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Network Working Group                                Young Lee (Huawei)
Internet Draft                                   Greg Bernstein (Grotto)
Intended status: Informational            Ning So (University of Texas)
                                                    Tae Yeon Kim (ETRI)
                                                    Kohei Shiomoto (NTT)
                                     Oscar Gonzalez de Dios (Telefonica)



                                                          March 3, 2011

    Research Proposal for Cross Stratum Optimization (CSO) between Data
                           Centers and Networks


          draft-lee-cross-stratum-optimization-datacenter-00.txt


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   publication of this document. Please review these documents
   carefully, as they describe your rights and restrictions with respect
   to this document.

Abstract

   Data Centers offer various application services to end-users such as
   video gaming, cloud computing and others. Since the data centers used
   to provide application services are distributed geographically around
   a network, many decisions made in the control and management of
   application services, such as where to instantiate another service
   instance or to which data center out of several a new client is
   assigned, can have a significant impact on the state of the network.
   Conversely the capabilities and state of the network can have a major
   impact on application performance.

   Currently application decisions are made with very little or no
   information concerning the underlying network used to deliver those
   services. Hence such decisions may be sub-optimal from both
   application and network resource utilization and quality of service
   objectives. This document proposes a research program into cross
   stratum application/network optimization focusing on the challenges
   and opportunities presented by data center based applications and
   carriers networks.

Table of Contents

    1. Introduction..................................................2
   2. Key Issues in Data Centers and Clouds..........................4
      2.1. Some Obstacles of Cloud Computing.........................5
      2.2. Changes in Network Access from Data Centers and Clouds....5
      2.3. Virtual Machine Migration.................................6
      2.4. Entities Involved.........................................6
      2.5. Load Balancing............................................7
      2.6. End-user capability and communication.....................7
   3. Deployed Applications, Services, and Products..................8
   4. Research Program...............................................9
      4.1. Tentative Research Deliverables..........................10
   5. References....................................................11
      5.1. Informative References...................................11
   Author's Addresses...............................................15
   Intellectual Property Statement..................................15
   Disclaimer of Validity...........................................16

1. Introduction

   This document describes a research program on the automation of
   certain interactions between data center based distributed


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   applications and the supporting networking infrastructure. Data
   center based applications are used to provide a wide variety of
   services such as video gaming, cloud computing [Nurmi], grid
   application [GFD-122] and others. High-bandwidth video applications
   such as remote medical surgery, live concerts and sporting events are
   also emerging. This document is mainly concerned with data center
   applications that in aggregate or individually make substantial
   bandwidth demands on the network. In addition these applications may
   desire specific bounds on QoS related parameters such as latency and
   jitter.

   Figure 1 shows a network diagram of an example data center based
   application. Data centers come in an extreme variety of sizes and
   configurations but all contain compute servers, storage and
   application control of some sort.

                        ,-----.     ---------------
     ----------        / App   \   |         DC 1  |
    | End-user |. . .>( Control )  |      o o o    |
    |          |       \       /   |       \|/     |
     ----------         `-----'    |        O      |
          |                         ----- --|------
          |                                 |
          |                                 |
          |       --------------------------|--
          |      /                      PE1 |  \
          |     /        ...................O   \     --------------
          |    |       .                         |   | o o o   DC 2 |
          |    | PE4 .                      PE2  |   |  \|/         |
           ----|---O.........................O---|---|---O          |
               |     .                           |   |              |
               |      .           PE3            |    --------------
                \      ..........O   Carrier    /
                 \               |   Network   /
                  ---------------|-------------
                                 |
                         --------|------
                        |        O      |
                        |       /|\     |
                        |      o o o    |
                        |          DC 3 |
                         ---------------

       Figure 1. Data center based application architecture example


   This research is concerned with a subset of "cross stratum
   optimization" (CSO) opportunities, e.g., combined optimization of


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   resources in the application and network stratum. We use the term
   stratum here to broadly differentiate the layers of most concern to
   the application and to the network in general.

   In the application stratum we are concerned with and limiting the
   scope of this research to those distributed applications offered via
   data centers. In particular, this project does not intend to cover
   applications delivered in a strictly peer to peer manner. Application
   resources can be roughly categorized into computing resources, i.e.,
   servers of various types and granularities (VMs, memory, disk) and
   content, e.g., video, audio, databases, large data sets, etc..

   By the network stratum we mean the "network layer" (IP) and below,
   e.g., MPLS, SDH, OTN, WDM. The network stratum has resources that
   include routers, switches, and links. We are particularly interested
   in further unleashing the potential presented by MPLS and GMPLS
   control planes at the lower network layers in response to the high
   aggregate or individual demands from the application layer.

   The four main cross stratum optimization opportunities of this
   research project are:

     1.  Resource optimization (application and network)

     2.  Responsiveness to quickly changing demands

     3.  Enhanced service resilience (via cooperative recovery
        techniques between application and network)

     4.  Quality of application experience (QoE) enhancement (via better
        use of existing network and application resources)

   In the following document we first give a brief overview of data
   center technology for network oriented readers, describe the current
   state of application/network integration from the deployment, and
   standards points of view, an then conclude with a more detailed
   description of the research thrusts (optimization, resilience, QoE)
   from the perspective of an IRTF project.

2. Key Issues in Data Centers and Clouds

   This section provides some key issues related to data centers and
   cloud computing that motivate the need for cross stratum optimization
   between applications and networks.






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2.1. Some Obstacles of Cloud Computing

   There are many drivers for the move towards data center based
   application services. They include reducing maintenance costs, energy
   costs, flexibility, scalability, etc...

   Reference [Armb] offers a very timely and readable review of cloud
   computing practice and potential. Though here we do not differentiate
   between cloud computing and medium and small data center based
   computing that utilize modern virtualization techniques and possibly
   other cloud computing techniques [Nurmi]. From their "top ten
   obstacles and opportunities for cloud computing" we see that over
   half have significantly involvement of the network.

     1. Availability/Business Continuity

     3. Data Confidentiality and Auditability

     4. Data Transfer Bottlenecks

     5. Performance Unpredictability

     8. Scaling Quickly

     9. Reputation Fate Sharing

2.2. Changes in Network Access from Data Centers and Clouds

   At the high side of data center size we begin to see significant
   changes in network access, e.g., from a drop-off of an optical metro
   ring (a wavelength or two), to an end destination in a long haul DWDM
   system (many wavelengths, multiple fibers). These changes have been
   partly driven by the consolidation effort of existing smaller size
   data centers into Super Data Centers in the government IT
   infrastructure and carriers.

   Another factor that contributes to high-speed network access is due
   to emerging applications that require high bandwidth such as sporting
   events, live converts, 3D video applications, remote medical surgery
   and so on.

   These changes provide still more motivation to enable the application
   layer to take advantage of the dynamic networking features offered by
   network capability such as MPLS/GMPLS.






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2.3. Virtual Machine Migration

   A key enabler for data center cost savings, consolidation,
   flexibility and application scalability has been the technology of
   compute virtualization or Virtual Machines (VMs)[XEN]. A VM to the
   software application looks like a dedicated processor with dedicated
   memory and dedicated operating system. In modern data centers or
   "computing clouds" the smallest unit of computing resource is the VM
   [Nurmi]. In public data centers one can buy computing capacity in
   terms of VMs for a particular amount of time. Though different VM
   configurations may be offered that are optimized for different types
   of processing (e.g., memory intensive, throughput intensive)[EC2].

   VMs offer not only a unit of compute power but also as an
   "application environment" that can be replicated, backed up and moved
   [Clark]. Although VM migration started in the LAN, Wide area VM
   migration has also been discussed in the literature, e.g., [Brad].
   The impact of VM migration on the network and hence other services
   has just recently been studied along with some mitigation approaches
   [Stage].

   Virtual machine migration has a variety of modes: (i) scheduled vs.
   dynamic; (ii) bulk vs. sequential; (iii) point-to-point vs. point-to-
   multi-point. Network capability can impact virtual machine migration
   strategy. For certain mission critical applications, bandwidth
   guarantee as well as performance guarantee must be provided by the
   network. Make-before-break capability is also critical to support
   seamless migration.

   For certain applications such as disaster recovery, bulk migration is
   required on the fly, which may necessitate concurrent computation and
   path setup dynamically.

2.4. Entities Involved

   We have the data center provider, a possibly separate application
   provider, and the user (See Figure 2). Note that the data center
   provider and the application provider may be potential competitors.
   In addition network providers may also offer data center services,
   making them potential competitors to an independent data center
   provider. Hence, for cross stratum optimization, understanding of
   various trust relationships is important when developing interfaces
   application/network interfaces.

   Figure 2 illustrates key entities involved.





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    ------------       ----------------------
   |  End-User  |-----| Application Provider |-----
    ------------       ----------------------      |
          |                       |                |
          |            ----------------------      |
          |           | Data Center Provider |     |
          |            ----------------------      |
          |                       |                |
          |            ----------------------      |
           -----------|   Network Provider   |-----
                       ----------------------

                  Figure 2: Key Entities involved in CSO

2.5. Load Balancing

   As the application servers are distributed geographically across many
   Data Centers for various reasons (e.g., load balancing), the decision
   as which server to select for an application request from end-users
   has many factors that can negatively affect the quality of experience
   (QoE) of the users if not done correctly. One of the major drivers
   for operating multiple Data Centers is allowing the application to be
   closer to the end-users, so that the overall service performance and
   the user experience can be enhanced.

   Among the key factors to be considered in choosing the server for an
   application or instantiating VM include:

     . The utilization of the servers;

     . The underlying network loading conditions within a data center
        (LAN);

     . The underlying network loading conditions between data centers
        (MAN/WAN);

     . The underlying network conditions between the end-user and data
        center.

2.6. End-user capability and communication

   As there are plethora of end-user terminal types (e.g., desktop
   device, PDA, mobile phones, etc.), it is important for application to
   capture end-user device capability and preference. For some
   applications, the same user may have multiple devices. In such case,



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   seamless device to device transition needs to be provided by
   application providers to ensure acceptable QoE to the end-users.

   For other applications, codec capability and/or terminal screen
   dimension of end-user devices may also have impact on QoS and
   bandwidth requirements.

   Hence, the interface between end-user and application may need to be
   enhanced to capture these aspects.

3. Deployed Applications, Services, and Products

   Most current methods are associated with IP networks. For instance,
   Akamai and other content distribution networks (CDN) carriers, have
   used some IP network knowledge to optimize their application overlay
   network usage. When selecting the surrogate (cache or mirror)
   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 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.

   The IETF ALTO WG has focused 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.

   In some cases, application controllers can estimate network load
   based on ping latency, and network topology based on trace routes in
   the Internet, based on the assumption that the underlying transport
   network is an IP network, and the routing is based on simple IP
   forwarding.

   In regards to load balancing, DNS redirect technique is currently
   used to redirect end-user request to certain servers that host end-
   user application.

   In the current Intra-Data Center network, the server selection for an
   application/VM is done by load-balancer. The load balancer is aware
   of a certain level of server usage data (e.g., the number


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   simultaneous instances of the application usage) and distributes the
   application requests based on that data.

   However, the current load balancing technology is insufficient in
   providing an optimal decision across multiple VLANs and multiple Data
   Centers. This capability is often referred to as global load
   balancing.

   First of all, there is no good mechanism for the communication
   exchange among load balancers located in different Data Centers. This
   implies that load balancers from different vendors cannot communicate
   to each other.

   Secondly, load balancers know little about the underlying network
   conditions listed in the previous section.  Nor is it user condition
   aware.

   When migrating existing VMs/applications from one data center to
   another, the underlying network load condition in LAN/MAN/WAN can be
   constraining factors. Migration of VMs/applications, for instance,
   typically requires a high-speed data transfer across LAN/MAN/WAN to
   minimize service impact. Application controllers responsible for this
   operation is not aware of LAN/MAN/WAN network conditions.



4. Research Program

   In the previous sections we have looked at key issues in Data Center
   and Cloud Computing and some commercial service deployments on a
   variety of cross layer optimization problems.

   A common theme to the previous work was that sharing information
   between the application and network stratums can lead to more optimal
   solutions to the challenges facing distributed applications. In
   addition to sharing information, both the application layer and
   network may possess capabilities that are can very useful to each
   other if appropriate access can be arranged, e.g., the dynamic high
   bandwidth services that are enabled by MPLS/GMPLS.

   Hence this research project is focused on the interfaces and services
   that could be used between the application and network stratum to
   address the four main problem thrusts of:

     1.  Joint application/network Resource optimization (global load
        balancing)




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     2.  Responsiveness to quickly changing demands from/to application
        to/from network

     3.  Enhanced service resilience (via cooperative recovery
        techniques between application and network)

     4.  Quality of application experience (QoE) enhancement (via better
        use of existing network and application resources)

   Even though algorithms play a big part of optimization, in thrust (1)
   we are concerned with the information that could be shared to promote
   optimization and various optimization criteria rather than specific
   algorithms. Note that this is similar to the approach taken with
   MPLS-TE, GMPLS and PCE where specific algorithms are not
   standardized.

4.1. Tentative Research Deliverables

       a)  Baseline network/application model - general enough to
          include most cases of interest but no more.

       b)  Survey the various "trust or lack of" in the relationships
          between various key players in both the application and
          network stratum. Include a survey of various "summarization",
          "abstraction", or other techniques that can reduce the level
          of "trust" needed at an interface.

       c)  Survey of the data center/cloud based applications -
          investigate the commonality and differences with respect to
          their impact on network infrastructure.

       d)  Define key interfaces and their functionality and relate
          these to current standards and potential future standards.

       e)  Investigation and report on the role of TE based network
          infrastructure (MPLS, GMPLS) in providing support to dynamic
          application loads, scaling and QoE enhancement.

       f)  Report on mechanisms for application level support for
          network recovery and network support for application recovery.

       g)  Investigate the time frames and responsiveness of interest
          to application/network interaction. For example what do
          various applications need, what can the network provide, can
          other techniques such as time based "load shifting" be
          utilized.




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5. Security Considerations

   TBD

6. IANA Considerations

   This informational document does not make any requests for IANA
   action.



7. References

7.1. Informative References

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   [Clark]   C. Clark et al., "Live migration of virtual machines," in
             Proceedings of the 2nd conference on Symposium on Networked
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   [CostCloud] A. Greenberg, J. Hamilton, D. Maltz, and P. Patel, "The
             cost of a cloud: research problems in data center
             networks," ACM SIGCOMM, Vol. 39, Number1, January 2009.






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   [GFD-122] Tiziana Ferrari (editor), "Grid Network services Use Cases
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   [Grampin] E. Grampin, A. Castro, M. German, F. Rodriguez, G. Tejera,
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    [Habib]    I.W. Habib, Qiang Song, Zhaoming Li, and N. S. V. Rao,
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Author's Addresses



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

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

   Ning So (Editor)
   Univerity of Texas at Dallas
   Email: ningso@yahoo.com


   Tae Yeon Kim
   ETRI
   tykim@etri.or.kr

   Kohei Shiomoto
   NTT
   Email : shiomoto.kohei@lab.ntt.co.jp

   Oscar Gonzalez de Dios
   Telefonica
   Email : ogondio@tid.es



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   Lee & Bernstein            Expires September 3, 2011  [Page 16]


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