One document matched: draft-lee-cross-layer-optimization-problem-00.txt
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."
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 January 4, 2011.
Copyright Notice
Copyright (c) 2010 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
publication of this document. Please review these documents
Lee & Xia & Bernstein Expires January 4, 2011 [Page 1]
Internet-Draft Cross-Layer Optimization (CLO) July 2010
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.
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]
| PAFTECH AB 2003-2026 | 2026-04-24 03:11:17 |