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Versions: 00 01 02                                                      
Network Working Group                           Greg Bernstein (Grotto)
Internet Draft                                        Young Lee (Huawei)
Intended status: Informational

                                                          June 28, 2011

      Use Cases for High Bandwidth Query and Control of Core Networks


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   carefully, as they describe your rights and restrictions with respect
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   This draft describes two generic use-cases that illustrate
   application layer traffic optimization concepts applied to high
   bandwidth core networks. For the purposes here high bandwidth will
   mean bandwidth that is significant with respect to the capacity of a
   wavelength in a wavelength division multiplexed optical transport
   system, e.g., 10-40Gbps or more. For each of these generic use cases,
   we present a generic optimization problem, look at the type of
   information needed (query interface) to perform the optimization,
   investigate a reservation interface to request network resources, and
   also consider enhanced availability and recovery scenarios.

Table of Contents

    1. Introduction..................................................2
      1.1. Computing Clouds, Data Centers, and End Systems...........3
   2. End System Aggregate Networking................................4
      2.1. Aggregated Bandwidth Scaling..............................5
      2.2. Cross Stratum Optimization Example........................5
      2.3. Data Center and Network Faults and Recovery...............6
      2.4. Cross Stratum Control Interfaces..........................7
   3. Data Center to Data Center Networking..........................8
      3.1. Cross Stratum Optimization Examples.......................9
      3.2. Network and Data Center Faults and Reliability............9
      3.3. Cross Stratum Control Interfaces.........................10
   4. Conclusion....................................................11
   5. Security Considerations.......................................11
   6. IANA Considerations...........................................11
   7. References....................................................11
      7.1. Informative References...................................11
   Author's Addresses...............................................14
   Intellectual Property Statement..................................14
   Disclaimer of Validity...........................................14

1. Introduction

   Cloud Computing, network applications, software as a service (SaaS),
   Platform as a service (PaaS), and Infrastructure as a Service (IaaS),
   are just a few of the terms used to describe situations where
   multiple computation entities interact with one another across a
   network.   When the communication resources consumed by these
   interacting entities is significant compared with link or network

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   capacity then opportunities may exist for more efficient utilization
   of available computation and network resources if both computation
   and network stratums cooperate in some way. The application layer
   traffic optimization (ALTO) working group is tackling the similar
   problem of "better-than-random peer selection" for distributed
   applications based on peer to peer (P2P) or client server
   architectures [16]. In addition, such optimization is important in
   content distribution networks (CDNs) as illustrated in [17].

   General multi-protocol label switching (GMPLS) [18] can and is being
   applied to various core networking technologies such as SONET/SDH
   [19] and wavelength division multiplexing (WDM) [20]. GMPLS provides
   dynamic network topology and resource information, and the capability
   to dynamically allocation resources (provision label switched paths).
   Furthermore, the path computation element (PCE) [21] provides for
   traffic engineered path optimization.

   However, neither GMPLS nor PCE provide interfaces that are
   appropriate for an application layer entity to use for the following

     .  GMPLS routing exposes full network topology information which
        tends to be proprietary to a carrier or require specialized
        knowledge and techniques to make use of, e.g., the routing and
        wavelength assignment (RWA) problem in WDM networks [20].

     .  Core networks typically consist of two or more layers, while
        applications are typically only know about the IP layer and
        above. Hence applications would not be able to make direct use
        of PCE capabilities.

     .  GMPLS signaling interfaces are defined for either peer GMPLS
        nodes or via a user network interface (UNI) [22]. Neither of
        these is appropriate for direct use by an application entity.

   In this paper we discuss two general use-cases that can generate core
   network flows with significant bandwidth and may vary significantly
   over time. The "cross stratum optimization" problems generated by
   these use cases are discussed. Finally, we look at interfaces between
   the application and network "stratums" that can enable overall

          1.1. Computing Clouds, Data Centers, and End Systems

   While the definition of cloud computing or compute clouds is somewhat
   nebulous (or "foggy" if you will) [1], the physical instantiation of
   compute resources with network connectivity is very real and bounded
   by physical and logical constraints. For the purposes of this paper

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   we will call any network connected compute resources a data center if
   its network connectivity is significant compared either to the
   bandwidth of an individual WDM wavelength or with respect to the
   network links in which it is located. Hence we include in our
   definition very large data centers that feature multiple fiber access
   and consume more than 10MW of power [2], moderate to large content
   distribution network (CDN) installations located in or near major
   internet exchange points [3], medium sized business centers, etc...

   We will refer to those computational entities that don't meet our
   bandwidth criteria for a data center as an "end system".

2. End System Aggregate Networking

   In this section we consider the fundamental use case of end systems
   communicating with data centers as shown in Figure 1. In this figure
   the "clients" are end systems with relatively small access bandwidth
   compared to a WDM wavelength, e.g., under 100Mbps. We show these
   clients roughly partitioned into three network related regions ("A",
   "B", and "C"). Given a particular network application, in a static
   network application situation, each client in a region would be
   associated with a particular data center.

                                           Region B
                             +---------+  +------+
                             |  Data   |  |Client|
                             |Center 2 |  |  B1  |+------+
             +------+        +----+----+  +--+---+|Client|
             |Client|             |         /     |  B2  |
             |  A1  `.         _.-+--------+-.    +--+---+
   Region A  +------+ `-.  ,-''               `--.  /   ...
        +------+        ,`:                       `+.     +------+
        |Client|       /                             \    |Client|
        |  A2  +------+                               \---+  BM  |
        +------+     (             Network             )  +------+
         ...        .-'                               /
     +------+   _.-'   \                             `.
     |Client|.-'        `=.                       ,-'  `.
     |  AN  |       _.-''  `--.               _.-\   +---`.----+
     +------+ +----'----+      `----+------+''    \  |  Data   |
              |  Data   |           |       \      | |Center 3 |
              |Center 1 |        +--+---+ +--+---+ \ +---------+
              +---------+        |Client| |Client|  \------+
                                 |  C1  | |  C2  |  |Client|
                                 +------+ +------+  |  CK  |
                                       Region C     +------+

            Figure 1. End system to data center communications.

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          2.1. Aggregated Bandwidth Scaling

    One of the simplest examples where the aggregation of end system
   bandwidth can quickly become significant to the "network" is for
   video on demand (VoD) streaming services. Unlike a live streaming
   service where IP or lower layer multicast techniques can be generally
   applied, in VoD the transmissions are unique between the data center
   and clients. For regular quality VoD we'll use an estimate of 1.5Mbps
   per stream (assuming H.264 coding), for HD VoD we'll use an estimate
   of 10Mbps per stream. To fill up a 10Gbps capacity optical wavelength
   requires either 6,666 or 1,000 clients for regular or high definition
   respectively.  Note that special multicasting techniques such as
   those discussed in [4] and peer assistance techniques such as
   provided in some commercial systems [5] can reduce the overall
   network bandwidth requirements.

    With current high speed internet deployment such numbers of clients
   are easily achieved; in addition demand for VoD services can vary
   significantly over time, e.g., new video releases, inclement weather
   (increases number of viewers), etc...

          2.2. Cross Stratum Optimization Example

    In an ideal world both data centers and networks would have
   unlimited capacity, however in actuality both can have constraints
   and possibly varying marginal costs that vary with load or time of
   day.  For example suppose that in Figure 1 that Data Center 3 has
   been primarily serving VoD to region "C" but that it has, at a
   particular period in time, run out of computation capacity to serve
   all the client requests coming from region "C". At this point we have
   a fundamental cross stratum optimization (CSO) problem. We want to
   see if we can accommodate additional client request from region "C"
   by using a different data center than the fully utilized data center
   #3. To answer this questions we need to know (a) available capacity
   on other data centers to meet a request, (b) the marginal
   (incremental) cost of servicing the request on a particular data
   center with spare capacity, (c) the ability of the network to provide
   bandwidth between region "C" to a data center, and (d) the
   incremental cost of bandwidth from region "C" to a data center.

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                                             Region B
                               +---------+  +------+
                               |  Data   |  |Client|
                               |Center 2 |  |  B1  |+------+
               +------+        +----+----+  +--+---+|Client|
               |Client|             |         /     |  B2  |
               |  A1  `.         _.-+--------+-.    +--+---+
     Region A  +------+ `-.  ,-'' XXXXX     XX  `--.  /   ...
          +------+        ,`:       ``---..__ XXXX  `+.     +------+
          |Client|       /  X        |       ```--XX   \    |Client|
          |  A2  +------+..X`.       \              XX--+---+  BM  |
          +------+     (  X   `-/     \                  )  +------+
           ...        .-'     .'       |        +----.X /
       +------+   _.-'   \  X/         \        |    X `.
       |Client|.-'        `=.X          \      XXXX ,-'  `.
       |  AN  |       _.-''  `--.    XXXXXXXXX  _.-\   +---`.----+
       +------+ +----'----+      `----+------+''    \  |  Data   |
                |  Data   |           |       \      | |Center 3 |
                |Center 1 |        +--+---+ +--+---+ \ +---------+
                +---------+        |Client| |Client|  \------+
                                   |  C1  | |  C2  |  |Client|
                                   +------+ +------+  |  CK  |
                                         Region C     +------+

     Figure 2. Aggregated flows between end systems and data centers.

   In Figure 2 we show a possible result of solving the previously
   mentioned CSO problem. Here we show the additional client requests
   from region "C" being serviced by data center #2 across the network.
   Figure 2 also illustrates the possibility of setting up "express"
   routes across the network at the MPLS level or below. Such
   techniques, known as "optical grooming" or "optical bypass" [6], [7]
   at the optical layer, can result in significant equipment and power
   savings for the network by "bypassing" higher level routers and

          2.3. Data Center and Network Faults and Recovery

    Data center failures, whether partial or complete, can have a major
   impact on revenues in the VoD example previously described. If there
   is excess capacity in other data centers within the network
   associated with the same application then clients could be redirected
   to those other centers if the network has the capacity.  Moreover,
   MPLS and GMPLS controlled networks have the ability to reroute
   traffic very quickly while preserving QoS. As with general network
   recovery techniques [8] various combinations of pre-planning and "on

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   the fly" approaches can be used to tradeoff between recovery time and
   excess network capacity needed for recovery.

    In the case of network failures there is the potential for clients
   to be redirected to other data centers to avoid failed or over
   utilized links.

          2.4. Cross Stratum Control Interfaces

    Two types of load balancing techniques are currently utilized in
   cloud computing. The first is load balancing within a data center and
   is sometimes referred to as local load balancing. Here one is
   concerned with distributing requests to appropriate machines (or
   virtual machines) in a pool based on the current machine utilization.
   The second type of load balancing is known as global load balancing
   and is used to assign clients to a particular data center out of a
   choice of more than one within the network and is our concern here.
   A number of commercial vendors offer both local and global load
   balancing products (F5, Brocade, Coyote Point Systems).  Currently
   global load balancing systems have very little knowledge of the
   underlying network. To make better assignments of clients to data
   centers many of these systems use geographic information based on IP
   addresses [9]. Hence we see that current systems are attempting to
   perform cross stratum optimization albeit with very coarse network
   information. A more elaborate interface for CSO in the client
   aggregation case would be:

       1. A Network Query Interface - Where the global load balancer can
          inquire as to the bandwidth availability between "client
          regions" and data centers.

       2. A Network Resource Reservation Interface - Where the global
          load balancer can make explicit requests for bandwidth between
          client regions and data centers.

       3. A Fault Recovery Interface - For the global load balancer to
          make requests for expedited bulk rerouting of client traffic
          from one data center to another.

    The network query interface can be considered a superset of the
   functionality proposed from the ALTO (application layer traffic
   optimization) servers being standardized in [10]. Note that in the
   network query and reservation interfaces it would be worthwhile to
   consider both current resources and resources at a future time, i.e.,
   scheduled resources. Although scheduled reservations are not
   supported directly by technologies such as MPLS and GMPLS they can be
   considered in network planning and provisioning systems. For example,
   a VoD provider knows ahead of time when the latest "blockbuster" film

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   will be available via its service and can make estimates based on
   historical data on the bandwidth that it will need to deal with the
   subsequent demand.

3. Data Center to Data Center Networking

    There are a number of motivations for data center to data center
   communications: on demand capacity expansion ("cloud bursting") [11],
   cooperative exchanges between business partners, offsite data backup,
   "rent before building"[12], etc... In Figure 3 we show an example
   where a number of businesses each with an "internal data center"
   contracts with a large external data center for additional
   computational (which may include storage) capacity. The data centers
   may connect to each other via IP transit type services or more
   typically via some type of Ethernet virtual private line or LAN

                         |                   |
                         | Large Data Center |
                         |                   |
                             ,--''               `---.
                          ,-'                         `-.
                        ,'                               `.
                      ,'                                   `.
     +--------+      ;                Network                :
     |Business|  __..+                                       |
     | #1 DC  +-'    :                                       ;
     +--------+       `.                                   ,'
                        `.                               ;:
                          `-.                         ,-'  \
                             `---.               _.--'   +--`.----+
                                  `+-----------''        |Business|
                                   /                     | #N DC  |
                                  |                      +--------+
                             | #2 DC  |

          Figure 3. Basic data center to data center networking.

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          3.1. Cross Stratum Optimization Examples

    In the DC-to-DC example of Figure 3 we can have computational
   constraints/limits at both local and remote data centers; fixed and
   marginal computational costs at local and remote data centers; and
   network bandwidth costs and constraints between data centers. Note
   that computing costs could vary by the time of day along with the
   cost of power and demand. Some cloud providers such as Amazon [13]
   have quite sophisticated compute pricing models including: reserved,
   on demand, and spot (auction) variants.

    In addition, to possibly dynamically changing pricing, traffic
   loads between data centers can be quite dynamic. In addition, data
   movement between data centers is another source of large network
   usage variation. Such peaks can be due to scheduled daily or weekly
   offsite data backup, bulk VM migration to a new data center, periodic
   virtual machine migration [14], etc...

          3.2. Network and Data Center Faults and Reliability

    For networked applications that require high levels of
   reliability/availability the network diagram of Figure 4 could be
   enhanced with redundant business locations and external data centers
   as shown in Figure 4. For example cell phone subscriber databases and
   financial transactions generally require what is called geographic
   database replication [15] and results in extra communication between
   sites supporting high availability. For example if business #1 in
   Figure 4 required a highly available database related service then
   there would be an additional communication flows from the data center
   "1a" to data center "1b".  Furthermore, if business #1 has outsourced
   some of its computation and storage needs to independent data center
   X then for resilience it may want/need to replicate (hot-hot
   redundancy) this information at independent data center Y.

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              +-------------+              +-------------+
              |Independent  |              |Independent  |
              |Data Center X|              |Data Center Y|
              +-----+-------+              +------+------+
                     \                           /
                      `.     _.------------.   .'
                        \--''               `-+-.
                     ,-'                         `-.       +--------+
                   ,'                               `.    .'Business|
                 ,'                                   `.-' |#N DC-a |
                ;                Network                :  +--------+
    +--------+  |                                       |
    |Business+---                                       ;
    |#1 DC-a |   `.                                   +:
    +--------+     `.                               ;/  \
                     `-.                         ,-'     `.
                      .'`---.               _.--'       +--`.----+
        +--------+   /       `+-+---------\'            |Business|
        |Business| .'           |          \            |#N DC-a |
        |#1 DC-b .'             /           \           +--------+
        +--------+             |             \
                          +----+---+    +--------+
                          |Business|    |Business|
                          |#2 DC-a |    |#2 DC-b |
                          +--------+    +--------+

     Figure 4. Data center to data center networking with redundancy.

          3.3. Cross Stratum Control Interfaces

    Similar to the end system aggregation case we can decompose cross
   stratum interfaces into three general types: (a) network query, (b)
   network reservation, and (c) recovery. However for DC-to-DC
   interfaces we are interested in network resources between data
   centers rather than between "client regions" and data centers.

    For network resource queries we may be concerned with (a) current
   bandwidth availability, (b) bandwidth availability at a future time,
   or (c) bandwidth for a bulk data transfer of a given amount that must
   take place within a given time window. A network reservation
   interface with both current and advanced reservation capability would
   complement the query interface.

   A simple recovery interface for data center based faults could be
   based on unused backup paths between data centers that are reserved

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   but not activated unless a request is received from the application
   stratum that recovery action is requested.

4. Conclusion

   In this draft we have discussed two generic use cases that motivate
   the usefulness of general interfaces for cross stratum optimization
   in the network core. In our first use case network resource usage
   became significant due to the aggregation of many individually unique
   client demands. While in the second use case where data centers were
   communicating with each other bandwidth usage was already significant
   enough to warrant the use of private line/LAN type of network

   Both use cases result in optimization problems that trade off
   computational versus network costs and constraints. Both featured
   scenarios where advanced reservation, on demand, and recovery type
   service interfaces could prove beneficial. Many concepts from recent
   standardization work at the IETF [10] such as location identifiers,
   and endpoint properties could be reused in defining such interfaces.

5. Security Considerations


6. IANA Considerations

   This informational document does not make any requests for IANA

7. References

7.1. Informative References

   [1]   M. Armbrust et al., "A view of cloud computing," Communications
         of the ACM, vol. 53, p. 50-58, Apr. 2010.

   [2]   "Location Information | DuPont Fabros Technology." (Online).
         Available: http://www.dft.com/data-centers/location-

   [3]   "Amazon CloudFront." (Online). Available:

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   [4]   K. A. Hua and S. Sheu, "Skyscraper broadcasting: a new
         broadcasting scheme for metropolitan video-on-demand systems,"
         in Proceedings of the ACM SIGCOMM  '97 conference on
         Applications, technologies, architectures, and protocols for
         computer communication, Cannes, France, 1997, pp. 89-100.

   [5]   "Adobe Flash Media Server 4.0 * Building peer-assisted
         networking applications." (Online). Available:

   [6]   Rudra Dutta and George N. Rouskas, "Traffic grooming in WDM
         networks: Past and future," IEEE Network, vol. 16, no. 6, pp.
         46 -56, 2002.

   [7]   Keyao Zhu and B. Mukherjee, "Traffic grooming in an optical WDM
         mesh network," Selected Areas in Communications, IEEE Journal
         on, vol. 20, no. 1, pp. 122-133, 2002.

   [8]   G. Bernstein, B. Rajagopalan, and D. Saha, Optical Network
         Control: Architecture, Protocols, and Standards. Addison-Wesley
         Professional, 2003.

   [9]   "Our IP Geolocation Products | Quova, Inc." (Online).
         Available: http://www.quova.com/what/products/.

   [10]  "draft-ietf-alto-reqs-09." (Online). Available:

   [11]  "Cloud Computing's Tipping Point -- InformationWeek." (Online).

   [12]  "Lessons From FarmVille: How Zynga Uses The Cloud --
         InformationWeek." (Online). Available:

   [13]  "Amazon EC2 Pricing." (Online). Available:

   [14]  Dynamic Workload Balancing with EMC VPLEX and Ciena Networking.
         EMC, 2010.

   [15]  "MySQL.:: MySQL Cluster Features." (Online). Available:

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   [16]  Seedorf, J. and E. Burger, "Application-Layer Traffic
         Optimization (ALTO) Problem Statement", RFC 5693,
         October 2009.

   [17]  B. Niven-Jenkins (Ed.), G. Watson, N. Bitar, J. Medved, S.
         Previdi, "Use Cases for ALTO within CDNs", work in progress,

   [18]  E. Mannie, Ed., "GMPLS Framework Generalized Multi-Protocol
         Label Switching (GMPLS) Architecture" RFC 3945, October 2004.

   [19]  G. Bernstein, E. Mannie, V. Sharma, E. Gray, "Framework for
         Generalized Multi-Protocol Label Switching (GMPLS)-based
         Control of Synchronous Digital Hierarchy/Synchronous Optical
         Networking (SDH/SONET) Networks", RFC 4257, December 2005.

   [20]  Y. Lee, Ed., G. Bernstein, Ed., W. Imajuku, "WSON Framework
         Framework for GMPLS and Path Computation Element (PCE) Control
         of Wavelength Switched Optical Networks (WSONs)", RFC6163,
         April 2011.

   [21]  A. Farrel, J.-P. Vasseur, J. Ash, "PCE Framework A Path
         Computation Element (PCE)-Based Architecture", RFC 4655, August

   [22]  G. Swallow, J. Drake, H. Ishimatsu, Y. Rekhter, "Generalized
         Multiprotocol Label Switching (GMPLS) User-Network Interface
         (UNI): Resource ReserVation Protocol-Traffic Engineering(RSVP-
         TE) Support for the Overlay Model" RFC 4208, October 2005.

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Author's Addresses

   Greg M. Bernstein
   Grotto Networking
   Fremont California, USA
   Phone: (510) 573-2237
   Email: gregb@grotto-networking.com

   Young Lee
   Huawei Technologies
   1700 Alma Drive, Suite 500
   Plano, TX 75075
   Phone: (972) 509-5599
   Email: ylee@huawei.com

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   All IETF Documents and the information contained therein are provided

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   Funding for the RFC Editor function is currently provided by the
   Internet Society.

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