Internet DRAFT - draft-krishnan-opsawg-large-flow-load-balancing
draft-krishnan-opsawg-large-flow-load-balancing
OPSAWG R. Krishnan
Internet Draft S. Khanna
Intended status: Informational Brocade Communications
Expires: October 2013 L. Yong
April 24, 2013 Huawei USA
A. Ghanwani
Dell
Ning So
Tata Communications
B. Khasnabish
ZTE Corporation
Mechanisms for Optimal LAG/ECMP Component Link Utilization in
Networks
draft-krishnan-opsawg-large-flow-load-balancing-08.txt
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Abstract
Demands on networking infrastructure are growing exponentially; the
drivers are bandwidth hungry rich media applications, inter-data
center communications, etc. In this context, it is important to
optimally use the bandwidth in wired networks that extensively use
LAG/ECMP techniques for bandwidth scaling. This draft explores some
of the mechanisms useful for achieving this.
Table of Contents
1. Introduction...................................................3
1.1. Acronyms..................................................3
1.2. Terminology...............................................4
2. Hash-based Load Distribution in LAG/ECMP.......................4
3. Mechanisms for Optimal LAG/ECMP Component Link Utilization.....5
3.1. Large Flow Recognition....................................7
3.1.1. Flow Identification..................................7
3.1.2. Criteria for Identifying a Large Flow................8
3.1.3. Sampling Techniques..................................8
3.1.4. Automatic Hardware Recognition.......................9
3.2. Load Re-balancing Options................................10
3.2.1. Alternative Placement of Large Flows................10
3.2.2. Redistributing Small Flows..........................11
3.2.3. Component Link Protection Considerations............11
3.2.4. Load Re-Balancing Example...........................12
4. Information Model for Flow Re-balancing.......................13
4.1. Configuration Parameters.................................13
4.2. Import of Flow Information...............................13
5. Operational Considerations....................................14
6. IANA Considerations...........................................14
7. Security Considerations.......................................15
8. Acknowledgements..............................................15
9. References....................................................15
9.1. Normative References.....................................15
9.2. Informative References...................................15
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1. Introduction
Networks extensively use LAG/ECMP techniques for capacity scaling.
Network traffic can be predominantly categorized into two traffic
types: long-lived large flows and other flows (which include long-
lived small flows, short-lived small/large flows). Stateless hash-
based techniques [ITCOM, RFC 2991, RFC 2992, RFC 6790] are often used
to distribute both long-lived large flows and other 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 best practices for optimal LAG/ECMP component
link utilization while using hash-based techniques. These best
practices comprise the following steps -- recognizing long-lived
large flows in a router; and assigning the long-lived large flows to
specific LAG/ECMP component links or redistributing other flows when
a component link on the router is congested.
It is useful to keep in mind that the typical use case is where the
long-lived 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
order of 10's or 100's per link. In other words, the number of long-
lived large flows is NOT expected to be on the order of millions of
flows. Examples of such long-lived large flows would be IPSec
tunnels in service provider backbones or storage backup traffic in
data center networks.
1.1. Acronyms
COTS: Commercial Off-the-shelf
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
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QoS: Quality of Service
STT: Stateless Transport Tunneling
TCAM: Ternary Content Addressable Memory
VXLAN: Virtual Extensible LAN
1.2. Terminology
Large flow(s): long-lived large flow(s)
Small flow(s): long-lived small flow(s) and short-lived small/large
flow(s)
2. Hash-based Load Distribution in LAG/ECMP
Hashing techniques are often used for traffic load balancing to
select among multiple available paths with LAG/ECMP. The advantages
of hash-based 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 on these fields is used to generate a unique number that
identifies a link/path in a LAG/ECMP. The result of the hashing
procedure is a many-to-one mapping of flows to component links.
If the traffic load 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, the hash-based algorithm
produces 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 unbalanced loads on
individual component links.
One example is illustrated in Figure 1. In the figure, 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
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need to be distributed across the links in this LAG. The result of
hashing 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.
+-----------+ +-----------+
| | -> -> | |
| |=====> | |
| (1)|--/---/-|(1) |
| | | |
| | | |
| (R1) |-> -> ->| (R2) |
| (2)|--/---/-|(2) |
| | | |
| | -> -> | |
| |=====> | |
| |=====> | |
| (3)|--/---/-|(3) |
| | | |
+-----------+ +-----------+
Where: ->-> small flows
===> large flow
Figure 1: Unevenly Utilized Component Links
This document presents improved load distribution techniques based on
the large flow awareness. The techniques compensate for unbalanced
load distribution resulting from hashing as demonstrated in the above
example.
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3. Mechanisms for Optimal LAG/ECMP Component Link Utilization
The suggested techniques 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 globally optimal 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
network for traffic from other networks that are not under the
control of the same operator.
The various steps in achieving optimal 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 3.1.
Step 2) The egress component links are periodically scanned for link
utilization. If the egress component link utilization exceeds a pre-
programmed threshold, an operator alert is generated. The large flows
mapped to the congested egress component link are exported to a
central management entity.
Step 3) On receiving the alert about the congested component link,
the operator, through a central management entity, finds the large
flows mapped to that component link and the LAG/ECMP group to which
the component link belongs.
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Step 4) The operator can choose to rebalance the large flows on
lightly loaded component links of the LAG/ECMP group or redistribute
the small flows on the congested link to other component links of the
group. The operator, through a central management entity, can choose
one of the following actions:
1) Indicate specific large flows to rebalance;
2) Have the router decide the best large flows to rebalance;
3) Have the router redistribute all the small flows on the
congested link to other component links in the group.
The central management entity conveys the above information to the
router. The load re-balancing options are explained in Section 3.2.
Steps 2) to 4) could be automated if desired.
Providing large flow information to a central management entity
provides the capability to further optimize flow distribution at with
multi-node visibility. 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 P1 may be congested, while P2 and P3 may be
under-utilized, which 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 P3 resulting in a
more optimized flow of traffic.
The techniques described above are especially useful when bundling
links of different bandwidths for e.g. 10Gbps and 100Gbps as
described in [I-D.ietf-rtgwg-cl-requirement].
3.1. Large Flow Recognition
3.1.1. 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
from the following list:
. Layer 2: source MAC address, destination MAC address, VLAN ID.
. IP header: IP Protocol, IP source address, IP destination
address, flow label (IPv6 only), TCP/UDP source port, TCP/UDP
destination port.
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. MPLS Labels.
For tunneling protocols like GRE, VXLAN, NVGRE, STT, etc., flow
identification is possible based on inner and/or outer headers. The
above list is not exhaustive. The mechanisms described in this
document are agnostic to the fields that are used for flow
identification.
3.1.2. Criteria for Identifying a Large Flow
From a bandwidth and time duration perspective, in order to identify
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 in a router 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 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 identify a large flow are described below.
3.1.3. 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 identification, sampling must be
enabled on all of the egress ports in the router where such
measurements are desired.
Using sflow as an example, processing in an 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.
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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
immediately downstream node to make measurements for egress traffic
at the local node. This 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 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.
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.
With sampling, the detection of large flows can be done on the order
of one second [DevoFlow].
3.1.4. Automatic Hardware Recognition
Implementations may perform automatic recognition of large flows in
hardware on a router. Since this is done in hardware, it is an inline
solution and would be expected to operate at line rate.
Using automatic hardware recognition of large flows, a faster
indication of large flows mapped to each of the component links in a
LAG/ECMP group is available (as compared to the sampling approach
described above).
The advantages and disadvantages of automatic hardware recognition
are:
Advantages:
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. Large flow detection is offloaded to hardware freeing up
software resources and possible dependence on an external
management station.
. 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 automatic hardware
recognition, large flows can be detected in shorter windows on
higher link speeds since every packet is accounted for in
hardware [NDTM]
Disadvantages:
. Not supported in many routers.
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 of automatic hardware recognition of large flows
is vendor dependent and beyond the scope of this document.
3.2. Load Re-balancing Options
Below are suggested techniques for load re-balancing. Equipment
vendors should implement all of these techniques and allow the
operator to choose one or more techniques based on their
applications.
Note that regardless of the method used, perfect re-balancing 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.
3.2.1. 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 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 unbalanced utilization
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once again across the link components. 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 4 10 Gbps component
links and there are 4 large flows each of 1 Gbps. These flows are
each placed on one of the component links. Subsequent, a 5-th large
flow of 2 Gbps is recognized and to maintain equitable load
distribution, it may require placement of one 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.
3.2.2. 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 3.2.1) 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.
3.2.3. 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
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reserved link. Likewise, the LAG/ECMP hash table replaces the failed
component link with the reserved link.
3.2.4. Load Re-Balancing Example
Optimal LAG/ECMP component utilization for the use case in Figure 1
is depicted below in Figure 2. The large flow rebalancing explained
in Section 3.2.1 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 flows
===> large flow
Figure 2: Evenly utilized Composite Links
Basically, the use of the mechanisms described in Section 3.2.1
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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4. Informat ion Model for Flow Re-balancing
4.1. Configuration Parameters
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: the difference between the utilization of
the least utilized and most utilized component links. Expressed
as a percentage of link speed.
4.2. Import of Flow Information
In cases where large flow recognition is handled by an external
management station (see Section 3.1.3), an information model for
flows is required to allow the import of large flow information to
the router.
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The following are some of the elements of information model for
importing of flows:
. Layer 2: source MAC address, destination MAC address, VLAN ID.
. Layer 3 IP: IP Protocol, IP source address, IP destination
address, flow label (IPv6 only), TCP/UDP source port, TCP/UDP
destination port.
. MPLS Labels.
This list is not exhaustive. For example, with overlay protocols
such as VXLAN and NVGRE, fields from the outer and/or inner headers
may be specified. In general, all fields in the packet that can be
used by forwarding decisions should be available for use when
importing flow information from an external management station.
5. Operational Considerations
Flows should be re-balanced only when the imbalance in the
utilization across component links exceeds a certain threshold.
Frequent re-balancing 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 re-distributed.
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. IANA Considerations
This memo includes no request to IANA.
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7. 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.
8. Acknowledgements
The authors would like to thank the following individuals for their
review and valuable feedback on earlier versions of this document:
Shane Amante, Curtis Villamizar, Fred Baker, Wes George, Brian
Carpenter, George Yum, Michael Fargano, Michael Bugenhagen, Jianrong
Wong, Peter Phaal, Roman Krzanowski and Weifeng Zhang.
9. References
9.1. Normative References
9.2. Informative References
[I-D.ietf-rtgwg-cl-requirement] Villamizar, C. et al., "Requirements
for MPLS over a Composite Link", June 2012.
[RFC 6790] Kompella, K. et al., "The Use of Entropy Labels in MPLS
Forwarding", November 2012.
[CAIDA] Caida Internet Traffic Analysis, http://www.caida.org/home.
[YONG] Yong, L., "Enhanced ECMP and Large Flow Aware Transport",
draft-yong-pwe3-enhance-ecmp-lfat-01, September 2010.
[ITCOM] Jo, J., et al., "Internet traffic load balancing using
dynamic hashing with flow volume", SPIE ITCOM, 2002.
[RFC 2991] Thaler, D. and C. Hopps, "Multipath Issues in Unicast and
Multicast", November 2000.
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[RFC 2992] Hopps, C., "Analysis of an Equal-Cost Multi-Path
Algorithm", November 2000.
[RFC 5475] Zseby, T., et al., "Sampling and Filtering Techniques for
IP Packet Selection", March 2009.
[sFlow-v5] Phaal, P. and M. Lavine, "sFlow version 5", July 2004.
[sFlow-LAG] Phaal, P. and A. Ghanwani, "sFlow LAG counters
structure", September 2012.
[RFC 3954] Claise, B., "Cisco Systems NetFlow Services Export Version
9", October 2004
[DevoFlow] Mogul, J., et al., "DevoFlow: Cost-Effective Flow
Management for High Performance Enterprise Networks", Proceedings of
the ACM SIGCOMM, August 2011.
[NDTM] Estan, C. and G. Varghese, "New directions in traffic
measurement and accounting", Proceedings of ACM SIGCOMM, August 2002.
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].
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
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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: ramk@brocade.com
Sanjay Khanna
Brocade Communications
San Jose, 95134, USA
Phone: +1-408-333-4850
Email: skhanna@brocade.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
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New Jersey, 07960, USA
Phone: +1-781-752-8003
Email: bhumip.khasnabish@zteusa.com
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