Internet DRAFT - draft-ietf-opsawg-large-flow-load-balancing
draft-ietf-opsawg-large-flow-load-balancing
OPSAWG R. Krishnan
Internet Draft Brocade Communications
Intended status: Informational L. Yong
Expires: April 6, 2015 Huawei USA
A. Ghanwani
Dell
Ning So
Tata Communications
B. Khasnabish
ZTE Corporation
October 7, 2014
Mechanisms for Optimizing LAG/ECMP Component Link Utilization in
Networks
draft-ietf-opsawg-large-flow-load-balancing-15.txt
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Abstract
Demands on networking infrastructure are growing exponentially due to
bandwidth hungry applications such as rich media applications and
inter-data center communications. In this context, it is important to
optimally use the bandwidth in wired networks that extensively use
link aggregation groups and equal cost multi-paths as techniques for
bandwidth scaling. This draft explores some of the mechanisms useful
for achieving this.
Table of Contents
1. Introduction...................................................3
1.1. Acronyms..................................................4
1.2. Terminology...............................................4
2. Flow Categorization............................................5
3. Hash-based Load Distribution in LAG/ECMP.......................6
4. Mechanisms for Optimizing LAG/ECMP Component Link Utilization..7
4.1. Differences in LAG vs ECMP................................8
4.2. Operational Overview......................................9
4.3. Large Flow Recognition...................................10
4.3.1. Flow Identification.................................10
4.3.2. Criteria and Techniques for Large Flow Recognition..11
4.3.3. Sampling Techniques.................................11
4.3.4. Inline Data Path Measurement........................13
4.3.5. Use of Multiple Methods for Large Flow Recognition..14
4.4. Load Rebalancing Options.................................14
4.4.1. Alternative Placement of Large Flows................14
4.4.2. Redistributing Small Flows..........................15
4.4.3. Component Link Protection Considerations............15
4.4.4. Load Rebalancing Algorithms.........................15
4.4.5. Load Rebalancing Example............................16
5. Information Model for Flow Rebalancing........................17
5.1. Configuration Parameters for Flow Rebalancing............17
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5.2. System Configuration and Identification Parameters.......18
5.3. Information for Alternative Placement of Large Flows.....19
5.4. Information for Redistribution of Small Flows............19
5.5. Export of Flow Information...............................20
5.6. Monitoring information...................................20
5.6.1. Interface (link) utilization........................20
5.6.2. Other monitoring information........................21
6. Operational Considerations....................................21
6.1. Rebalancing Frequency....................................21
6.2. Handling Route Changes...................................21
6.3. Forwarding Resources.....................................22
7. IANA Considerations...........................................22
8. Security Considerations.......................................22
9. Contributing Authors..........................................22
10. Acknowledgements.............................................23
11. References...................................................23
11.1. Normative References....................................23
11.2. Informative References..................................23
1. Introduction
Networks extensively use link aggregation groups (LAG) [802.1AX] and
equal cost multi-paths (ECMP) [RFC 2991] as techniques for capacity
scaling. For the problems addressed by this document, network traffic
can be predominantly categorized into two traffic types: long-lived
large flows and other flows. These other flows, which include long-
lived small flows, short-lived small flows, and short-lived large
flows, are referred to as "small flows" in this document. Long-lived
large flows are simply referred to as "large flows."
Stateless hash-based techniques [ITCOM, RFC 2991, RFC 2992, RFC 6790]
are often used to distribute both large flows and small flows over
the component links in a LAG/ECMP. However the traffic may not be
evenly distributed over the component links due to the traffic
pattern.
This draft describes mechanisms for optimizing LAG/ECMP component
link utilization while using hash-based techniques. The mechanisms
comprise the following steps -- recognizing large flows in a router;
and assigning the large flows to specific LAG/ECMP component links or
redistributing the small flows when a component link on the router is
congested.
It is useful to keep in mind that in typical use cases for this
mechanism the large flows are those that consume a significant amount
of bandwidth on a link, e.g. greater than 5% of link bandwidth. The
number of such flows would necessarily be fairly small, e.g. on the
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order of 10's or 100's per LAG/ECMP. In other words, the number of
large flows is NOT expected to be on the order of millions of flows.
Examples of such large flows would be IPsec tunnels in service
provider backbone networks or storage backup traffic in data center
networks.
1.1. Acronyms
DOS: Denial of Service
ECMP: Equal Cost Multi-path
GRE: Generic Routing Encapsulation
LAG: Link Aggregation Group
MPLS: Multiprotocol Label Switching
NVGRE: Network Virtualization using Generic Routing Encapsulation
PBR: Policy Based Routing
QoS: Quality of Service
STT: Stateless Transport Tunneling
TCAM: Ternary Content Addressable Memory
VXLAN: Virtual Extensible LAN
1.2. Terminology
Central management entity: Refers to an entity that is capable of
monitoring information about link utilization and flows in routers
across the network and may be capable of making traffic engineering
decisions for placement of large flows. It may include the functions
of a collector [RFC 7011].
ECMP component link: An individual nexthop within an ECMP group. An
ECMP component link may itself comprise a LAG.
ECMP table: A table that is used as the nexthop of an ECMP route that
comprises the set of ECMP component links and the weights associated
with each of those ECMP component links. The input for looking up
the table is the hash value for the packet, and the weights are used
to determine which values of the hash function map to a given ECMP
component link.
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LAG component link: An individual link within a LAG. A LAG component
link is typically a physical link.
LAG table: A table that is used as the output port which is a LAG
that comprises the set of LAG component links and the weights
associated with each of those component links. The input for looking
up the table is the hash value for the packet, and the weights are
used to determine which values of the hash function map to a given
LAG component link.
Large flow(s): Refers to long-lived large flow(s).
Small flow(s): Refers to any of, or a combination of, long-lived
small flow(s), short-lived small flows, and short-lived large
flow(s).
2. Flow Categorization
In general, based on the size and duration, a flow can be categorized
into any one of the following four types, as shown in Figure 1:
(a) Short-lived Large Flow (SLLF),
(b) Short-lived Small Flow (SLSF),
(c) Long-lived Large Flow (LLLF), and
(d) Long-lived Small Flow (LLSF).
Flow Bandwidth
^
|--------------------|--------------------|
| | |
Large | SLLF | LLLF |
Flow | | |
|--------------------|--------------------|
| | |
Small | SLSF | LLSF |
Flow | | |
+--------------------+--------------------+-->Flow Duration
Short-lived Long-lived
Flow Flow
Figure 1: Flow Categorization
In this document, as mentioned earlier, we categorize long-lived
large flows as "large flows", and all of the others -- long-lived
small flows, short-lived small flows, and short-lived large flows
as "small flows".
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3. Hash-based Load Distribution in LAG/ECMP
Hash-based techniques are often used for traffic load balancing to
select among multiple available paths within a LAG/ECMP group. The
advantages of hash-based techniques for load distribution are the
preservation of the packet sequence in a flow and the real-time
distribution without maintaining per-flow state in the router. Hash-
based techniques use a combination of fields in the packet's headers
to identify a flow, and the hash function computed using these fields
is used to generate a unique number that identifies a link/path in a
LAG/ECMP group. The result of the hashing procedure is a many-to-one
mapping of flows to component links.
If the traffic mix constitutes flows such that the result of the hash
function across these flows is fairly uniform so that a similar
number of flows is mapped to each component link, if the individual
flow rates are much smaller as compared to the link capacity, and if
the rate differences are not dramatic, hash-based techniques produce
good results with respect to utilization of the individual component
links. However, if one or more of these conditions are not met, hash-
based techniques may result in imbalance in the loads on individual
component links.
One example is illustrated in Figure 2. In Figure 2, there are two
routers, R1 and R2, and there is a LAG between them which has 3
component links (1), (2), (3). There are a total of 10 flows that
need to be distributed across the links in this LAG. The result of
applying the hash-based technique is as follows:
. Component link (1) has 3 flows -- 2 small flows and 1 large
flow -- and the link utilization is normal.
. Component link (2) has 3 flows -- 3 small flows and no large
flow -- and the link utilization is light.
o The absence of any large flow causes the component link
under-utilized.
. Component link (3) has 4 flows -- 2 small flows and 2 large
flows -- and the link capacity is exceeded resulting in
congestion.
o The presence of 2 large flows causes congestion on this
component link.
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+-----------+ -> +-----------+
| | -> | |
| | ===> | |
| (1)|--------|(1) |
| | -> | |
| | -> | |
| (R1) | -> | (R2) |
| (2)|--------|(2) |
| | -> | |
| | -> | |
| | ===> | |
| | ===> | |
| (3)|--------|(3) |
| | | |
+-----------+ +-----------+
Where: -> small flow
===> large flow
Figure 2: Unevenly Utilized Component Links
This document presents mechanisms for addressing the imbalance in
load distribution resulting from commonly used hash-based techniques
for LAG/ECMP that were shown in the above example. The mechanisms use
large flow awareness to compensate for the imbalance in load
distribution.
4. Mechanisms for Optimizing LAG/ECMP Component Link Utilization
The suggested mechanisms in this draft are about a local optimization
solution; they are local in the sense that both the identification of
large flows and re-balancing of the load can be accomplished
completely within individual nodes in the network without the need
for interaction with other nodes.
This approach may not yield a global optimization of the placement of
large flows across multiple nodes in a network, which may be
desirable in some networks. On the other hand, a local approach may
be adequate for some environments for the following reasons:
1) Different links within a network experience different levels of
utilization and, thus, a "targeted" solution is needed for those hot-
spots in the network. An example is the utilization of a LAG between
two routers that needs to be optimized.
2) Some networks may lack end-to-end visibility, e.g. when a
certain network, under the control of a given operator, is a transit
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network for traffic from other networks that are not under the
control of the same operator.
4.1. Differences in LAG vs ECMP
While the mechanisms explained herein are applicable to both LAGs and
ECMP groups, it is useful to note that there are some key differences
between the two that may impact how effective the mechanism is. This
relates, in part, to the localized information with which the scheme
is intended to operate.
A LAG is usually established across links that are between 2 adjacent
routers. As a result, the scope of problem of optimizing the
bandwidth utilization on the component links is fairly narrow. It
simply involves re-balancing the load across the component links
between these two routers, and there is no impact whatsoever to other
parts of the network. The scheme works equally well for unicast and
multicast flows.
On the other hand, with ECMP, redistributing the load across
component links that are part of the ECMP group may impact traffic
patterns at all of the nodes that are downstream of the given router
between itself and the destination. The local optimization may
result in congestion at a downstream node. (In its simplest form, an
ECMP group may be used to distribute traffic on component links that
are between two adjacent routers, and in that case, the ECMP group is
no different than a LAG for the purpose of this discussion. It
should be noted that an ECMP component link may itself comprise a
LAG, in which case the scheme may be further applied to the component
links within the LAG.)
+-----+ +-----+
| S1 | | S2 |
+-----+ +-----+
/ \ \ / /\
/ +---------+ / \
/ / \ \ / \
/ / \ +------+ \
/ / \ / \ \
+-----+ +-----+ +-----+
| L1 | | L2 | | L3 |
+-----+ +-----+ +-----+
Figure 3: Two-level Clos Network
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To demonstrate the limitations of local optimization, consider a two-
level Clos network topology as shown in Figure 3 with three leaf
nodes (L1, L2, L3) and two spine nodes (S1, S2). Assume all of the
links are 10 Gbps.
Let L1 have two flows of 4 Gbps each towards L3, and let L2 have one
flow of 7 Gbps also towards L3. If L1 balances the load optimally
between S1 and S2, and L2 sends the flow via S1, then the downlink
from S1 to L3 would get congested resulting in packet discards. On
the other hand, if L1 had sent both its flows towards S1 and L2 had
sent its flow towards S2, there would have been no congestion at
either S1 or S2.
The other issue with applying this scheme to ECMP groups is that it
may not apply equally to unicast and multicast traffic because of the
way multicast trees are constructed.
Finally, it is possible for a single physical link to participate as
a component link in multiple ECMP groups, whereas with LAGs, a link
can participate as a component link of only one LAG.
4.2. Operational Overview
The various steps in optimizing LAG/ECMP component link utilization
in networks are detailed below:
Step 1) This involves large flow recognition in routers and
maintaining the mapping of the large flow to the component link that
it uses. The recognition of large flows is explained in Section 4.3.
Step 2) The egress component links are periodically scanned for link
utilization and the imbalance for the LAG/ECMP group is monitored. If
the imbalance exceeds a certain imbalance threshold, then re-
balancing is triggered. Measurement of the imbalance is discussed
further in 5.1. Additional criteria may also be used to determine
whether or not to trigger rebalancing, such as the maximum
utilization of any of the component links, in addition to the
imbalance. The use of sampling techniques for the measurement of
egress component link utilization, including the issues of depending
on ingress sampling for these measurements, are discussed in Section
4.3.3.
Step 3) As a part of rebalancing, the operator can choose to
rebalance the large flows on to lightly loaded component links of the
LAG/ECMP group, redistribute the small flows on the congested link to
other component links of the group, or a combination of both.
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All of the steps identified above can be done locally within the
router itself or could involve the use of a central management
entity.
Providing large flow information to a central management entity
provides the capability to globally optimize flow distribution as
described in Section 4.1. Consider the following example. A router
may have 3 ECMP nexthops that lead down paths P1, P2, and P3. A
couple of hops downstream on path P1 there may be a congested link,
while paths P2 and P3 may be under-utilized. This is something that
the local router does not have visibility into. With the help of a
central management entity, the operator could redistribute some of
the flows from P1 to P2 and/or P3 resulting in a more optimized flow
of traffic.
The mechanisms described above are especially useful when bundling
links of different bandwidths for e.g. 10 Gbps and 100 Gbps as
described in [ID.ietf-rtgwg-cl-requirement].
4.3. Large Flow Recognition
4.3.1. Flow Identification
A flow (large flow or small flow) can be defined as a sequence of
packets for which ordered delivery should be maintained. Flows are
typically identified using one or more fields from the packet header,
for example:
. Layer 2: Source MAC address, destination MAC address, VLAN ID.
. IP header: IP Protocol, IP source address, IP destination
address, flow label (IPv6 only)
. Transport protocol header: Source port number, destination port
number. These apply to protocols such as TCP, UDP, SCTP.
. MPLS Labels.
For tunneling protocols like Generic Routing Encapsulation (GRE)
[RFC 2784], Virtual eXtensible Local Area Network (VXLAN) [RFC 7348],
Network Virtualization using Generic Routing Encapsulation (NVGRE)
[NVGRE], Stateless Transport Tunneling (STT) [STT], Layer 2 Tunneling
Protocol (L2TP) [RFC 3931], etc., flow identification is possible
based on inner and/or outer headers as well as fields introduced by
the tunnel header, as any or all such fields may be used for load
balancing decisions [RFC 5640]. The above list is not exhaustive.
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The mechanisms described in this document are agnostic to the fields
that are used for flow identification.
This method of flow identification is consistent with that of IPFIX
[RFC 7011].
4.3.2. Criteria and Techniques for Large Flow Recognition
From a bandwidth and time duration perspective, in order to recognize
large flows we define an observation interval and observe the
bandwidth of the flow over that interval. A flow that exceeds a
certain minimum bandwidth threshold over that observation interval
would be considered a large flow.
The two parameters -- the observation interval, and the minimum
bandwidth threshold over that observation interval -- should be
programmable to facilitate handling of different use cases and
traffic characteristics. For example, a flow which is at or above 10%
of link bandwidth for a time period of at least 1 second could be
declared a large flow [DevoFlow].
In order to avoid excessive churn in the rebalancing, once a flow has
been recognized as a large flow, it should continue to be recognized
as a large flow for as long as the traffic received during an
observation interval exceeds some fraction of the bandwidth
threshold, for example 80% of the bandwidth threshold.
Various techniques to recognize a large flow are described below.
4.3.3. Sampling Techniques
A number of routers support sampling techniques such as sFlow [sFlow-
v5, sFlow-LAG], PSAMP [RFC 5475] and NetFlow Sampling [RFC 3954].
For the purpose of large flow recognition, sampling needs to be
enabled on all of the egress ports in the router where such
measurements are desired.
Using sFlow as an example, processing in a sFlow collector will
provide an approximate indication of the large flows mapping to each
of the component links in each LAG/ECMP group. It is possible to
implement this part of the collector function in the control plane of
the router reducing dependence on an external management station,
assuming sufficient control plane resources are available.
If egress sampling is not available, ingress sampling can suffice
since the central management entity used by the sampling technique
typically has multi-node visibility and can use the samples from an
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immediately downstream node to make measurements for egress traffic
at the local node.
The option of using ingress sampling for this purpose may not be
available if the downstream device is under the control of a
different operator, or if the downstream device does not support
sampling.
Alternatively, since sampling techniques require that the sample be
annotated with the packet's egress port information, ingress sampling
may suffice. However, this means that sampling would have to be
enabled on all ports, rather than only on those ports where such
monitoring is desired. There is one situation in which this approach
may not work. If there are tunnels that originate from the given
router, and if the resulting tunnel comprises the large flow, then
this cannot be deduced from ingress sampling at the given router.
Instead, if egress sampling is unavailable, then ingress sampling
from the downstream router must be used.
To illustrate the use of ingress versus egress sampling, we refer to
Figure 2. Since we are looking at rebalancing flows at R1, we would
need to enable egress sampling on ports (1), (2), and (3) on R1. If
egress sampling is not available, and if R2 is also under the control
of the same administrator, enabling ingress sampling on R2's ports
(1), (2), and (3) would also work, but it would necessitate the
involvement of a central management entity in order for R1 to obtain
large flow information for each of its links. Finally, R1 can enable
ingress sampling only on all of its ports (not just the ports that
are part of the LAG/ECMP group being monitored) and that would
suffice if the sampling technique annotates the samples with the
egress port information.
The advantages and disadvantages of sampling techniques are as
follows.
Advantages:
. Supported in most existing routers.
. Requires minimal router resources.
Disadvantages:
. In order to minimize the error inherent in sampling, there is a
minimum delay for the recognition time of large flows, and in
the time that it takes to react to this information.
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With sampling, the detection of large flows can be done on the order
of one second [DevoFlow]. A discussion on determining the
appropriate sampling frequency is available in the following
reference [SAMP-BASIC].
4.3.4. Inline Data Path Measurement
Implementations may perform recognition of large flows by performing
measurements on traffic in the data path of a router. Such an
approach would be expected to operate at the interface speed on every
interface, accounting for all packets processed by the data path of
the router. An example of such an approach is described in IPFIX
[RFC 5470].
Using inline data path measurement, a faster and more accurate
indication of large flows mapped to each of the component links in a
LAG/ECMP group may be possible (as compared to the sampling-based
approach).
The advantages and disadvantages of inline data path measurement are:
Advantages:
. As link speeds get higher, sampling rates are typically reduced
to keep the number of samples manageable which places a lower
bound on the detection time. With inline data path measurement,
large flows can be recognized in shorter windows on higher link
speeds since every packet is accounted for [NDTM].
. Eliminates the potential dependence on an external management
station for large flow recognition.
Disadvantages:
. It is more resource intensive in terms of the tables sizes
required for monitoring all flows in order to perform the
measurement.
As mentioned earlier, the observation interval for determining a
large flow and the bandwidth threshold for classifying a flow as a
large flow should be programmable parameters in a router.
The implementation details of inline data path measurement of large
flows is vendor dependent and beyond the scope of this document.
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4.3.5. Use of Multiple Methods for Large Flow Recognition
It is possible that a router may have line cards that support a
sampling technique while other line cards support inline data path
measurement of large flows. As long as there is a way for the router
to reliably determine the mapping of large flows to component links
of a LAG/ECMP group, it is acceptable for the router to use more than
one method for large flow recognition.
If both methods are supported, inline data path measurement may be
preferable because of its speed of detection [FLOW-ACC].
4.4. Load Rebalancing Options
Below are suggested techniques for load balancing. Equipment vendors
may implement more than one technique, including those not described
in this document, and allow the operator to choose between them.
Note that regardless of the method used, perfect rebalancing of large
flows may not be possible since flows arrive and depart at different
times. Also, any flows that are moved from one component link to
another may experience momentary packet reordering.
4.4.1. Alternative Placement of Large Flows
Within a LAG/ECMP group, the member component links with least
average port utilization are identified. Some large flow(s) from the
heavily loaded component links are then moved to those lightly-loaded
member component links using a policy-based routing (PBR) rule in the
ingress processing element(s) in the routers.
With this approach, only certain large flows are subjected to
momentary flow re-ordering.
When a large flow is moved, this will increase the utilization of the
link that it moved to potentially creating imbalance in the
utilization once again across the component links. Therefore, when
moving large flows, care must be taken to account for the existing
load, and what the future load will be after large flow has been
moved. Further, the appearance of new large flows may require a
rearrangement of the placement of existing flows.
Consider a case where there is a LAG compromising four 10 Gbps
component links and there are four large flows, each of 1 Gbps.
These flows are each placed on one of the component links.
Subsequent, a fifth large flow of 2 Gbps is recognized and to
maintain equitable load distribution, it may require placement of one
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of the existing 1 Gbps flow to a different component link. And this
would still result in some imbalance in the utilization across the
component links.
4.4.2. Redistributing Small Flows
Some large flows may consume the entire bandwidth of the component
link(s). In this case, it would be desirable for the small flows to
not use the congested component link(s). This can be accomplished in
one of the following ways.
This method works on some existing router hardware. The idea is to
prevent, or reduce the probability, that the small flow hashes into
the congested component link(s).
. The LAG/ECMP table is modified to include only non-congested
component link(s). Small flows hash into this table to be mapped
to a destination component link. Alternatively, if certain
component links are heavily loaded, but not congested, the
output of the hash function can be adjusted to account for large
flow loading on each of the component links.
. The PBR rules for large flows (refer to Section 4.4.1) must
have strict precedence over the LAG/ECMP table lookup result.
With this approach the small flows that are moved would be subject to
reordering.
4.4.3. Component Link Protection Considerations
If desired, certain component links may be reserved for link
protection. These reserved component links are not used for any flows
in the absence of any failures. In the case when the component
link(s) fail, all the flows on the failed component link(s) are moved
to the reserved component link(s). The mapping table of large flows
to component link simply replaces the failed component link with the
reserved link. Likewise, the LAG/ECMP table replaces the failed
component link with the reserved link.
4.4.4. Load Rebalancing Algorithms
Specific algorithms for placement of large flows are out of scope of
this document. One possibility is to formulate the problem for large
flow placement as the well-known bin-packing problem and make use of
the various heuristics that are available for that problem [bin-
pack].
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4.4.5. Load Rebalancing Example
Optimizing LAG/ECMP component utilization for the use case in Figure
2 is depicted below in Figure 4. The large flow rebalancing explained
in Section 4.4 is used. The improved link utilization is as follows:
. Component link (1) has 3 flows -- 2 small flows and 1 large
flow -- and the link utilization is normal.
. Component link (2) has 4 flows -- 3 small flows and 1 large
flow -- and the link utilization is normal now.
. Component link (3) has 3 flows -- 2 small flows and 1 large
flow -- and the link utilization is normal now.
+-----------+ -> +-----------+
| | -> | |
| | ===> | |
| (1)|--------|(1) |
| | | |
| | ===> | |
| | -> | |
| | -> | |
| (R1) | -> | (R2) |
| (2)|--------|(2) |
| | | |
| | -> | |
| | -> | |
| | ===> | |
| (3)|--------|(3) |
| | | |
+-----------+ +-----------+
Where: -> small flow
===> large flow
Figure 4: Evenly Utilized Composite Links
Basically, the use of the mechanisms described in Section 4.4.1
resulted in a rebalancing of flows where one of the large flows on
component link (3) which was previously congested was moved to
component link (2) which was previously under-utilized.
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5. Information Model for Flow Rebalancing
In order to support flow rebalancing in a router from an external
system, the exchange of some information is necessary between the
router and the external system. This section provides an exemplary
information model covering the various components needed for the
purpose. The model is intended to be informational and may be used
as input for development of a data model.
5.1. Configuration Parameters for Flow Rebalancing
The following parameters are required the configuration of this
feature:
. Large flow recognition parameters:
o Observation interval: The observation interval is the time
period in seconds over which the packet arrivals are
observed for the purpose of large flow recognition.
o Minimum bandwidth threshold: The minimum bandwidth threshold
would be configured as a percentage of link speed and
translated into a number of bytes over the observation
interval. A flow for which the number of bytes received,
for a given observation interval, exceeds this number would
be recognized as a large flow.
o Minimum bandwidth threshold for large flow maintenance: The
minimum bandwidth threshold for large flow maintenance is
used to provide hysteresis for large flow recognition.
Once a flow is recognized as a large flow, it continues to
be recognized as a large flow until it falls below this
threshold. This is also configured as a percentage of link
speed and is typically lower than the minimum bandwidth
threshold defined above.
. Imbalance threshold: A measure of the deviation of the
component link utilizations from the utilization of the overall
LAG/ECMP group. Since component links can be of a different
speed, the imbalance can be computed as follows. Let the
utilization of each component link in a LAG/ECMP group with n
links of speed b_1, b_2 .. b_n, be u_1, u_2 .. u_n. The mean
utilization is computed is u_ave = [ (u_1 x b_1) + (u_2 x b_2) +
.. + (u_n x b_n) ] / [b_1 + b_2 + .. + b_n]. The imbalance is
then computed as max_{i=1..n} | u_i - u_ave |.
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. Rebalancing interval: The minimum amount of time between
rebalancing events. This parameter ensures that rebalancing is
not invoked too frequently as it impacts packet ordering.
These parameters may be configured on a system-wide basis or it may
apply to an individual LAG. It may be applied to an ECMP group
provided the component links are not shared with any other ECMP
group.
5.2. System Configuration and Identification Parameters
The following parameters are useful for router configuration and
operation when using the mechanisms in this document.
. IP address: The IP address of a specific router that the
feature is being configured on, or that the large flow placement
is being applied to.
. LAG ID: Identifies the LAG on a given router. The LAG ID may be
required when configuring this feature (to apply a specific set
of large flow identification parameters to the LAG) and will be
required when specifying flow placement to achieve the desired
rebalancing.
. Component Link ID: Identifies the component link within a LAG
or ECMP group. This is required when specifying flow placement
to achieve the desired rebalancing.
. Component Link Weight: The relative weight to be applied to
traffic for a given component link when using hash-based
techniques for load distribution.
. ECMP group: Identifies a particular ECMP group. The ECMP group
may be required when configuring this feature (to apply a
specific set of large flow identification parameters to the ECMP
group) and will be required when specifying flow placement to
achieve the desired rebalancing. We note that multiple ECMP
groups can share an overlapping set (or non-overlapping subset)
of component links. This document does not deal with the
complexity of addressing such configurations.
The feature may be configured globally for all LAGs and/or for all
ECMP groups, or it may be configured specifically for a given LAG or
ECMP group.
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5.3. Information for Alternative Placement of Large Flows
In cases where large flow recognition is handled by an external
management station (see Section 4.3.3), an information model for
flows is required to allow the import of large flow information to
the router.
Typical fields use for identifying large flows were discussed in
Section 4.3.1. The IPFIX information model [RFC 7012] can be
leveraged for large flow identification.
Large Flow placement is achieved by specifying the relevant flow
information along with the following:
. For LAG: Router's IP address, LAG ID, LAG component link ID.
. For ECMP: Router's IP address, ECMP group, ECMP component link
ID.
In the case where the ECMP component link itself comprises a LAG, we
would have to specify the parameters for both the ECMP group as well
as the LAG to which the large flow is being directed.
5.4. Information for Redistribution of Small Flows
Redistribution of small flows is done using the following:
. For LAG: The LAG ID and the component link IDs along with the
relative weight of traffic to be assigned to each component link
ID are required.
. For ECMP: The ECMP group and the ECMP Nexthop along with the
relative weight of traffic to be assigned to each ECMP Nexthop
are required.
It is possible to have an ECMP nexthop that itself comprises a LAG.
In that case, we would have to specify the new weights for both the
ECMP nexthops within the ECMP group as well as the component links
within the LAG.
In the case where an ECMP component link itself comprises a LAG, we
would have to specify new weights for both the component links within
the ECMP group as well as the component links within the LAG.
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5.5. Export of Flow Information
Exporting large flow information is required when large flow
recognition is being done on a router, but the decision to rebalance
is being made in an external management station. Large flow
information includes flow identification and the component link ID
that the flow currently is assigned to. Other information such as
flow QoS and bandwidth may be exported too.
The IPFIX information model [RFC 7012] can be leveraged for large
flow identification.
5.6. Monitoring information
5.6.1. Interface (link) utilization
The incoming bytes (ifInOctets), outgoing bytes (ifOutOctets) and
interface speed (ifSpeed) can be obtained, for example, from the
Interface table (iftable) MIB [RFC 1213].
The link utilization can then be computed as follows:
Incoming link utilization = (delta_ifInOctets * 8) / (ifSpeed * T)
Outgoing link utilization = (delta_ifOutOctets * 8) / (ifSpeed * T)
Where T is the interval over which the utilization is being measured,
delta_ifInOctets is the change in ifInOctets over that interval, and
delta_ifOutOctets is the change in ifOutOctets over that interval.
For high speed Ethernet links, the etherStatsHighCapacityTable MIB
[RFC 3273] can be used.
Similar results may be achieved using the corresponding objects of
other interface management data models such as YANG [RFC 7223] if
those are used instead of MIBs.
For scalability, it is recommended to use the counter push mechanism
in [sflow-v5] for the interface counters. Doing so would help avoid
counter polling through the MIB interface.
The outgoing link utilization of the component links within a
LAG/ECMP group can be used to compute the imbalance (See Section 5.1)
for the LAG/ECMP group.
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5.6.2. Other monitoring information
Additional monitoring information that is useful includes:
. Number of times rebalancing was done.
. Time since the last rebalancing event.
. The number of large flows currently rebalanced by the scheme.
. A list of the large flows that have been rebalanced including
o the rate of each large flow at the time of the last
rebalancing for that flow,
o the time that rebalancing was last performed for the given
large flow, and
o the interfaces that the large flows was (re)directed to.
. The settings for the weights of the interfaces within a
LAG/ECMP used by the small flows which depend on hashing.
6. Operational Considerations
6.1. Rebalancing Frequency
Flows should be rebalanced only when the imbalance in the utilization
across component links exceeds a certain threshold. Frequent
rebalancing to achieve precise equitable utilization across component
links could be counter-productive as it may result in moving flows
back and forth between the component links impacting packet ordering
and system stability. This applies regardless of whether large flows
or small flows are redistributed. It should be noted that reordering
is a concern for TCP flows with even a few packets because three out-
of-order packets would trigger sufficient duplicate ACKs to the
sender resulting in a retransmission [RFC 5681].
The operator would have to experiment with various values of the
large flow recognition parameters (minimum bandwidth threshold,
observation interval) and the imbalance threshold across component
links to tune the solution for their environment.
6.2. Handling Route Changes
Large flow rebalancing must be aware of any changes to the FIB. In
cases where the nexthop of a route no longer to points to the LAG, or
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to an ECMP group, any PBR entries added as described in Section 4.4.1
and 4.4.2 must be withdrawn in order to avoid the creation of
forwarding loops.
6.3. Forwarding Resources
Hash-based techniques used for load balancing with LAG/ECMP are
usually stateless. The mechanisms described in this document require
additional resources in the forwarding plane of routers for creating
PBR rules that are capable of overriding the forwarding decision from
the hash-based approach. These resources may limit the number of
flows that can be rebalanced and may also impact the latency
experienced by packets due to the additional lookups that are
required.
7. IANA Considerations
This memo includes no request to IANA.
8. Security Considerations
This document does not directly impact the security of the Internet
infrastructure or its applications. In fact, it could help if there
is a DOS attack pattern which causes a hash imbalance resulting in
heavy overloading of large flows to certain LAG/ECMP component
links.
An attacker with knowledge of the large flow recognition algorithm
and any stateless distribution method can generate flows that are
distributed in a way that overloads a specific path. This could be
used to cause the creation of PBR rules that exhaust the available
rule capacity on nodes. If PBR rules are consequently discarded,
this could result in congestion on the attacker-selected path.
Alternatively, tracking large numbers of PBR rules could result in
performance degradation.
9. Contributing Authors
Sanjay Khanna
Cisco Systems
Email: sanjakha@gmail.com
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10. Acknowledgements
The authors would like to thank the following individuals for their
review and valuable feedback on earlier versions of this document:
Shane Amante, Fred Baker, Michael Bugenhagen, Zhen Cao, Brian
Carpenter, Benoit Claise, Michael Fargano, Wes George, Sriganesh
Kini, Roman Krzanowski, Andrew Malis, Dave McDysan, Pete Moyer,
Peter Phaal, Dan Romascanu, Curtis Villamizar, Jianrong Wong, George
Yum, and Weifeng Zhang. As a part of the IETF Last Call process,
valuable comments were received from Martin Thomson and Carlos
Pignatro.
11. References
11.1. Normative References
[802.1AX] IEEE Standards Association, "IEEE Std 802.1AX-2008 IEEE
Standard for Local and Metropolitan Area Networks - Link
Aggregation", 2008.
[RFC 2991] Thaler, D. and C. Hopps, "Multipath Issues in Unicast and
Multicast," November 2000.
[RFC 7011] Claise, B. et al., "Specification of the IP Flow
Information Export (IPFIX) Protocol for the Exchange of IP Traffic
Flow Information," September 2013.
[RFC 7012] Claise, B. and B. Trammell, "Information Model for IP Flow
Information Export (IPFIX)," September 2013.
11.2. Informative References
[bin-pack] Coffman, Jr., E., M. Garey, and D. Johnson. Approximation
Algorithms for Bin-Packing -- An Updated Survey. In Algorithm Design
for Computer System Design, ed. by Ausiello, Lucertini, and Serafini.
Springer-Verlag, 1984.
[CAIDA] "Caida Internet Traffic Analysis," http://www.caida.org/home.
[DevoFlow] Mogul, J., et al., "DevoFlow: Cost-Effective Flow
Management for High Performance Enterprise Networks," Proceedings of
the ACM SIGCOMM, August 2011.
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[FLOW-ACC] Zseby, T., et al., "Packet sampling for flow accounting:
challenges and limitations," Proceedings of the 9th international
conference on Passive and active network measurement, 2008.
[ID.ietf-rtgwg-cl-requirement] Villamizar, C. et al., "Requirements
for MPLS over a Composite Link," September 2013.
[ITCOM] Jo, J., et al., "Internet traffic load balancing using
dynamic hashing with flow volume," SPIE ITCOM, 2002.
[NDTM] Estan, C. and G. Varghese, "New directions in traffic
measurement and accounting," Proceedings of ACM SIGCOMM, August 2002.
[NVGRE] Sridharan, M. et al., "NVGRE: Network Virtualization using
Generic Routing Encapsulation," draft-sridharan-virtualization-
nvgre-06, January 2015.
[RFC 2784] Farinacci, D. et al., "Generic Routing Encapsulation
(GRE)," March 2000.
[RFC 6790] Kompella, K. et al., "The Use of Entropy Labels in MPLS
Forwarding," November 2012.
[RFC 1213] McCloghrie, K., "Management Information Base for Network
Management of TCP/IP-based internets: MIB-II," March 1991.
[RFC 2992] Hopps, C., "Analysis of an Equal-Cost Multi-Path
Algorithm," November 2000.
[RFC 3273] Waldbusser, S., "Remote Network Monitoring Management
Information Base for High Capacity Networks," July 2002.
[RFC 3931] Lau, J. (Ed.), M. Townsley (Ed.), and I. Goyret (Ed.),
"Layer 2 Tunneling Protocol - Version 3," March 2005.
[RFC 3954] Claise, B., "Cisco Systems NetFlow Services Export Version
9," October 2004.
[RFC 5470] G. Sadasivan et al., "Architecture for IP Flow Information
Export," March 2009.
[RFC 5475] Zseby, T. et al., "Sampling and Filtering Techniques for
IP Packet Selection," March 2009.
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[RFC 5640] Filsfils, C., P. Mohapatra, and C. Pignataro, "Load
Balancing for Mesh Softwires," August 2009.
[RFC 5681] Allman, M. et al., "TCP Congestion Control," September
2009.
[RFC 7223] Bjorklund, M., "A YANG Data Model for Interface
Management," May 2014.
[SAMP-BASIC] Phaal, P. and S. Panchen, "Packet Sampling Basics,"
http://www.sflow.org/packetSamplingBasics/.
[sFlow-v5] Phaal, P. and M. Lavine, "sFlow version 5,"
http://www.sflow.org/sflow_version_5.txt, July 2004.
[sFlow-LAG] Phaal, P. and A. Ghanwani, "sFlow LAG counters
structure," http://www.sflow.org/sflow_lag.txt, September 2012.
[STT] Davie, B. (Ed.) and J. Gross, "A Stateless Transport Tunneling
Protocol for Network Virtualization (STT)," draft-davie-stt-06, March
2014.
[RFC 7348] Mahalingam, M. et al., "VXLAN: A Framework for Overlaying
Virtualized Layer 2 Networks over Layer 3 Networks," August 2014.
[YONG] Yong, L., "Enhanced ECMP and Large Flow Aware Transport,"
draft-yong-pwe3-enhance-ecmp-lfat-01, September 2010.
Appendix A. Internet Traffic Analysis and Load Balancing Simulation
Internet traffic [CAIDA] has been analyzed to obtain flow statistics
such as the number of packets in a flow and the flow duration. The
five tuples in the packet header (IP addresses, TCP/UDP Ports, and IP
protocol) are used for flow identification. The analysis indicates
that < ~2% of the flows take ~30% of total traffic volume while the
rest of the flows (> ~98%) contributes ~70% [YONG].
The simulation has shown that given Internet traffic pattern, the
hash-based technique does not evenly distribute the flows over ECMP
paths. Some paths may be > 90% loaded while others are < 40% loaded.
The more ECMP paths exist, the more severe the misbalancing. This
implies that hash-based distribution can cause some paths to become
congested while other paths are underutilized [YONG].
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The simulation also shows substantial improvement by using the large
flow-aware hash-based distribution technique described in this
document. In using the same simulated traffic, the improved
rebalancing can achieve < 10% load differences among the paths. It
proves how large flow-aware hash-based distribution can effectively
compensate the uneven load balancing caused by hashing and the
traffic characteristics [YONG].
Authors' Addresses
Ram Krishnan
Brocade Communications
San Jose, 95134, USA
Phone: +1-408-406-7890
Email: ramkri123@gmail.com
Lucy Yong
Huawei USA
5340 Legacy Drive
Plano, TX 75025, USA
Phone: +1-469-277-5837
Email: lucy.yong@huawei.com
Anoop Ghanwani
Dell
San Jose, CA 95134
Phone: +1-408-571-3228
Email: anoop@alumni.duke.edu
Ning So
Tata Communications
Plano, TX 75082, USA
Phone: +1-972-955-0914
Email: ning.so@tatacommunications.com
Bhumip Khasnabish
ZTE Corporation
New Jersey, 07960, USA
Phone: +1-781-752-8003
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Email: vumip1@gmail.com
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