Internet Engineering Task Force (IETF) V. Hilt
Request for Comments: 6357 Bell Labs/Alcatel-Lucent
Category: Informational E. Noel
ISSN: 2070-1721 AT&T Labs
C. Shen
Columbia University
A. Abdelal
Sonus Networks
August 2011
Design Considerations for
Session Initiation Protocol (SIP) Overload Control
Abstract
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 document is not an Internet Standards Track specification; it is
published for informational purposes.
This document is a product of the Internet Engineering Task Force
(IETF). It represents the consensus of the IETF community. It has
received public review and has been approved for publication by the
Internet Engineering Steering Group (IESG). Not all documents
approved by the IESG are a candidate for any level of Internet
Standard; see Section 2 of RFC 5741.
Information about the current status of this document, any errata,
and how to provide feedback on it may be obtained at
http://www.rfc-editor.org/info/rfc6357.
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Copyright Notice
Copyright (c) 2011 IETF Trust and the persons identified as the
document authors. All rights reserved.
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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 . . . . . . . . . . . . . . . . . . . . . . . . . . 12
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 . . . . . . . . . . . . . . . . . . . . 21
13. Operational Considerations . . . . . . . . . . . . . . . . . . 21
14. Security Considerations . . . . . . . . . . . . . . . . . . . 22
15. Informative References . . . . . . . . . . . . . . . . . . . . 23
Appendix A. Contributors . . . . . . . . . . . . . . . . . . . . 25
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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 SIP messages in a
network of SIP servers can be significantly degraded. In fact,
overload in a SIP server may lead to a situation in which the
overload is amplified by retransmissions of SIP messages causing the
throughput to drop down to a very small fraction of the original
processing capacity. This is often called congestion collapse.
An overload control mechanism enables a SIP server to process SIP
messages 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
Public Switched Telephone Network (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
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other services. The specifics of controlling the resource usage of
other services and their coordination is out of scope for this
document.
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 SIP Overload Control (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] [Noel] [Shen] [Abdelal].
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.
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
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successfully forwarded will decrease as the load increases. Thus,
the number of transactions that complete successfully will decrease
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 user agents (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 difference 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 to 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 overload.
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
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explicit overload signal enables a SIP server to actively steer the
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. For example, if (F) is defined as a loss-rate (e.g.,
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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).
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
accordingly.
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
independently.
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 the Monitor and Actuator
reside on the same server. In this case, the sending entity and
receiving entity are the same SIP server, and the 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. That is, 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
server also becomes overloaded, it will report this problem to its
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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 user agent client (UAC) to
user agent server (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 feedback.
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. A previous message
to a target that has been routed through an overloaded server does
not necessarily mean that 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
take.
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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 created by a SIP UA to achieve a
specific goal (e.g., to start setting up a call). All messages have
their own source and destination addresses. Even SIP messages
containing identical SIP URIs (e.g., a SUBSCRIBE and an INVITE
message to the same SIP URI) can be routed to different destinations.
This is different from TCP, where the traffic exchanged between
routers consists of packets belonging to a usually longer flow of
messages exchanged between a source and a destination (e.g., to
transmit a file). If congestion occurs, the sources can detect this
condition and adjust the rate at which the next 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 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 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.
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6. Topologies
The following topologies describe four generic SIP server
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 acts 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 are
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 all upstream neighbors need to be served by server E
periodically to ensure that updated information is received.
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
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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
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" scenario.
+---+ +---+
/->| 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
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
time.
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 connecting through an overloaded SIP
server, irrespective of the service provider in which the call was
initiated. An example of customized fairness would be a server that
assigns different resource allocations to its upstream neighbors
(e.g., service providers) as defined in a service level agreement
(SLA).
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
maximum capacity during periods of overload. For example, 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 introduced by a SIP server is another important
indicator. An overload control mechanism should ensure that the
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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], [Shen], [Hilt], and [Abdelal].
In addition to the above metrics, there are other indicators that are
relevant for the evaluation of an overload control mechanism:
Fairness: Which type 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
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each upstream neighbor to send, at most, X requests per second. Each
upstream neighbor can be assigned a different rate cap.
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
load.
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,
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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.
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.
To implement a loss percentage, the sending entity may employ an
algorithm 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 be
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
RFC, FYI, BCP