
   associated with a response has already been processed and dropping
   the response will waste the efforts that have been spent on the
   request.  Most importantly, rejecting a request effectively also
   removes the request and the response.  If no requests are passed
   along there will be no responses coming back in return.

   Overload control does not change the retransmission behavior of SIP.
   Retransmissions are triggered using procedures defined in RFC 3261
   [RFC3261] and not subject to throttling.

13.  Security Considerations

   Overload control mechanisms, in general, have security implications.
   If not designed carefully they can, for example, be used to launch a
   denial of service attack.  The specific security risks and their
   remedies depend on the actual protocol mechanisms chosen for overload
   control.  They need to be addressed in a document that specifies such
   a mechanism.

14.  IANA Considerations

   This document does not require any IANA considerations.

15.  Informative References

   [Abdelal et al.]
              Abdelal, A. and W. Matragi, "Signal-Based Overload Control

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              for SIP Servers", 7th Annual IEEE Consumer Communications
              and Networking Conference (CCNC-10), Las Vegas, Nevada,
              USA, January 2010.

   [Hilt et al.]
              Hilt, V. and I. Widjaja, "Controlling Overload in Networks
              of SIP Servers", IEEE International Conference on Network
              Protocols (ICNP'08), Orlando, Florida, October 2008.

   [Noel et al.]
              Noel, E. and C. Johnson, "Initial Simulation Results That
              Analyze SIP Based VoIP Networks Under Overload",
              International Teletraffic Congress (ITC'07), Ottawa,
              Canada, June 2007.

   [RFC0793]  Postel, J., "Transmission Control Protocol", STD 7,
              RFC 793, September 1981.

   [RFC3261]  Rosenberg, J., Schulzrinne, H., Camarillo, G., Johnston,
              A., Peterson, J., Sparks, R., Handley, M., and E.
              Schooler, "SIP: Session Initiation Protocol", RFC 3261,
              June 2002.

   [RFC4412]  Schulzrinne, H. and J. Polk, "Communications Resource
              Priority for the Session Initiation Protocol (SIP)",
              RFC 4412, February 2006.

   [RFC5390]  Rosenberg, J., "Requirements for Management of Overload in
              the Session Initiation Protocol", RFC 5390, December 2008.

   [Shen et al.]
              Shen, C., Schulzrinne, H., and E. Nahum, "Session
              Initiation Protocol (SIP) Server Overload Control: Design
              and Evaluation, Principles", Systems and Applications of
              IP Telecommunications (IPTComm'08), Heidelberg, Germany,
              July 2008.

Appendix A.  Contributors

   Many thanks for the contributions, comments and feedback on this
   document to: Mary Barnes (Nortel), Janet Gunn (CSC), Carolyn Johnson
   (AT&T Labs), Paul Kyzivat (Cisco), Daryl Malas (CableLabs), Tom
   Phelan (Sonus Networks), Jonathan Rosenberg (Cisco), Henning
   Schulzrinne (Columbia University), Robert Sparks (Tekelec), Nick
   Stewart (British Telecommunications plc), Rich Terpstra (Level 3),
   Fangzhe Chang (Bell Labs/Alcatel-Lucent).

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Authors' Addresses

   Volker Hilt
   Bell Labs/Alcatel-Lucent
   791 Holmdel-Keyport Rd
   Holmdel, NJ  07733

   Email: volker.hilt@alcatel-lucent.com

   Eric Noel
   AT&T Labs

   Email: eric.noel@att.com

   Charles Shen
   Columbia University

   Email: charles@cs.columbia.edu

   Ahmed Abdelal
   Sonus Networks

   Email: aabdelal@sonusnet.com

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