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Resource Allocation Model for Hybrid Switching Networks
draft-sun-nmrg-hybrid-switching-04

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This is an older version of an Internet-Draft whose latest revision state is "Active".
Authors Weiqiang Sun , Junyi Shao , Weisheng Hu
Last updated 2021-12-05 (Latest revision 2021-06-13)
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draft-sun-nmrg-hybrid-switching-04
Network Working Group                                             W. Sun
Internet-Draft                                                   J. Shao
Intended status: Informational                                     W. Hu
Expires: June 9, 2022                                               SJTU
                                                             Dec 6, 2021

        Resource Allocation Model for Hybrid Switching Networks
                draft-sun-nmrg-hybrid-switching-04.txt

Abstract

   The fast increase in traffic volumn within and outside Datacenters is
   placing an unprecendented challenge on the underline network, in both
   the capacity it can provide, and the way it delivers traffic.  When a
   large portion of network traffic is contributed by large flows,
   providing high capacity and slow to change optical circuit switching
   along side fine-granular packet services may potentially improve
   network utility and reduce both CAPEX and OpEX.  This gives rise to
   the concept of hybrid switching - a paradigm that seeks to make the
   best of packet and circuit switching.

   However, the full potential of hybrid switching networks (HSNs) can
   only be realized when such a network is optimally designed and
   operated, in the sense that "an appropriate amount of resource is
   used to handle an appropriate amount of traffic in both switching
   planes."  The resource allocation problem in HSNs is in fact complex
   ineractions between three components: resource allocation between the
   two switching planes, traffic partitioning between the two switching
   planes, and the overall cost or performance constraints.

   In this memo, we explore the challenges of planning and operating
   hybrid switching networks, with a particular focus on the resource
   allocation problem, and provide a high-level model that may guide
   resource allocation in future hybrid switching networks.

Status of This Memo

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   time.  It is inappropriate to use Internet-Drafts as reference
   material or to cite them other than as "work in progress."

   This Internet-Draft will expire on June 9, 2022.

Copyright Notice

   Copyright (c) 2021 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
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1.  Introduction

   In facing rapid increase of network traffic [Gantz12], as well as the
   number of servers in cloud data centers [Cisco15], new architectures
   and operation models of Data Center Networks (DCNs) gained wide
   interests.  One concept that attracted considerable and lasting
   attention is the introduction of optical switching technologies into
   DCNs, hoping that bypassing some of the traffic without performing
   per-packet electronic processing will help reducing the Operational
   Cost (OpEx), as well as the Capital Expenditure (CapEx) of DCNs.
   This concept of combining electronic packet switching (EPS) and
   optical switching (often optical circuit switching, OCS), is called
   hybrid switching [Zukerman89].  In recent years, many hybrid
   switching schemes have been proposed [Gauger06], and it is reasonable
   to believe that when a DCN grows beyond a certain scale, the benefit
   of introducing optical switching will emerge and become more evident
   as the size of the DC continues to increase.

   On the other hand, achieving the benefits of hybrid switching
   requires careful design at the planning stage, and proper operation
   during runtime.  This poses challenges that goes far beyond the
   topological or architectural aspects.  For instance, at the planning
   stage, one has to decide how much to invest in the two switching
   planes, such that each could be fully utilized when the network
   becomes operational.  Under cases when dynamic resource allocation
   between the two planes are possible, one has to decide how resource
   is allocated between the two planes, and how traffic should be
   directed to each of them, such that performance constraints can be
   satisfied, and operational cost such as power consumption can be
   minimized.

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   This memo aims to explore the challenges of planning and operating
   hybrid switching networks, and provide a high-level model that may
   guide the resource allocation in future hybrid switching networks.
   We will use hybrid switching DCN as an example to show one possible
   application of this model.

2.  Conventions Used in This Document

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
   document are to be interpreted as described in RFC2119.

3.  Overview of Hybrid Switching Networks

   Hybrid Switching Networks (HSNs) are networks that employ more than
   one switching technology.  The term started to attract attention when
   Wavelength Dense Multiplexing (WDM) started to be deployed as a
   underlying infrastructure of TCP/IP based packet networks [FENG17].
   It continued to receive considerable attention, as the research on
   future-looking optical switching schemes boomed, around and after the
   begining of the 21th century.

   The research on hybrid switching gained momentum again with the rapid
   growth of cloud data centers.  With a clearer context and real-life
   prototyping efforts, a wider concensus regarding the benefits and
   feasibility of HSN have been reached.

   The challenges of planning and operating hybrid DCNs are rooted in
   the fundamental differences between EPS and OCS [Farrington10],
   [WANG10].  In principle, EPS is good at delivering traffic that is
   bursty and difficult to predict.  By aggregating the traffic from a
   large number of communcating peers, high network utilization can be
   achieved at modest cost.  OCS, on the other hand, is suited for well
   planed, or highly predictable traffic patterns.  One good example is
   the delivery of bulk flows, which can last up to a few minutes when
   carried by a wavelength channel at full capacity.

4.  Terms used in this document

   o  Electronic Packet Switching (EPS)
      EPS in this memo refers to the off-the-shelf switching technology.
      It provides "best-effort" packet delivery service.  Since EPS
      performs fine-granular per-packet processing, it is generally
      regarded to be best suitable for traffic that is bursty and
      difficult to predict.  Existing researches show that when lightly
      loaded, the performance of EPS can be rather reliable and
      predictable.  However, when the network is heavily loaded, the

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      performance of EPS will deteriorate very quickly and result in
      long queueing delay and high packet loss rate.

   o  Optical Circuit Switching (OCS)
      OCS in this memo refers to connection oriented network services
      based on optical switching technologies, such as MEMS or WSS based
      switches, and the like.  The connection oriented nature of OCS
      requires the establishment of connections through signaling prior
      to data transfer.  The capacity of each connection, for instance,
      a wavelength channel, often consumes a significant portion of the
      overall network capacity.  Request blocking is thus difficult to
      eliminate in OCS, if not impossible.

   o  Hybrid Switching Networks (HSNs)
      HSNs in this memo refers to networks that: i) employ both EPS and
      OCS, and ii) accept data transfer request in both packet and
      stream/flow form.  Upon entering the network, requests in packets
      form will be handled by the EPS plane, and requests in flow form
      will be handled by OCS following the connection provisioning
      procedures.  This differs HSN from IP over WDM networks, where
      both switching schemes exist, but services start and terminate
      only on the IP layer, and standalone OCS service is not provided.
      Note that the boundary between packet and flow requests may not
      naturally exist.  For instance, when flow level information is not
      available from outside the network, it will be up to the network
      to decide how traffic should be partitioned and then directed to
      either EPS or OCS.

5.  Performance Measures in Hybrid switching Networks

5.1.  Performance Measures in Electronic Packet Switching

   Without loss of generality, performance of packet switching networks
   can be characterized by one or more of the following metrics:

   o  Packet loss rate - packet loss may happen when congestions occur.
      Statistically, in a given network, packet loss rate can be seen as
      a function of network load.  Packet loss rarely happen when the
      traffic load is low.  But when the load increases to a certain
      threshold in the network, or in part of it, packet loss rate may
      increase quickly as load continues to increase.
      Packet delay and jitter - like packet loss rate, packet delay is
      mostly stable and jitter is small when the network is lightly
      loaded.  Delay and jitter will increase dramatically when network
      load increases.
      Flow completion time - flow completion time is a composite metric
      that relies on both packet loss rate and packet delay.

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5.2.  Performance Measures in Optical Circuit Switching

   Performance of Optical Circuit Switching (OCS) is typically measured
   by request blocking rate, defined as the number of admitted requests
   over the total number of arrivals.  In theory, blocking in OCS can
   not be eliminated.  The planning of OCS is thus often a tradeoff
   between performance and cost, as in the case of conventional
   telephone networks, in which trunk capacity can be dimensioned with
   the Erlang-B formula.

6.  BLOC - the Blocking LOss Curves

6.1.  General Idea

   To understand the resource allocation in HSNs, it is important to
   understand the interactions between the three components in the
   systems:

   o  Traffic partitioning
      Traffic partitioning means the separation of incoming traffic into
      two parts so that each part can be handled by the one of the two
      switching planes.  In today's networks, there might be many
      traffic separation/differentiation mechanisms for the purpose of
      enforcing differentiated policy based on traffic type.  Traffic
      partitioning in the context of HSN, however, aims to realize the
      optimal separation of flows into the two planes, such that the
      utility of the network can be maximized.
      One traffic partitioning method is a flow length based method.
      With a predefined threshold, flows are classified into short flows
      and large flows, each served with the packet switching plane and
      the circuit switching plane, respectively.
      Partitioning can be performed according to a priori knowledge,
      e.g., according to the information provided by the applications
      that generate the traffic flows.  It also can be performed in
      network during runtime.  The details on traffic classification and
      partitioning may be found in [Cisco15] and are outside the scope
      of this memo.

   o  Resource allocation
      The resource here can be physical resources such as switch ports,
      wavelengths or fibers.  It also can be abstract resource such as
      the overall budget.

   o  Performance/Cost Constraints
      The cost constraint applies when the making of the hybrid
      switching system is subject to limited budget.  For any given
      traffic demand, the cost and performance of carrying the traffic
      through either EPS or OCS can be very different.  A good design

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      should, on the first hand, satisfy the performance constraint; on
      the other, it should also leave space for future traffic demand
      growth.  The performance constraints specify the acceptable worst-
      case performance of the system, for example, the maximum packet
      loss rate, highest request-blocking rate, or longest packet delay
      etc.  Given traffic demand, the worst-case performance constraint
      specifies the least amount of resource that should be allocated to
      a switching plane.

   As can easily be seen, the operation of HSNs involves close
   interactions between the three components, and is a difficult
   problem.  The interactions can be summarized into the following
   diagram.

                     +-----------------------+
                     |   Cost/Performance    |
                     |      Constraint       |
                     +-----------------------+
                                 ^
                 Constraint      |        Constraint
           +---------------------+-------------------------+
           |                                               |
           v                                               v
   +-----------------+                            +-----------------+
   |     Traffic     |        Linkage             |    Resource     |
   |    Partition    |----------------------------|   allocation    |
   |                 |                            |                 |
   +-----------------+                            +-----------------+

              Interactions beween the three components in HSN

6.2.  Modeling the curves

   In a typical IP network with a given traffic load, the packet loss
   rate decreases when the network capacity increases and vice versa.
   Similarly, in circuit switching networks, the request blocking
   probability will decrease when the bandwidth increases and vice
   versa.  In a hybrid switching system, the overall resource capacity
   is constant.  The resource allocation between EPS and OCS plane will
   directly affect the network performance of both switching planes.
   The network performance is also affected directly by how the traffic
   is partitioned between EPS and OCS planes.

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   ^ packet loss rate
   |   o   capacity resource 1
   |   *   capacity resource 2           ____o   _____*  ____x
   |   x   capacity resource 3          /       /       /
   |                              _____o  _____*  _____x
   |                             /       /       /
   |                        ____o   ____*   ____x
   |                       /       /       /
   |                   ___o    ___*    ___x
   |                  /       /       /
   |               __o     __*     __x
   |              /       /       /
   |            _o      _*      _x
   |           /       /       /
   |         _o      _*      _x
   |        /       /       /
   |      _o      _*      _x
   |     /       /       /
   |    o       *       x
   +-------------------------------------------------------->
                    the traffic load for EPS

                 Fig. 1(a) the performance curves for EPS

   ^ request blocking rate
   |   o   capacity resource 1
   |   *   capacity resource 2           ____o   _____*  ____x
   |   x   capacity resource 3          /       /       /
   |                              _____o  _____*  _____x
   |                             /       /       /
   |                        ____o   ____*   ____x
   |                       /       /       /
   |                   ___o    ___*    ___x
   |                  /       /       /
   |               __o     __*     __x
   |              /       /       /
   |            _o      _*      _x
   |           /       /       /
   |         _o      _*      _x
   |        /       /       /
   |      _o      _*      _x
   |     /       /       /
   |    o       *       x
   +-------------------------------------------------------->
                    the traffic load for OCS

                 Fig. 1(b) the performance curves for OCS

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   To clearly classify the influence from the traffic load partition and
   network capacity allocation, we take Fig. 1(a) and 1(b) to show the
   performance curves for EPS and OCS with varying traffic loads and
   network capacities.  In Fig. 1(a), we choose the packet loss rate as
   the performance of EPS.  When the traffic load for EPS increases, the
   packet loss rate becomes worse under the constrained network
   capacity.  The extension of network capacity will bring a promoted
   packet loss rate for EPS which is classified in Fig. 1(a) with the
   capacity resource 1 <= capacity resource 2 <= capacity resource 3.
   The performance curves for OCS is shown in Fig. 1(b) after choosing
   the request blocking rate as the y-axis, and the relationship among
   these curves is still resource 1 <= capacity resource 2 <= capacity
   resource 3.  Combining Fig. 1(a) and 1(b), the more network
   capacities we allocate to EPS or OCS, the better service they will
   provide under a heavier traffic load transmission.

6.3.  The BLOC System

   The BLOC framework comprises two types of curves, i.e., loss curves
   (LCs) and blocking curves (BCs), in the same two-dimensional
   coordinate system.  An LC or a BC in the BLOC framework is a curve
   that contains a series of points with the same packet loss rate or
   request blocking probability.  Using the percentage of traffic
   delivered by EPS as the x-axis and the percentage of bandwidth
   allocated to EPS plane as the y-axis, all of the curves in the BLOC
   framework are monotonic.  Another important component of the BLOC
   framework is the feasible region.  In this paper, "feasible" means
   that as long as the traffic partitioning and resource allocation fall
   within this area, the resulting packet loss rate will be smaller than
   Pmax (the maximum packet loss rate) and the request blocking
   probability will be lower than Bmax (the maximum request blocking
   rate).  Thus, the feasible region contains all the feasible
   combinations of resource allocation and traffic partitioning that
   satisfy the network performance requirements.  Different resource
   allocation strategies in hybrid switching networks can be achieved by
   choosing a point from the feasible region.

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   ^ 100%
   |...% of resource allocated to the EPS plane (Y Axis)...
   |                                    _o                  _x
   |  (1) packet loss                  /                   /
   |      rate increase             __o                 __x
   |  (2) packet loss              /  ^                /
   |      rate decrease          _o   .              _x
   |                            /     .             /
   |                         __o      .(2)       __x
   |                        /         .         /
   |                     __o          .      __x
   |                    /             .     /
   |                ___o              . ___x
   |               /      (1)         ./
   |          ____o .............>____x
   |         /                   /
   |   _____o               ____x
   |  /                    /
   |_o____________________x
   +-------------------------------------------------------->
   (0,0)  % of traffic offered to the EPS plane (X Axis) 100%

              Fig. 2(a) the packet loss curves for EPS plane

   ^ 100%
   |                                                   (100%,100%)
   |...% of resource allocated to the EPS plane (Y Axis)...  +
   |                                  o______________x______/
   |                                 /              /
   |                           _____o         _____x
   |                          /              /
   |                     ____o          ____x
   |                    /    ^         /
   |                ___o     .     ___x
   |               /      (2).    /
   |            __o          . __x
   |           /    (1)      ./
   |        __o  ........> __x     (1) request blocking
   |       /              /            rate decrease
   |     _o             _x         (2) request blocking
   |    /              /               rate increase
   |  _o             _x
   | /              /
   |o              x
   +-------------------------------------------------------->
       % of traffic offered to the EPS plane (X Axis)    100%

            Fig. 2(b) the request blocking curves for OCS plane

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   Fig. 2(a) and 2(b) show an example of LCs and BCs when the overall
   hybrid system capacity and the traffic volume are fixed.  In Fig.
   2(a), when the percentage of traffic to be transmitted by EPS
   increases, the bandwidth allocated to EPS plane must also be
   increased so the same packet loss rate can be achieved.  Hence, each
   LC is monotonically increasing.  In addition, the LCs with smaller
   loss rate values require a larger percentage of bandwidth for the
   same amount of traffic.  Therefore, the LCs moves to the top left
   when the packet loss rate becomes smaller, as shown in Fig. 2(a).
   All of the LCs pass through the origin (0, 0), so if no bandwidth is
   allocated to PS plane, it cannot transmit any traffic.  Similarly,
   the BCs move downward to the right when the request blocking
   probability becomes lower, and all of the BCs converge to the point
   (100,100), where all of the bandwidth and traffic is assigned to PS
   plane, as shown in Fig. 2(b).

   ^ 100%
   |
   |----- % of resource allocated to the EPS plane (Y Axis)
   |                 +----------------------------------+
   |                 | Request blocking curve with Bmax |  /
   |                 +------------------+---------------+ /
   |                                    |                /
   |   +-----------------------------+  |             / /
   |   | Packet loss curve with Pmax |  |            / /
   |   +------+----------------------+  |           / /
   |          |                         v          / /
   |          |     /-------------------O---------X-/
   |          |    / -- -- -- -- -- -- -- -- --  /
   |          |   / -- -- -- -- -- -- -- -- --  /
   |          v  / -- -- -- -- -- -- -- -- --  /
   |        /-O-.-----------------^-----------/
   |       /   /                  |
   |      /   /                   |
   |     /   /         +---------------------+
   |    /   /          |   feasible region   |
   |   /               +---------------------+
   |  /
   +-------------------------------------------------------->
       % of traffic offered to the EPS plane (X Axis)   100%

                         Fig. 3 an examplery BLOC

   We now consider a hybrid switching system with the maximal allowed
   packet loss rate Pmax and the maximal allowed request blocking
   probability Bmax [FENG16].  Fig. 3 shows a BLOC where the LCs and BCs
   are placed in the same two-dimensional coordinate system.  The
   hatched area above the LC of Pmax and below the BC of Bmax contains

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   all of the feasible combinations of traffic partitioning and resource
   allocation.  Choosing a point from the feasible region (i.e., a
   combination of resource allocation and traffic partitioning) is
   subject to various optimization objectives.  For instance, from an
   energy consumption perspective, we need to choose the point with the
   minimal percentage of EPS resources from the feasible region (i.e.,
   the lowest point in the feasible region), so that the overall energy
   consumption would be minimized.  In Section 5, we show that other
   metrics can also be optimized with the BLOC, such as the packet delay
   in EPS plane as a function of resource allocation and traffic
   partitioning.

6.4.  An example

   A hybrid switching Datacenter network is shown in Fig. 4 [FENG17].
   Among all s+p uplink interfaces on each ToR switch, s of them connect
   the switch to the EPS plane and the rest, p, connect the ToR switch
   to the OCS network.  As the cost of supporting an OCS connection can
   be very different from that of an EPS port, different combinations of
   s and p will result in significant difference in building cost.
   Different combinations of s and p will also lead to different
   performance and running cost, such as power consumption.

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     +-------------------------+          +-----------------------+
     |                         |          |                       |
     |       EPS Network       |          |      OCS Network      |
     | +-+ +-+ +-+ +-+ +-+ +-+ |          |+-+ +-+ +-+ +-+ +-+ +-+|
     +-+-+-+-+-+-+-+-+-+-+-+-+-+          ++-+-+-+-+-+-+-+-+-+-+-++
        ^   ^   ^   ^   ^   ^               ^   ^   ^   ^   ^   ^
        |   |   |   |    \   \             /    |  /   /     \   \
        |   |   |   |     \   \           /     / /   /       \   \
        |   |   |   |      \   \         /     / /   /        |   |
        |   |   |   \       \   \-------*-----*-*---*---\     |   |
        |   |   |    \       \---------*-----*-*---*\    \    \   \
       .+---+.  \     *---------------/     / /   /  \    \    \   \
 s -->( |   | )  \   / \ /-----------------/ /   /    \    \    |   \
       `+---+'    \ /   *-------\           /   /      \    \   |   |
        |   |      *---*-----\   \         /   /        \   |   |   |
        |   |    .X--./       \   \       /   /         |   |   |   |
 p -----+---+-->(/   /)        \   \     /   /          |   |   |   |
        v   v   v`---'          v   v   v   v           v   v   v   v
     +-+-+-+-+-+-+-+-+-+     +-+-+-+-+-+-+-+-+-+     +-+-+-+-+-+-+-+-+-+
     | +-+ +-+ +-+ +-+ |     | +-+ +-+ +-+ +-+ |     | +-+ +-+ +-+ +-+ |
     |   ToR Switch    |     |   ToR Switch    |     |   ToR Switch    |
     +-----------------+     +-----------------+     +-----------------+
     +-----------------+     +-----------------+     +-----------------+
     +-----------------+     +-----------------+     +-----------------+
     |       ...       |     |       ...       |     |       ...       |
     +-----------------+     +-----------------+     +-----------------+
     +-----------------+     +-----------------+     +-----------------+
     +-----------------+     +-----------------+     +-----------------+

               Fig. 4 switch ports allocation in hybrid DCN

   The costs of network interconnecting devices in the EPS and OCS
   networks are determined by allocation of uplink interfaces.  Thus,
   for each ToR, the cost constraint can be presented as Cp(s) + Cc(p)<=
   C, in which Cp(s) stands for the cost of EPS with s uplinks, and
   Cc(p) stands for the OCS cost with p uplinks.

   The total volume of flows to be transmitted on a ToR switch is V.
   The traffic is carried by either EPS or OCS:Vp + Vc = V.

   The performance requirements specify the acceptable worst-case
   performance of the system, such as the longest flow completion time
   and the highest request blocking probability.  A proper resource
   allocation and traffic partitioning should satisfy the performance
   requirements in both EPS and OCS networks: Tp(s,Vp) <= Tmax, Bc(p,Vc)
   <= Bmax, where Tmax and Bmax are the flow completion time and request
   blocking probability requirements in EPS and OCS, respectively.

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      ^ 100%
      | % of resource to EPS
      |        +------------+       +-----------+         *
      |        |Tmax=0.02ms |       | Bmax=0.01 |         *
      |        +------------+       +-----------+        **----
      |               |                   |             */ ////
      |               |                   |             */ ////
      |               |                   |            */ /////
      |               v         ***********************/ //////
      |            ***O********           v           / / +++++
      |         **      /-----------------O----------/ / +
      |        **      / ////////////////////////// / / +
      |        *      / ////////////////////////// / / +
      |       *      /--------O-------------------/ / +
      |       *     / ////////^////////////////////// +
      |      **    / /////////|///////////////////// +
      |      *    / // +++++++|+++++++++++++O++++++++
      |      *   /+++++       |             ^
      |      *  /+            |             |
      |      *  ++      +-----------+ +-----------+
      |      *  +       |Bmax=0.001 | | Tmax=1ms  |
      |     ** ++       +-----------+ +-----------+
      |   ***+++
      | ***                                                100%
      +-------------------------------------------------------->
                  % of traffic offered to the EPS

                        Fig. 5 BLOC for hybrid DCN

   Fig. 5 shows the BLOC with different network performance
   requirements.  When Tmax equals 1 ms and Bmax equals 0.01, there is a
   feasible region between the curves with Tmax and Bmax.  When the
   performance requirements are higher (i.e., smaller Tmax and Bmax),
   the feasibleregion will be smaller or may even disappear.  For
   example, when Tmax and Bmax decrease respectively to 0.02 ms and
   0.001, the feasible region cannot be found.  That means it is not
   possible to find a resource allocation that can satisfy Tmax and Bmax
   simultaneously.  As the hybrid switching system is an interaction
   between the three components, when the network performance
   requirements cannot be satisfied, the system should have a greater
   budget or carry less traffic to obtain a feasible resource
   allocation.

7.  Security Considerations

   This document does not impose any new challenges to the current
   Internet.

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8.  IANA Considerations

   This document makes no requests for IANA action.

9.  Acknowledgements

   We are grateful to the valuable discussions and inputs from the
   community.  We thank the support from NSFC.

10.  Informative References

   [Cisco15]  Cisco, Cisco., "Cisco global cloud index: Forecast and
              methodology, 2015-2020. white paper", http://www.cisco.com
              /en/US/solutions/collateral/ns341/ns525/ns537/ns705/
              ns1175/Cloud_Index_White_Paper.html#wp9000816 1-29, 2015.

   [Farrington10]
              Farrington, Nathan., Porter, George., Radhakrishnan,
              Sivasankar., Bazzaz,, Hamid., Subramanya, Vikram.,
              Fainman, Yeshaiahu., Papen, George., and Amin. Vahdat,
              "Helios: a hybrid electrical/optical switch architecture
              for modular data centers", SIGCOMM'10 339-350,
              DOI 10.1145/1851182.1851223, August 2010.

   [FENG16]   Feng, Z., Sun, W., and W. Hu, "BLOC: A Generic Resource
              Allocation Framework for Hybrid Packet/Circuit-Switched
              Networks", J. Opt. Commun. Netw. 8, 689-700,
              DOI 10.1364/JOCN.8.000689, August 2016.

   [FENG17]   Feng, Z., Sun, W., Zhu, J., Shao, J., and W. Hu, "Resource
              Allocation in Electrical/Optical Hybrid Switching Data
              Center Networks", J. Opt. Commun. Netw. 9, 648-657,
              DOI 10.1364/JOCN.8.000689, August 2017.

   [Gantz12]  Gantz, John. and David. Reinsel, "The digital universe in
              2020: Big data, bigger digital shadows, and biggest growth
              in the far east", International Data Corporation 1414_v2,
              December 2012.

   [Gauger06]
              Gauger, C., Kuhn, P., Breusegem, E., Pickavet, M., and P.
              Demeester, "Hybrid optical network architectures: Bringing
              packets and circuits together", IEEE Commun. Mag. 44(8),
              36-42, DOI 10.1109/MCOM.2006.1678107, August 2006.

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   [WANG10]   Wang, Guohui., Andersen, David., Kaminsky, Michael.,
              Papagiannaki, Konstantina., Eugene Ng, T., Kozuch,
              Michael., and Michael. Ryan, "c-Through: part-time optics
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              DOI 10.1145/1851182.1851223, August 2010.

   [Zukerman89]
              Zukerman, M., "Bandwidth allocation for bursty isochronous
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              on Communications 37(12), 1367-1371, DOI 10.1109/26.44208,
              December 1989.

Authors' Addresses

   Weiqiang Sun
   Shanghai Jiao Tong University
   800 Dongchuan Road
   Shanghai  200240
   China

   Phone: +86 21 3420 5359
   EMail: sun.weiqiang@gmail.com

   Junyi Shao
   Shanghai Jiao Tong University
   800 Dongchuan Road
   Shanghai  200240
   China

   EMail: shaojunyi@sjtu.edu.cn

   Weisheng Hu
   Shanghai Jiao Tong University
   800 Dongchuan Road
   Shanghai  200240
   China

   EMail: wshu@sjtu.edu.cn

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