OPSAWG                                                      R. Krishnan
Internet Draft                                                S. Khanna
Intended status: Informational                   Brocade Communications
Expires: November December 23, 2013                                      L. Yong
May 8,
June 23, 2013                                                Huawei USA
                                                            A. Ghanwani
                                                                Ning So
                                                    Tata Communications
                                                          B. Khasnabish
                                                        ZTE Corporation

       Mechanisms for Optimal LAG/ECMP Component Link Utilization in



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   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. Flow Categorization............................................4
   3. Hash-based Load Distribution in LAG/ECMP.......................4
   3. LAG/ECMP.......................5
   4. Mechanisms for Optimal LAG/ECMP Component Link Utilization.....5
      3.1. Utilization.....7
      4.1. Differences in LAG vs ECMP................................7
      4.2. Overview of the mechanism.................................8
      4.3. Large Flow Recognition....................................7
         3.1.1. Recognition....................................9
         4.3.1. Flow Identification..................................7
         3.1.2. Identification..................................9
         4.3.2. Criteria for Identifying a Large Flow................8
         3.1.3. Flow...............10
         4.3.3. Sampling Techniques..................................8
         3.1.4. Techniques.................................10
         4.3.4. Automatic Hardware Recognition.......................9
      3.2. Recognition......................11
      4.4. Load Re-balancing Options................................10
         3.2.1. Options................................12
         4.4.1. Alternative Placement of Large Flows................10
         3.2.2. Flows................12
         4.4.2. Redistributing Small Flows..........................11
         3.2.3. Flows..........................13
         4.4.3. Component Link Protection Considerations............11
         3.2.4. Considerations............13
         4.4.4. Load Re-balancing Algorithms........................14
         4.4.5. Load Re-Balancing Example...........................12
   4. Example...........................14
   5. Information Model for Flow Re-balancing.......................13
      4.1. Re-balancing.......................15
      5.1. Configuration Parameters.................................13
      4.2. Import Parameters for Flow Re-balancing...........15
      5.2. System Configuration and Identification Parameters.......16
      5.3. Information for Alternative Placement of Large Flows.....16
      5.4. Information for Redistribution of Small Flows............17
      5.5. Export of Flow Information...............................13
   5. Operational Considerations....................................14 Information...............................17
      5.6. Monitoring information...................................17
         5.6.1. Interface (link) utilization........................17
         5.6.2. Other monitoring information........................17
   6. IANA Considerations...........................................14 Operational Considerations....................................18
   7. Security Considerations.......................................15 IANA Considerations...........................................18
   8. Acknowledgements..............................................15 Security Considerations.......................................18
   9. References....................................................15
      9.1. Acknowledgements..............................................18
   10. References...................................................19
      10.1. Normative References.....................................15
      9.2. References....................................19
      10.2. Informative References...................................15 References..................................19

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 mechanisms for optimal LAG/ECMP component link
   utilization while using hash-based techniques. These best
   practices The mechanisms
   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

   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

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 Size
         |                    |                    |
   Large |      SLLF          |       LLLF         |
   Flow  |                    |                    |
         |                    |                    |
   Small |      SLSF          |       LLSF         |
   Flow  |                    |                    |
         +--------------------+--------------------+---> Flow duration
              Short-Lived            Long-Lived
              Flow                   Flow

       Figure 1: Flow Categorization

In this document, we categorize Long-lived large flow(s) as "Large"
flow(s), and all of the others -- Long-lived small flow(s) and short-
lived small/large flow(s) as "Small" flow(s).

3. 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. 2.  In the figure, 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
   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

     .  Component link (3) has 4 flows -- 2 small flows and 2 large
        flows -- and the link capacity is exceeded resulting in

          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: 2: 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


4. 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

4.1. Differences in networks LAG vs ECMP

   While the mechanisms explained herein are detailed below:

   Step 1) applicable to both LAGs and
   ECMP, it is useful to note that there are some key differences
   between the two that may impact how effective the mechanism is.  This involves large flow recognition
   relates, in routers and
   maintaining part, to the mapping of localized information with which the large flow scheme
   is intended to operate.

   A LAG is almost always between 2 adjacent routers.  As a result, the component link that
   it uses. The recognition
   scope of large flows is explained in Section 3.1.

   Step 2) The egress component links are periodically scanned for link
   utilization. If problem of optimizing the egress component link bandwidth utilization exceeds a pre-
   programmed threshold, an operator alert is generated. The large flows
   mapped to on the congested egress
   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.)

   To demonstrate the limitations of local optimization, consider a two-
   level fat-tree topology with three leaf nodes (L1, L2, L3) and two
   spine nodes (S1, S2) and 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.

4.2. Overview of the mechanism

   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.

   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].


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
   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.

     .  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


4.3.2. 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.


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 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 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
   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

   The advantages and disadvantages of sampling techniques are as


     .  Supported in most existing routers.

     .  Requires minimal router resources.


     .  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].


4.3.4. 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


     .  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]


     .  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.


4.4. 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

   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.


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 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
   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.


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 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


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 hash table replaces the failed
   component link with the reserved link.


4.4.4. Load Re-balancing 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-

4.4.5. Load Re-Balancing Example

   Optimal LAG/ECMP component utilization for the use case in Figure 1 2
   is depicted below in Figure 2. 3. The large flow rebalancing explained
   in Section 3.2.1 is used. The improved link utilization is as

     .  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: 3: 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.


5. Information Model for Flow Re-balancing


5.1. Configuration Parameters for Flow Re-balancing

   The following parameters are required the configuration of this

     .  Large flow recognition parameters. 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

     .  Rebalancing interval: the minimum amount of Flow time between
        rebalancing events.  This parameter ensures that rebalancing is
        not invoked too frequently as it impacts frame ordering.

   These parameters may be configured on a system-wide basis or it may
   apply to an individual LAG.

5.2. System Configuration and Identification Parameters

     .  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. 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.
        This is required when specifying flow placement to achieve the
        desired rebalancing.

5.3. Information for Alternative Placement of Large Flows

   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.

   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.


   The IPFIX information model [RFC 5101] can be leveraged for large
   flow identification.  The component link ID would be used to specify
   the target component link for the flow.

5.4. Information for Redistribution of Small Flows

   For small flows, the LAG ID and the component link IDs along with the
   percentage of traffic to be assigned to each component link ID Is

5.5. Export of Flow Information

   Exporting flow information is required when large flow identification
   is being done on a router, but the decision to rebalance is being
   made in an external management station.

   It is recommended to use IPFIX protocol [RFC 5101] for exporting of
   large flows from the router to an external management station.

5.6. Monitoring information

5.6.1. Interface (link) utilization

   The incoming bytes (ifInOctets), outgoing bytes (ifOutOctets) and
   interface speed (ifSpeed) can be measured from the Interface table
   (iftable) MIB [RFC 1213].

   The link utilization can then be computed as follows:

   Incoming link utilization = (ifInOctets *8 / ifSpeed)

   Outgoing link utilization = (ifOutOctets * 8 / ifSpeed)

   For high speed links, the etherStatsHighCapacityTable MIB [RFC 3273]
   can be used.

   The outgoing link utilization of the component links within a LAG can
   be used to compute the imbalance threshold (See Section 5.1) for the

5.6.2. Other monitoring information

   Additional monitoring information includes:

     .  Number of times rebalancing was done.

     .  Time since the last rebalancing event.

6. 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.  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

   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.


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


9. 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, Weifeng Zhang, Pete Moyer,
   Mallis, Malis, Dave Mcdysan and McDysan, Zhen Cao

9. Cao, and Dan Romascanu.

10. References


10.1. Normative References


10.2. Informative References

   [I-D.ietf-rtgwg-cl-requirement] Villamizar, C. et al., "Requirements
   for MPLS over a Composite Link", June 2012. Link," September 2013.

   [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", 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", volume," SPIE ITCOM, 2002.

   [RFC 2991] Thaler, D. and C. Hopps, "Multipath Issues in Unicast and
   Multicast," November 2000.

   [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", Selection," March 2009.

   [sFlow-v5] Phaal, P. and M. Lavine, "sFlow version 5", 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

   [RFC 5101] Claise, B., "Specification of the IP Flow Information
   Export (IPFIX) Protocol for the Exchange of IP Traffic Flow
   Information," January 2008

   [RFC 1213] McCloghrie, K., "Management Information Base for Network
   Management of TCP/IP-based internets: MIB-II," March 1991.

   [RFC 3273] Waldbusser, S., "Remote Network Monitoring Management
   Information Base for High Capacity Networks," July 2002.

   [DevoFlow] Mogul, J., et al., "DevoFlow: Cost-Effective Flow
   Management for High Performance Enterprise Networks", Networks," Proceedings of
   the ACM SIGCOMM, August 2011.

   [Bloom] Bloom, B. H., "Space /Time Trade-offs in Hash Coding with
   Allowable Errors", Communications of the ACM, July 1970.

   [NDTM] Estan, C. and G. Varghese, "New directions in traffic
   measurement and accounting", accounting," Proceedings of ACM SIGCOMM, August 2002.

   [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.

   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
   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
   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
   Email: bhumip.khasnabish@zteusa.com