OPSAWG R. Krishnan Internet Draft S. Khanna Intended status: Informational Brocade Communications Expires: August 12, 2013 L. Yong February 13, 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-03.txt Status of this Memo This Internet-Draft is submitted in full conformance with the provisions of BCP 78 and BCP 79. Internet-Drafts are working documents of the Internet Engineering Task Force (IETF), its areas, and its working groups. Note that other groups may also distribute working documents as Internet- Drafts. Internet-Drafts are draft documents valid for a maximum of six months and may be updated, replaced, or obsoleted by other documents at any time. It is inappropriate to use Internet-Drafts as reference material or to cite them other than as "work in progress." The list of current Internet-Drafts can be accessed at http://www.ietf.org/ietf/1id-abstracts.txt The list of Internet-Draft Shadow Directories can be accessed at http://www.ietf.org/shadow.html This Internet-Draft will expire on August 13, 2013. Copyright Notice Copyright (c) 2013 IETF Trust and the persons identified as the document authors. All rights reserved. This document is subject to BCP 78 and the IETF Trust's Legal Provisions Relating to IETF Documents (http://trustee.ietf.org/license-info) in effect on the date of Krishnan Expires August 13, 2013 [Page 1] Internet-Draft Optimal Load Distribution over LAG/ECMP February 2013 publication of this document. Please review these documents carefully, as they describe your rights and restrictions with respect to this document. Code Components extracted from this document must include Simplified BSD License text as described in Section 4.e of the Trust Legal Provisions and are provided without warranty as described in the Simplified BSD License. This document is subject to BCP 78 and the IETF Trust's Legal Provisions Relating to IETF Documents (http://trustee.ietf.org/license-info) in effect on the date of publication of this document. Please review these documents carefully, as they describe your rights and restrictions with respect to this document. 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. Conventions...............................................3 1.2. Acronyms..................................................4 1.3. Terminology...............................................4 2. Hash-based Load Distribution in LAG/ECMP.......................4 3. Mechanisms for Optimal LAG/ECMP Component Link Utilization.....6 3.1. Large Flow Recognition....................................8 3.1.1. Flow Identification..................................8 3.1.2. Criteria for Identifying a Large Flow................8 Krishnan Expires August 13, 2013 [Page 2] Internet-Draft Optimal Load Distribution over LAG/ECMP February 2013 3.1.3. Sampling Techniques..................................9 3.1.4. Automatic Hardware Recognition.......................9 3.2. Load Re-Balancing Options................................11 3.2.1. Alternative Placement of Large Flows................11 3.2.2. Redistributing Small flows..........................11 3.2.3. Component Link Protection Considerations............12 3.2.4. Load Re-Balancing Example...........................12 4. Future Work...................................................13 5. IANA Considerations...........................................14 6. Security Considerations.......................................14 7. Acknowledgements..............................................14 8. References....................................................14 8.1. Normative References.....................................14 8.2. Informative References...................................14 9. Appendix A. Internet Traffic Analysis and Load Balancing Simulation.......................................................15 10. Appendix B. Techniques for Automatic Hardware Recognition...16 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. 1.1. Conventions The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this document are to be interpreted as described in RFC 2119 [RFC2119]. Krishnan Expires August 13, 2013 [Page 3] Internet-Draft Optimal Load Distribution over LAG/ECMP February 2013 1.2. 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 1.3. 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 Krishnan Expires August 13, 2013 [Page 4] Internet-Draft Optimal Load Distribution over LAG/ECMP February 2013 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 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. Krishnan Expires August 13, 2013 [Page 5] Internet-Draft Optimal Load Distribution over LAG/ECMP February 2013 +-----------+ +-----------+ | | -> -> | | | |=====> | | | (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. 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: Krishnan Expires August 13, 2013 [Page 6] Internet-Draft Optimal Load Distribution over LAG/ECMP February 2013 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. 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. Krishnan Expires August 13, 2013 [Page 7] Internet-Draft Optimal Load Distribution over LAG/ECMP February 2013 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. . 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 Krishnan Expires August 13, 2013 [Page 8] Internet-Draft Optimal Load Distribution over LAG/ECMP February 2013 programmable in a router to facilitate handling of different use cases and traffic characteristics. For example, a flow which is at or above 100 Mbps for a time period of at least 5 minutes could be declared a large flow. 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. For example, through sFlow processing in a sFlow collector, an approximate indication of the large flows mapping to each of the component links in each LAG/ECMP group is 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. The advantages and disadvantages of sampling techniques are as follows. Advantages: . Supported in most routers. . Requires minimal router resources. Disadvantages: . There is a delay in the recognition time for 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 to a few seconds [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. Krishnan Expires August 13, 2013 [Page 9] Internet-Draft Optimal Load Distribution over LAG/ECMP February 2013 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: . Accurate and performed in real-time. 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. Below is a suggested technique. This technique requires a few tables -- a flow table, and multiple hash tables. The flow table comprises entries which are programmed with packet fields for flows that are already known to be large flows and each entry has a corresponding byte counter. It is initialized as an empty table (i.e. none of the incoming packets would match a flow table entry). The hash tables each have a different hash function and comprise entries which are byte counters. The counters are initialized to zero and would be modified as described by the algorithm below. Step 1) If the large flow exists in the flow table (for e.g. TCAM), increment the counter associated with the flow by the packet size. Else, proceed to Step 2. Step 2) The hash function for each table is applied to the fields of the packet header and the result is looked up in parallel in corresponding hash table and the associated counter corresponding to the entry that is hit in that table is incremented by the packet size. If the counter exceeds a programmed byte threshold in the observation interval (this counter threshold would be set to match the bandwidth threshold) in the entries that were hit in all of the Krishnan Expires August 13, 2013 [Page 10] Internet-Draft Optimal Load Distribution over LAG/ECMP February 2013 hash tables, a candidate large flow is learnt and programmed in the flow table and the counters are reset. Additionally, the counters in all of the hash tables must be reset every observation interval. There may be some false positives due to multiple small flows masquerading as a large flow. The number of such false positives is reduced by increasing the number of parallel hash tables using different hash functions. There will be a design tradeoff between size of the hash tables, the number of hash tables, and the probability of a false positive. More details on this algorithm are found in Appendix B. 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. 3.2.1. Alternative Placement of Large Flows In the LAG/ECMP group, choose other member component links with least average port utilization. Move some large flow(s) from the heavily loaded component link to other member component links using a Policy Based Routing (PBR) rule in the ingress processing element(s) in the routers. The key aspects of this are: . Small flows are not subjected to flow re-ordering. . Only certain large flows are subjected to momentary flow re- ordering. Note that perfect re-balancing of large flows may not be possible since flows arrive and depart at different times. 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). Krishnan Expires August 13, 2013 [Page 11] Internet-Draft Optimal Load Distribution over LAG/ECMP February 2013 . 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. . Small flows may be subject to momentary packet re-ordering. . The PBR rules for large flows (refer to Section 3.2.1) must have strict precedence over the LAG/ECMP table lookup result. 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 which are described in Section 3.2. 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 reference pointer from the failed component link to the reserved link. Likewise, the LAG/ECMP hash table replaces the reference pointer from the failed component link to 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. Krishnan Expires August 13, 2013 [Page 12] Internet-Draft Optimal Load Distribution over LAG/ECMP February 2013 +-----------+ +-----------+ | | -> -> | | | |=====> | | | (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. 4. Future Work There are two areas that would benefit from further standards work: 1) Development of a data model used to move the large flow information from the router to the central management entity. 2) Development of a data model used to move the large flow re- balancing information from the central management entity to the router. Krishnan Expires August 13, 2013 [Page 13] Internet-Draft Optimal Load Distribution over LAG/ECMP February 2013 5. IANA Considerations This memo includes no request to IANA. 6. 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. 7. 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, and Peter Phaal. 8. References 8.1. Normative References [RFC2119] Bradner, S., "Key words for use in RFCs to Indicate Requirement Levels", BCP 14, RFC 2119, March 1997. [RFC2234] Crocker, D. and Overell, P. (Editors), "Augmented BNF for Syntax Specifications: ABNF", RFC 2234, Internet Mail Consortium and Demon Internet Ltd., November 1997. 8.2. 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. Krishnan Expires August 13, 2013 [Page 14] Internet-Draft Optimal Load Distribution over LAG/ECMP February 2013 [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. [RFC2991] Thaler, D. and C. Hopps, "Multipath Issues in Unicast and Multicast", November 2000. [RFC2992] Hopps, C., "Analysis of an Equal-Cost Multi-Path Algorithm", November 2000. [RFC5475] 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. 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 Krishnan Expires August 13, 2013 [Page 15] Internet-Draft Optimal Load Distribution over LAG/ECMP February 2013 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]. Appendix B. Techniques for Automatic Hardware Recognition B.1 Comparison of Single Hash vs Multiple Hash for Large Flow Identification The suggested multiple hash technique is scalable in terms of hash table storage and flow-table storage. This is independent of the quality of the hash functions. Scalability is an important consideration because there could be millions of flows only a small percentage of which are large flows which need to be examined. The amount of hash table storage is proportional to the number of large flows - the exact number would depend on the use cases and traffic characteristics. The amount of flow-table storage needed is only slightly more than the number of large flows; a little extra space is needed to accommodate any false positives (small flows which may have been incorrectly identified as large flows). Krishnan Expires August 13, 2013 [Page 16] Internet-Draft Optimal Load Distribution over LAG/ECMP February 2013 With a single hash, the hash table storage would be proportionate to the total number of flows in order to minimize false positives. The amount of flow-table storage needed depends on the total number flows and the hash table size. B.2 Steady state analysis of suggested multiple hash technique Objective: Determine the probability of short-lived flows masquerading as long- lived-flows Assumptions: The small flows are uniformly distributed over all of the hash buckets. Further, assume that the small flows have an identical number of packets in the observation interval. Notation: Number of hash stages - m Number of hash buckets per stage - n Minimum large flow rate (bytes/sec) - s Time interval of examination (sec) - t Number of small flows in time interval t - x1 Number of packets per small flow in time interval t - y Average packet size of small flow - z Average number of small flows in a hash bucket - x2 (x1/n) Minimum number of small flows in the same hash bucket that would lead to one small flow being incorrectly identified as a large flow - x3Basically, if we have some number of small flows hashing into a bucket, only those buckets which hit the minimum bandwidth threshold would trigger large flow identification and only the last small flow which triggered the event would be incorrectly identified as a large flow. Using the above notation, it would take at least x3 small flows hashing to the same bucket to trigger the minimum bandwidth threshold Krishnan Expires August 13, 2013 [Page 17] Internet-Draft Optimal Load Distribution over LAG/ECMP February 2013 for that bucket, and the flow to which the last packet belongs would be incorrectly identified as a large flow. x3 = (s*t)/(y*z) Let p1 be the probability of such an incorrectly identified large flow per hash stage. Let x be a variable denoting the number of small flows which hash to the same bucket and can take a value {0, 1, ..., x1}. As noted above, the mean value of x is x2. p1 = Prob(x >= x3) Overall probability with m independent hash stages - p1^m Thus, larger tables and more number of tables would lead to a lower probability of incorrectly identified large flows. An example: m = 4, n = 2K, s = 1 MB/sec, t = 1 sec, x1 = 200K, y = 10, z = 1K x2 = 200K/2K = 100 x3 = (1024*1024)/(10*1024) = 102.4 p1 = Prob (x >= x3) ~= 0.5 (x2 = 100 small flows fall into one hash bucket on the average) Overall probability with m independent hash stages is (p1)^m = (0.5)^4 = 0.0625 Thus, by having 4 stages, the probability of a small flow being incorrectly identified as a large flow is reduced from 0.5 to 0.0625. Authors' Addresses Ram Krishnan Brocade Communications San Jose, 95134, USA Phone: +1-408-406-7890 Email: ramk@brocade.com Sanjay Khanna Krishnan Expires August 13, 2013 [Page 18] Internet-Draft Optimal Load Distribution over LAG/ECMP February 2013 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 New Jersey, 07960, USA Phone: +1-781-752-8003 Email: bhumip.khasnabish@zteusa.com Krishnan Expires August 13, 2013 [Page 19]