Network Working Group Y. Lee Internet Draft F. Xia Huawei G. Bernstein Grotto Networking Intended status: Informational Expires: January 2011 July 4, 2010 Problem Statement for Cross-Layer Optimization draft-lee-cross-layer-optimization-problem-00.txt Status of this Memo This Internet-Draft is submitted to IETF 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." 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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. Abstract Due to the lack of layer interaction between networked applications and the network during service provisioning, many application services can make poor use of network resources or not achieve their overall quality of service objectives. This document describes the general problem of cross layer optimization. Cross-layer optimization (CLO) involves the overall optimization of application layer and network resources by providing an interface for interactions and exchanges between the two layers. The potential gains of cross layer optimization are illustrated via examples from content delivery systems, video on demand systems, and grid computing. Table of Contents 1. Introduction......................................... 2 1.1. Terminology and Glossary........................... 3 1.2. Application Resources and Service Profile............. 4 1.3. Network Capabilities.............................. 5 2. Network Application Examples ........................... 6 2.1. File Distribution Systems.......................... 6 2.2. Streaming Content Distribution Systems............... 7 2.3. Conferencing and Gaming ........................... 8 2.4. Grid Computing................................... 9 3. Problem Statement and Opportunities...................... 9 3.1. Topology Related Processes........................ 10 3.2. Load and Traffic Adaptive Processes................. 11 3.3. Provisioning Processes........................... 11 4. Security Considerations............................... 12 5. References......................................... 12 Author's Addresses..................................... 15 Intellectual Property Statement .......................... 15 Disclaimer of Validity.................................. 16 1. Introduction Application layer services by their very nature utilize application layer resources, and the underlying network resources. Application layer services can involve a variety of application layer resources Lee & Xia & Bernstein Expires January 4, 2011 [Page 2] Internet-Draft Cross-Layer Optimization (CLO) July 2010 such as data storage, computation, specialized server capabilities, and large data sets. However, the provisioning of network applications typically includes minimal or no information about the state of underlying network resources. For example if an application client can obtain a desired large data set (file, video, database, etc...) from a choice of many different servers, the application service will take into account the current status and load on the servers but only minimal network considerations, e.g., topological proximity, connectivity, ping latency, rather than current link bandwidth utilization or other quality of service parameters (e.g., delay and jitter). In addition application services may make significant demands on network resources such as bandwidth and may have a variety of quality of service requirements. Due to the lack of layer interaction between networked applications and the network during service provisioning, many application services can make poor use of network resources or not achieve their overall quality of service objectives. This document describes the general problem of cross layer optimization. Cross-layer optimization (CLO) involves the overall optimization of application layer and network resources by providing an interface for interactions and exchanges between the two layers. The potential gains of cross layer optimization are illustrated via examples from content delivery systems, video on demand systems, and grid computing. 1.1. Terminology and Glossary Application Layer -- The highest layer in the OSI or TCP/IP protocol models. Application Profile -- The characteristics of the application from a network traffic perspective and the QoS requirements that the application service will require from the network. Application Resources -- Non-network resources critical to achieving the application service functionality. Examples include: caches, mirrors, application specific servers, content, large data sets, etc... Application Service -- A networked application offered to a variety of clients. Network Layer - All layers below and including layer 3 in the OSI protocol model that can contribute to meeting the requirements of an application service. This includes MPLS and GMPLS controlled networks. Lee & Xia & Bernstein Expires January 4, 2011 [Page 3] Internet-Draft Cross-Layer Optimization (CLO) July 2010 Network Resources -- Any layer 3 or below resources such as bandwidth, connections, links, connection processing, etc... 1.2. Application Resources and Service Profile Current and emerging application resources can be grouped into a number of categories as follows: o Live data sources -- such as video or audio from live sporting or entertainment events, data feeds from radio telescopes used in very long baseline interferometry, large particle physics experiments such as the LHC, etc... o Processing Resources -- such as raw computational capability for cloud computing, transactional capabilities for e-commerce, transcoding capabilities for video and audio, etc... o Storage Resources -- disk farms, tape libraries, etc... o Content/Data Sets -- video, audio, commercial, scientific, etc... These application resources may be distributed around a network from each other or from users/clients. The scope of a network application can be within a building, within an enterprise, within an autonomous system or distributed amongst multiple autonomous systems. Application profile defines the characteristics of the application from a network traffic perspective and the QoS requirements that the application service will require from the network. Each application is associated with the sources from which the application resources originate and the consuming locations (e.g., client locations) of the application resources. Application service profiles can be characterized by the following categories: o Location profile: locations of both the clients and the sources o QoS profile: (i) Delay Intolerant; (ii) Jitter Intolerant; (iii) Packet Drop Sensitive o Connectivity profile: (i) P-P; (ii) P-MP; (iii) MP-MP; (iv) Any cast, etc. o Directionality profile: (i) uni-directional; (ii) bi-directional o Bandwidth profile: bandwidth required for the connectivity o Duration of service profile: service time of the application Lee & Xia & Bernstein Expires January 4, 2011 [Page 4] Internet-Draft Cross-Layer Optimization (CLO) July 2010 1.3. Network Capabilities Depending on the application, its nature, and related quality of service, the underlying network is required to have different capabilities. For our purposes here, network resources and capabilities can be categorized as follows: o Bandwidth guarantee --- the ability of the network to make bandwidth guarantee for the application service. o QoS and SLA -- the ability of the network to deliver a given amount of QoS and live up to service level agreements on that QoS. Typical QoS may involve delay, jitter, maximum single incident outage time, yearly total outage time, etc... o Configurability -- the ability to reconfigure/reoptimize various aspects of the network and the timeliness in which changes can occur. o Adaptability --- the ability to adapt changes due to changes of service demand or application/network congestion/failure. The ability to optimize the utilization of both application layer resources and network resources while meeting service goals will be highly dependent on the nature of the application and the properties of the network. However, there is basic information that could be exchanged across application and network layers, and possible configuration agility that could apply to a wide variety of network applications. The following is a non-exhaustive list of some underlying network types over which application services are transported and the information that could be shared to promote cross layer optimization: 1. Raw best effort IP network: network resource related location information of clients and application resources is of prime interest; some notions of available bandwidth could be helpful for example some applications could make use of information on time of day (TOD) variations for scheduling. 2. Raw best effort IP network with tunable weights: In the intra- domain case traffic variations have sometimes been accommodated with the variation of IGP weights [REF]. For network applications with a significant and somewhat predictable load such techniques could be beneficial in addition to basic network resource related location information as mentioned above. Lee & Xia & Bernstein Expires January 4, 2011 [Page 5] Internet-Draft Cross-Layer Optimization (CLO) July 2010 3. Diff-Serv capable IP network: filtering and PHB could be adjusted based on network application needs. Server choices could be influenced by existing "bandwidth allocations". Previously mentioned techniques could also be applied. 4. MPLS-TE and/or GMPLS enabled networks: These networks are designed to provide configurability and bandwidth/QoS guarantees. For the application types that require stringent bandwidth/QoS requirements, these networks are well suited for such cross layer optimization. Section 2 describes some major classes of network applications and the cross layer optimization opportunities they can present. Section 3 offers a problem statement based on basic processes and interfaces, and then describe how this work relates to other work within the IETF and other standard development organizations (SDOs). 2. Network Application Examples We group application examples by increasing complexity in terms of resource optimization and quality of service (profile) requirements. In the following we look at file distribution systems, streaming content distribution systems (live and on-demand), and grid computing applications. 2.1. File Distribution Systems File distribution systems began by accelerating the download of web pages, particularly those with images, and expanded to include software, audio and, video file delivery. These are also known as content distribution systems, but we will use the name file distribution system to emphasize that these are concerned with the transfer of entire files and are not concerned with streaming services (covered in the next section). As such these applications have the fewest network QoS requirements. Goals of these system include reduction of latency to clients, offloading originating server, and conservation of network resources. Such systems have been set up as overlays on existing network infrastructures. Commonly encountered optimization problems with network implications include: o Cache and Mirror placement problem o Efficient transfer of content to servers o Client to server assignment problem Lee & Xia & Bernstein Expires January 4, 2011 [Page 6] Internet-Draft Cross-Layer Optimization (CLO) July 2010 The cache placement problem is concerned with what content to allocate to which cache servers based on their proximity to clients and their loading [Cache]. Mirrors differ from caches in that a client is only directed to a mirror if it has the desired content [Mirror]. The mirror or server replica placement problem is concerned with where to place a number of server given a fixed number of possible sites [Mirror],[Replica]. Depending upon the relationship between the file distribution service provider and the network provider the cache assignment problem comes in two flavors, a general problem formulation with relatively arbitrary server placement and a specific formulation for Transparent En-Route Caches which are placed along the route from the client to the originating file server and work transparently from the client perspective [Cache]. All the processing placement optimizations work with some type of network topological information, e.g., relative link cost network models. However, exact network models are not always necessary to achieve significant performance improvements [Topo]. The efficient transport of the original content to the "replication" servers may be important when the amount of material becomes large. We will revisit this issue in the grid computing applications section. In assignment or selection of a content server for a particular client one would ideally take into account both current server load and network latency between client and server [Topo]. In the streaming case we will also need to worry more about bandwidth and QoS. 2.2. Streaming Content Distribution Systems Steaming services come in two basic flavors, live and on-demand. In addition many variants in between these two extremes are created when pause or replay functionality is included in a live streaming service. Streaming services are different from file download in a number of ways. First, the commencement of content consumption does not require an entire file to be downloaded. Second minimum bandwidth and QoS requirements are needed between the client and the server to render such services viable. Hence such services have a non-trivial service profile. By "live streaming" here we mean that the client is willing to receive the stream at its current play out point rather than at some pre-existing start point. A key network issue for live streaming services is whether multi-casting takes place at the application or network level. For example in carrier operated IPTV networks IP Lee & Xia & Bernstein Expires January 4, 2011 [Page 7] Internet-Draft Cross-Layer Optimization (CLO) July 2010 multi-casting is beginning to be used [IPTV]. In the case of an independent live video distribution service, one may make use of an overlay network of servers that provide the multi-casting. Examples of optimization problems for a live streaming service include: o Server selection problem (application based multi-cast) or leaf attachment problem (network based multi-cast)[ServMulti] o Server placement problem (application based multi-cast) or tree construction (network based multi-cast). On-demand services provide additional technical challenges. Service providers wish to avoid long start up service delays to retain customers, while at the same time batch together requests to save on server costs. A number of additional optimization decisions and problems typically arise in the on-demand applications in addition to those seen in live streaming: o Client stream sharing technique o Batch or Multicast Server selection problem The on-demand streaming services as opposed to the live streaming services also has a set of problems similar to those seen in file distribution: (a) data allocation problem: when and where should we pre-stock video files, (b) on-demand server placement problem (where to put and how much capacity), and (c) efficient (cost effective and timely) transfer of content to servers. 2.3. Conferencing and Gaming When we look at the complexity of the overall application connectivity, video and audio conferencing take us from the point-to- multipoint scenario of streaming content distribution to a multipoint-to-multipoint situation. In addition, we see an additional hard QoS constraint on latency. Both conferencing and gaming are characterized by bi-directional connection and asymmetric bandwidth from/to the server location to/from the user location. Video and audio conferences may for non-technical reasons be limited in scope to a few handfuls of clients. Gaming applications, however, can push the scalability limits of both server and network technologies. Gaming, in particular massively multi-player online games (MMOGs), has the connectivity and QoS requirements of conferencing but many more issues related to the scale of application. Note that as a part of game play many gamers utilize audio conferencing services such as Lee & Xia & Bernstein Expires January 4, 2011 [Page 8] Internet-Draft Cross-Layer Optimization (CLO) July 2010 Ventrilo [VENT] and hence would generate well modeled audio conferencing traffic. Due to scalability concerns [GameServ] and player desires [MPSel], server selection can be more complicated than in the streaming content distribution case. In summary conferencing and gaming have optimization problems similar to those seen in file and streaming content distribution, but the scale of gaming, its latency requirements, and it revenue generating potential make it worthy of individual study for cross layer optimization. 2.4. Grid Computing Grid computing has requirements for large file transfer somewhat similar to our file distribution systems but most likely with reduced fanout but with much larger file sizes. In addition grid computing may have a "streaming" requirement similar to the streaming content distribution systems but again with significantly reduced fanout and sometimes extremely large bandwidth requirements. For example current estimates of the streaming traffic produce by one antenna in the proposed Square Kilometer Array (SKA) [SKA] is approximately 160Gbps with the array consisting of approximately 3000 antennas. Reference [GFD-122] details a number of grid use cases including visualization, large data streaming coordinated with job execution, High Energy Physics file replica management, health care, distributed manufacturing and maintenance, super computing, and Very Long Baseline Interferometery (radio astronomy). In some cases these applications run over shared research networks such as Internet2 [VLBI]. We note that some instantiations of grid computing produce problems very similar to those already discussed, others other push technology limits in terms of data rates and/or data set sizes and hence could benefit from the latest techniques such as GMPLS and its extensions for controlling very high speed network infrastructure [WSON]. 3. Problem Statement and Opportunities The previous examples show the benefits that can accrue when both application layer and network layer resources are jointly considered for optimization. The key missing piece in all these situations is an appropriate interface/architecture that could accommodate this joint optimization while meeting the business, technical and security constraints that may be inherent in the relationship between the application and network provider. Lee & Xia & Bernstein Expires January 4, 2011 [Page 9] Internet-Draft Cross-Layer Optimization (CLO) July 2010 ITU-T Y.2011 NGN and Y.2111 Resource and Admission Control Functions (RACF) discuss NGN service stratum separation from NGN transport stratum. ITU-T Y.2012 defines application network interface (ANI) which provides a channel for interactions and exchanges between applications and NGN elements. This interface is similar to the CLO interface. Y.2012 however does not address any details on the functionality and information requirements and control flows of the interface. Since it is premature at this point to dictate any particular architecture or interface it will be instead pointed out in this section, as the examples have previously shown, the type of information/control flows needed and currently unavailable as inputs or outputs to various cross layer optimization processes. 3.1. Topology Related Processes As seen in the previous sections, the fundamental processes of server selection and content placement can have dramatically better outcomes if some type of network topology information is known concerning clients and servers. For example, location mapping information for servers and clients from a network perspective would flow from the application layer to the network layer using this interface so that the network may be able to provide some performance estimates concerning the routes associated with the given location mapping information. In more complex or resource "hungry" scenarios knowing something about the capabilities of the network infrastructure can be used to determine the viability of a particular application prior to any attempts to reserve network resources for its support. Some level of network topology that depicts the network bandwidth availability for the requested servers-clients pairs would be useful if the network is capable of traffic-engineering. This "topology" information does not need to be exact. Indeed various levels of abstraction/virtualization may be helpful since if the application provider and network provider may be different organizational or business entities where neither party may wish to divulge detailed information. Most current methods are associated with IP networks. For instance, Akamai and other content distribution networks (CDN) carriers, has used some IP network knowledge to optimize their application overlay network usage. When selecting the surrogate location from the client location, many CDN providers use network latency via a probing technique or proximity based on static configuration to determine the optimal surrogate location. These overlays are not closely integrated Lee & Xia & Bernstein Expires January 4, 2011 [Page 10] Internet-Draft Cross-Layer Optimization (CLO) July 2010 with carrier's network real load condition such as link bandwidth utilization and availability. For many current and emerging applications that require stringent QoS and bandwidth guarantee, current CDN infrastructure is not well suited for meeting such service need. IETF ALTO WG has been focusing on overlay optimization among peers by utilizing information about topological proximity and appropriate geographical locations of the underlay networks. With this method, the optimization generally occurs in selecting peer location which will help reduce IP traffic unnecessarily traversing IP service providers. Current scope of this work does not address general problems this document has been discussing such as the selection of application servers based on resource availability and usage of the underlying networks. 3.2. Load and Traffic Adaptive Processes Load and traffic adaptive processes can be facilitated using an interface from the network to the CLO entity in the application layer. It concerns the current QoS being delivered, the network loading impacts, etc. of the network application so that the CLO if necessary can make adjustments, e.g., change client server relationships, change criteria for allocating clients to servers, change bandwidth allocation/reservation level, etc... Re-optimization of network application based on application feedback and network monitoring has not been properly defined in any of the existing interfaces. By allowing this type of information flow between the application layer and the network layer, adaptive and agile process would be enabled to better meet the performance objectives for certain applications. 3.3. Provisioning Processes If the network is configurable, an interface such that the CLO entity can use this configurability. For example, in MPLS-TE networks, we would like the network CLO entity to be able to initiate connection setup on behalf of the various application entities, e.g., clients and servers. The UNI interface defined for GMPLS networks are currently defined for network equipment rather than interacting with higher layer Lee & Xia & Bernstein Expires January 4, 2011 [Page 11] Internet-Draft Cross-Layer Optimization (CLO) July 2010 services. It tends not to extend fully between application resources and/or clients. 4. Security Considerations TBD 5. References [Cache] P. Krishnan, D. Raz, and Y. Shavitt, "The cache location problem," Networking, IEEE/ACM Transactions on, vol. 8, 2000, pp. 568-582. [GameMirror] S.D. Webb, S. Soh, and W. Lau, "Enhanced mirrored servers for network games," Proceedings of the 6th ACM SIGCOMM workshop on Network and system support for games, Melbourne, Australia: ACM, 2007, pp. 117-122. [GameServ]P. Quax, J. Dierckx, B. Cornelissen, G. Vansichem, and W. Lamotte, "Dynamic server allocation in a real-life deployable communications architecture for networked games," Proceedings of the 7th ACM SIGCOMM Workshop on Network and System Support for Games, Worcester, Massachusetts: ACM, 2008, pp. 66-71. [GameTrf] J. Farber, "Network game traffic modeling," Proceedings of the 1st workshop on Network and system support for games, Braunschweig, Germany: ACM, 2002, pp. 53-57. [GroupGame] K. Vik, C. Griwodz, and P. Halvorsen, "Applicability of group communication for increased scalability in MMOGs," Proceedings of 5th ACM SIGCOMM workshop on Network and system support for games, Singapore: ACM, 2006, p. 2. [IPTV] A.A. Mahimkar, Z. Ge, A. Shaikh, J. Wang, J. Yates, Y. Zhang, and Q. Zhao, "Towards automated performance diagnosis in a large IPTV network," Proceedings of the ACM SIGCOMM 2009 conference on Data communication, Barcelona, Spain: ACM, 2009, pp. 231-242. [Mirror] E. Cronin, S. Jamin, Cheng Jin, A. Kurc, D. Raz, and Y. Shavitt, "Constrained mirror placement on the Internet," Selected Areas in Communications, IEEE Journal on, vol. 20, 2002, pp. 1369-1382. Lee & Xia & Bernstein Expires January 4, 2011 [Page 12] Internet-Draft Cross-Layer Optimization (CLO) July 2010 [MPSel] S. Gargolinski, C.S. Pierre, and M. Claypool, "Game server selection for multiple players," Proceedings of 4th ACM SIGCOMM workshop on Network and system support for games, Hawthorne, NY: ACM, 2005, pp. 1-6. [PartState] P.B. Beskow, K. Vik, P. Halvorsen, and C. Griwodz, "Latency reduction by dynamic core selection and partial migration of game state," Proceedings of the 7th ACM SIGCOMM Workshop on Network and System Support for Games, Worcester, Massachusetts: ACM, 2008, pp. 79-84. [Replica] Lili Qiu, V. Padmanabhan, and G. Voelker, "On the placement of Web server replicas," INFOCOM 2001. Twentieth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE, 2001, pp. 1587-1596 vol.3. [ServVoD] N. Carlsson and D.L. Eager, "Server selection in large-scale video-on-demand systems," ACM Trans. Multimedia Comput. Commun. Appl., vol. 6, 2010, pp. 1-26. [ServStream]M. Guo, M.H. Ammar, and E.F. Zegura, "Selecting among replicated batching video-on-demand servers," Proceedings of the 12th international workshop on Network and operating systems support for digital audio and video, Miami, Florida, USA: ACM, 2002, pp. 155-163. [ServMulti] Zongming Fei, M. Ammar, and E. Zegura, "Multicast server selection: problems, complexity, and solutions," Selected Areas in Communications, IEEE Journal on, vol. 20, 2002, pp. 1399-1413. [SKA] P.E. Dewdney, P.J. Hall, R.T. Schilizzi, and T.J.L.W. Lazio, "The Square Kilometre Array," Proceedings of the IEEE, vol. 97, 2009, pp. 1482-1496. [Stream] D. Eager, M. Vernon, and J. Zahorjan, "Minimizing bandwidth requirements for on-demand data delivery," Knowledge and Data Engineering, IEEE Transactions on, vol. 13, 2001, pp. 742- 757. [Topo] S. Ratnasamy, M. Handley, R. Karp, and S. Shenker, "Topologically-aware overlay construction and server selection," INFOCOM 2002. Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE, 2002, pp. 1190-1199 vol.3. [VENT] http://www.ventrilo.com/ Lee & Xia & Bernstein Expires January 4, 2011 [Page 13] Internet-Draft Cross-Layer Optimization (CLO) July 2010 [VLBI] http://www.internet2.edu/science/vlbi.html [WoWHrs] P. Tarng, K. Chen, and P. Huang, "An analysis of WoW players' game hours," Proceedings of the 7th ACM SIGCOMM Workshop on Network and System Support for Games, Worcester, Massachusetts: ACM, 2008, pp. 47-52. [WoWAct] M. Suznjevic, M. Matijasevic, and O. Dobrijevic, "Action specific Massive Multiplayer Online Role Playing Games traffic analysis: case study of World of Warcraft," Proceedings of the 7th ACM SIGCOMM Workshop on Network and System Support for Games, Worcester, Massachusetts: ACM, 2008, pp. 106-107. [GFD-122] Tiziana Ferrari (editor), "Grid Network services Use Cases from the e-Science Community", GFD-I-122, Open Grid Forum, December 12, 2007. [CDN2001] B. Krishnamurthy, C. Wills, and Y. Zhang, "On the use and performance of content distribution networks," Proceedings of the 1st ACM SIGCOMM Workshop on Internet Measurement, San Francisco, California, USA: ACM, 2001, pp. 169-182. Lee & Xia & Bernstein Expires January 4, 2011 [Page 14] Internet-Draft Cross-Layer Optimization (CLO) July 2010 Author's Addresses Young Lee Huawei Technologies 1700 Alma Drive, Suite 500 Plano, TX 75075 USA Phone: (972) 509-5599 Email: ylee@huawei.com Frank Xia Huawei Technologies 1700 Alma Drive, Suite 500 Plano, TX 75075 USA Phone: (972) 509-5599 Email: xiayangsong@huawei.com Greg M. Bernstein Grotto Networking Fremont California, USA Phone: (510) 573-2237 Email: gregb@grotto-networking.com Intellectual Property Statement The IETF Trust takes no position regarding the validity or scope of any Intellectual Property Rights or other rights that might be claimed to pertain to the implementation or use of the technology described in any IETF Document or the extent to which any license under such rights might or might not be available; nor does it represent that it has made any independent effort to identify any such rights. Copies of Intellectual Property disclosures made to the IETF Secretariat and any assurances of licenses to be made available, or the result of an attempt made to obtain a general license or permission for the use of such proprietary rights by implementers or users of this specification can be obtained from the IETF on-line IPR repository at http://www.ietf.org/ipr The IETF invites any interested party to bring to its attention any copyrights, patents or patent applications, or other proprietary Lee & Xia & Bernstein Expires January 4, 2011 [Page 15] Internet-Draft Cross-Layer Optimization (CLO) July 2010 rights that may cover technology that may be required to implement any standard or specification contained in an IETF Document. Please address the information to the IETF at ietf-ipr@ietf.org. Disclaimer of Validity All IETF Documents and the information contained therein are provided on an "AS IS" basis and THE CONTRIBUTOR, THE ORGANIZATION HE/SHE REPRESENTS OR IS SPONSORED BY (IF ANY), THE INTERNET SOCIETY, THE IETF TRUST AND THE INTERNET ENGINEERING TASK FORCE DISCLAIM ALL WARRANTIES, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY WARRANTY THAT THE USE OF THE INFORMATION THEREIN WILL NOT INFRINGE ANY RIGHTS OR ANY IMPLIED WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Acknowledgment Funding for the RFC Editor function is currently provided by the Internet Society. Lee & Xia & Bernstein Expires January 4, 2011 [Page 16]