[Docs] [txt|pdf] [Tracker] [Email] [Diff1] [Diff2] [Nits]

Versions: 00 01 02

Cross Stratum Optimization Research Group                        H. Yang
Internet-Draft                                                   K. Zhan
Intended status: Informational                                     A. Yu
Expires: November 6, 2019                                         Q. Yao
                                                                J. Zhang
                      Beijing University of Posts and Telecommunications
                                                             May 5, 2019


     Multiple Layer Resource Optimization for Optical as a Service
             draft-multiple-layer-resource-optimization-01

Abstract

   We have established a neural network model optimized by adaptive
   artificial fish swarm algorithm.  Then we propose a novel multi-path
   pre-reserved resource allocation strategy to increase resource
   utilization.  The results prove the effectiveness of our method.

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

   Internet-Drafts are working documents of the Internet Engineering
   Task Force (IETF).  Note that other groups may also distribute
   working documents as Internet-Drafts.  The list of current Internet-
   Drafts is at https://datatracker.ietf.org/drafts/current/.

   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."

   This Internet-Draft will expire on November 6, 2019.

Copyright Notice

   Copyright (c) 2019 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
   (https://trustee.ietf.org/license-info) in effect on the date of
   publication of this document.  Please review these documents
   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



Yang, et al.            Expires November 6, 2019                [Page 1]


Internet-DrMultiple Layer Resource Optimization for Optical a   May 2019


   the Trust Legal Provisions and are provided without warranty as
   described in the Simplified BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
     1.1.  Conventions Used in This Document . . . . . . . . . . . .   3
   2.  PREDICTION STRATEGY . . . . . . . . . . . . . . . . . . . . .   3
     2.1.  Artificial neural network model . . . . . . . . . . . . .   3
     2.2.  Adaptive artificial fish swarm artificial neural networks
           (AAFS-ANN ) . . . . . . . . . . . . . . . . . . . . . . .   4
   3.  MULTI-PATH PRE-RESERVED RESOURCE ALLOCATION . . . . . . . . .   4
     3.1.  Reconfiguration time calculation  . . . . . . . . . . . .   6
     3.2.  Multi-path pre-reserved resource allocation(MP-RA)  . . .   6
   4.  Experimental evaluation and results analysis  . . . . . . . .   7
   5.  CONCLUSION  . . . . . . . . . . . . . . . . . . . . . . . . .   9
   6.  ACKNOWLEDGMENT  . . . . . . . . . . . . . . . . . . . . . . .   9
   7.  References  . . . . . . . . . . . . . . . . . . . . . . . . .   9
     7.1.  Normative References  . . . . . . . . . . . . . . . . . .   9
     7.2.  Informative References  . . . . . . . . . . . . . . . . .   9
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  10

1.  Introduction

   With the rapid growth of cloud computing, 5G services, and the
   periodicity of people's activities, traffic load has exhibited
   periodicity in both time and space domains, namely tidal traffic [1].
   The number of people using optical metropolitan networks is enormous
   and unevenly distributed.  In addiction, the separation of work areas
   and residential areas is an important cause of tidal traffic.
   Generally, tidal traffic will reduce the performance of networks
   during to following two reasons: firstly, the network traffic will be
   blocked due to the sharp increase in traffic in the high-traffic
   area; secondly, network nodes may be idle and waste resources in the
   low-traffic areas.  The static configuration resources will intensify
   both network and service congestion during traffic peak hours, as
   well as low resource utilization during low-traffic times and
   regions.  In the future, global mobile Internet traffic will increase
   by 10 times [2], urbanization is rapidly advancing, the scope and
   severity of space and time domains affected by tidal traffic are
   increasing as communication need and network technologies developing.
   Tidal traffic will further affect the optical access network and the
   optical core network, making it essential issue for network
   operators.  Therefore, a more reasonable and efficient resource
   allocation scheme is urgently needed to solve the congestion and
   resource waste caused by the tidal traffic.





Yang, et al.            Expires November 6, 2019                [Page 2]


Internet-DrMultiple Layer Resource Optimization for Optical a   May 2019


   Known from the above, tidal traffic prediction becomes the core
   process of network optimization decision-making.  Currently, there
   are several prediction methods, like support vector machine (SVM) and
   multi-layer perceptron (MLP).  Literature [3] proposes a deep-
   learning-based prediction strategy to implement traffic assessment of
   data center optical networks.  At the same time, a deep-learning-
   based global evaluation factor resource allocation algorithm is
   suggested to achieve lower blocking rate of the network.  Compared
   with the traditional algorithm, deep learning can improve the
   accuracy of prediction, but it cannot identify the tidal traffic in
   specific festivals.  In addition, the lower priority service will be
   discarded to reduce the network blocking rate.  This method does not
   make good use of idle resources of other nodes, and some traffic
   requests cannot be executed normally.  So we propose multi-path pre-
   reserved resource allocation based on traffic prediction.

   In this paper, we establish an adaptive artificial fish-group neural
   network model to predict traffic, then use the predicted traffic
   demand to optimize the network at different times.  Meanwhile, we
   propose multi-path pre-reserved resource allocation to adapt to the
   resource requirements of different nodes.  Simulation results
   demonstrate that our strategy achieves a lower network blocking rate
   and higher resource utilization.

1.1.  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].

2.  PREDICTION STRATEGY

   Before presenting the resource allocation algorithm, we provide an
   introduction to traffic prediction model.  We establish a neural
   network of adaptive artificial fish algorithm to predict traffic
   request.  The key resides in the construction of the artificial fish
   individual model.  The optimal variables of the neural network are
   two weight matrices and two threshold variables _io,v_o .

2.1.  Artificial neural network model

   We build the neural network structure as shown in figure 1.  The
   input is composed of six entries, i_(s,1) is the hour of the day,
   i_(s,2) is the day of the week, i_(s,3) is a flag for holiday/
   weekend, i_(s,4) is the previous days average load, i_(s,5) is the
   load from the same hour of the previous day, and i_(s,6) is the load
   from the same hour and same day from the previous week.  The result
   of the output node Y_(s,1) represents the traffic request that we



Yang, et al.            Expires November 6, 2019                [Page 3]


Internet-DrMultiple Layer Resource Optimization for Optical a   May 2019


   want to predict[1].  Training sample setA={(X^i,Y^i )|i=1,2,,n}X^i is
   the i_th group training data input, and Y^i is the i_th group input
   corresponding expected output.  We define the error function as
   follows:

   where O^i is the actual output of the i_th.

2.2.  Adaptive artificial fish swarm artificial neural networks (AAFS-
      ANN )

   In the artificial fish swarm algorithm, we introduce adaptive step
   size and visible range to improve convergence accuracy and speed.
   Generate initial artificial fish population N, namely N group
   {omega_ij,nu_io,omega_io,nu_o}. Every artificial fish is a neural
   network.  The food concentration is defined as FC=1/E.  X_i is the
   state of current location state,X_j is random state of the
   search,d_ij is the distance between X_i and X_j, omega_ij
   (i),omega_ij (j) and omega_ij (i+1) respectively are X_i,X_jnext
   state X_(i+1) matrix omega_ij} element of i_th row j_th column,
   "Rand(Step") represents a random number between [0, Step].

   Let X_0 be the current artificial fish, its position is C, X_1 is the
   current optimal fish, X_2 is the nearest fish, Then we set two
   visible fields viusual_1=d_01,viusual_2=d_02.  Two target positions
   A, B are randomly determined in the range of viusual_1 and viusual_2
   respectively, then compare FC_A,FC_B,FC_C,

   If FC_A,FC_B

   If FC_A,FC_B

   omega(i+1)=omega(i)+Rand(step)

   If one or both of them are better than C, Then advance to the best
   point, and execute formula (3)

   omega(i+1)=omega(i)+Rand(step)(omega(j)-omega(i))/d_ij

   Go for A with viusual_1Rand() as the step size, to B with
   viusual_2alphaRand(), where a ,which equal to 1 or slightly less than
   1,is the visual factor.The other three optimization variables are
   similarly.

3.  MULTI-PATH PRE-RESERVED RESOURCE ALLOCATION

   The resource allocation method bases on the AAFS-ANN described above,
   and we propose a multi-path pre-reserved resource allocation way to
   optimize optical network.  We uses the predicted result to perform



Yang, et al.            Expires November 6, 2019                [Page 4]


Internet-DrMultiple Layer Resource Optimization for Optical a   May 2019


   configuration time calculation and estimate the future network
   resource demand to pre-reserve resource for traffic request.


                  -------------------------------------
                  |     ---                ---         |
                  |    | A |--------------| B |        |
                  |     --- \              --- \       |
                  |          \                  \      |
                  |           \                  \     |
                  |            \                  \    |
                  |           ---                 ---  |
                  |          | C |---------------| D | |
                  |           ---                 ---  |
                  -------------------------------------

                          Fig.1(a) Sample network


                     |------------------------------------
                  T4 |   |   |   |   |   |   |   |   |   |
                     |------------------------------------
                  T3 |   | * | * |   |   |   |   |   |   |
                     |------------------------------------
                  T2 |   | * | * |   |   |   |   |   |   |
                     |------------------------------------
                  T1 |   |   |   |   |   |   |   |   |   |
                     |------------------------------------
                  T0 |   |   |   |   |   |   |   |   |   |
                     -------------------------------------
                       S0  S1  S2  S3  S4

                       Fig.1(b) Requested resources


















Yang, et al.            Expires November 6, 2019                [Page 5]


Internet-DrMultiple Layer Resource Optimization for Optical a   May 2019


                     |------------------------------------
                  T4 |   |   |   |   |   |   |   |   |   |
                     |------------------------------------
                  T3 | # | * | # | # | # |   |   |   |   |
                     |------------------------------------
                  T2 | # | * | # | # | # |   |   |   |   |
                     |------------------------------------
                  T1 | # |   |   | # | # |   |   |   |   |
                     |------------------------------------
                  T0 | # |   |   | # | # |   |   |   |   |
                      ------------------------------------
                           S0  S1  S2  S3  S4

                       Fig.1(c) Requested resources


                     |------------------------------------
                  T4 |   |   |   |   |   |   |   |   |   |
                     |------------------------------------
                  T3 | # | # | * |   | # | # |   |   |   |
                     |------------------------------------
                  T2 | # | # | * | # | # | # |   |   |   |
                     |------------------------------------
                  T1 | # |   |   | # |   |   |   |   |   |
                     |------------------------------------
                  T0 | # |   |   |   |   |   |   |   |   |
                      ------------------------------------
                           S0  S1  S2  S3  S4

                       Fig.1(d) Requested resources

3.1.  Reconfiguration time calculation

   Frequent reconfiguration can result in service interruption and
   unstable of distributed routing algorithm, so we need to predict the
   next 24-hour traffic demand D^24 for the next configuration time
   point calculation.  Algorithm 1 is the calculation process of the
   reconfiguration time point.

3.2.  Multi-path pre-reserved resource allocation(MP-RA)

   We reserve network resources for the predicted traffic.  This type of
   service request is called Advance Reservation Service (AR).  As the
   optical link is continuously established and removed, fragments are
   easily generated in both the time domain and the spectrum domain.
   The application of the Sliceable Bandwidth Variable Transceiver
   (S-BVT) [4] further enhances the flexibility of EON.  The S-BVT has a
   slicing capability, i.e. it can provide multiple optical carriers for



Yang, et al.            Expires November 6, 2019                [Page 6]


Internet-DrMultiple Layer Resource Optimization for Optical a   May 2019


   carrying optical links to different destinations.  In order to reduce
   time and spectral fragmentation (referred to as two-dimensional
   fragmentation) and to solve the problem of insufficient resources, we
   propose cutting the request into multiple parts, and transfer on
   multiple paths.

   The underlying optical network can be modeled as
   G_s=(L_s,N_s,R_st,D_s){ L_s: link set, N_s: optical node set, R_st:
   resource status of optical nodes and optical links at time t, D_s:
   distance of each pair of nodes in the set of nodes N in the network
   topology}. R_A=(s,d,w,b,h) denotes a predicted service request, where
   s and d represent the source and destination nodes of the service, b
   is the time of service starts, h is the duration of the AR service,
   and w is the service start time b, and the duration h period required
   link rate.  P_((s,d)) represents the path set of the source node to
   the destination node.

   If there are not enough spectrum resources available in the link for
   the incoming request, we will attempt to cut the request into
   different parts and assign those parts to different frequency bands.
   For a simple example, as shown in Firgue 3(a), in order to reflect
   the state of the spectral resources in the time domain, we use a two-
   dimensional time spectrum resource model and assume that each time
   slot has the same time period.  The network diagram is illustrated in
   the figure 3(a).  Now there was an AR request, from node A to node D.
   The request requires two spectrum slots, lasting from T2 to T3, as
   shown in figure 2(b).  Figure 2(c) and Figure 2(d) show the spectrum
   states of path A-C-D and path A-B-D, respectively.  The black slot
   represents the occupied spectrum slot, the white slot represents the
   spectrum slot available for the spectrum resource, and the blue slot
   represents the spectrum slot occupied by the AR request.  Before
   splitting the AR request, the two paths do not have enough resources
   to allocate.  However, after we split the request into two parts, we
   can distribute them to two spectrum segments to implement AR-
   requested service provision.  The MP-RA is as shown in Algorithm 2.

4.  Experimental evaluation and results analysis

   In this paper, we present the results of the AAFS-ANN prediction.
   Our goal is to demonstrate the accuracy and network performance of
   AAFS-ANN in different network environments.  To fully reflect the
   changes in the network environment, we use WIDE data from 96h traffic
   data from April 6th to 9th, 2017, to train and verify.  Figure 3(a)
   is the comparison between the actual traffic and the prediction
   results, which verify the effectiveness of our method.  As shown in
   Figure 3(a), the prediction results of AAFS-ANN are significantly
   better than the traditional predictions.  This is because the
   introduction of the adaptive step size and the visible field, making



Yang, et al.            Expires November 6, 2019                [Page 7]


Internet-DrMultiple Layer Resource Optimization for Optical a   May 2019


   the artificial fish compares the FC in the large field of view.  Our
   method enhances the global convergence and the optimization
   precision.  The prediction error occurs because the traffic is
   directly affected by many non-linear sudden factors such as hot
   events, user movement patterns.  Therefore, many traffic cannot be
   accurately predicted.

   We also compare MP-RA with several state-of-the-art resource
   allocation techniques including evolutionary algorithms(EA) and
   artificial neural networks (ANN).  From firgue 3(b), we can see that
   MP-RA performs well among the three optimization resource allocation
   method, MP-RA greatly improves resource utilization.

   According to the prediction results, the MP-RA can allocate resources
   to traffic more reasonably.  This is because the algorithm considers
   the traffic that will be reached at each point in time and the
   resources it needs.  Then re-plans the resources at the configuration
   time.  As can be observed in the results shown in Figure 3(c), MP-RA
   can greatly reduce the probability of traffic blocking.


           +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
           |               |   Resource utilization rate       |
           | Traffic load  +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
           |               |   MP-RA   |   ANN    |    EA      |
           +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
           |     40        |  0.254    |  0.242   |  0.251     |
           |     70        |  0.263    |  0.253   |  0.272     |
           |     95        |  0.273    |  0.275   |  0.300     |
           |     120       |  0.332    |  0.29    |  0.420     |
           |     145       |  0.389    |  0.325   |  0.504     |
           |     170       |  0.457    |  0.356   |  0.583     |
           |     200       |  0.52     |  0.403   |  0.723     |
           +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+

           Tab.1 Network blocking probability of four strategies















Yang, et al.            Expires November 6, 2019                [Page 8]


Internet-DrMultiple Layer Resource Optimization for Optical a   May 2019


           +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
           |               |   Network blocking probability    |
           | Traffic load  +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
           |               |   MP-RA   |   ANN    |    EA      |
           +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
           |     50        | 0.008     | 0.0075   |   0.0078   |
           |     70        | 0.009     | 0.010    |   0.012    |
           |     100       | 0.0095    | 0.025    |   0.029    |
           |     125       | 0.01      | 0.06     |   0.074    |
           |     150       | 0.0108    | 0.08     |   0.10     |
           |     175       | 0.025     | 0.115    |   0.129    |
           |     200       | 0.06      | 0.15     |   0.20     |
           +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+

                   Tab.2 Average hop of four strategies

5.  CONCLUSION

   In the tidal traffic scenario, we propose AAFS-ANN model and MP-RA
   strategy.  We use AAFS-ANN model to predict traffic and MP-RA to
   optimize metropolitan optical network.  Results demonstrate that
   AAFS-ANN and MP-RA successfully increase prediction accuracy and
   resource utilization, as well as reduce the traffic blocking rate.

6.  ACKNOWLEDGMENT

   This work has been supported in part by NSFC project (61871056),
   National Postdoctoral Program for Innovative Talents (BX201600021),
   Fundamental Research Funds for the Central Universities (2018XKJC06)
   and State Key Laboratory of Information Photonics and Optical
   Communications (BUPT), P.  R.  China (No.  IPOC2017ZT11).

7.  References

7.1.  Normative References

   [RFC2119]  Bradner, S., "Key words for use in RFC's to Indicate
              Requirement Levels", RFC 2119, March 1997.

7.2.  Informative References

   [Ref1]     Alvizu, R., Troia, S., and G. Maier, "Matheuristic with
              machine-learning-based prediction for software-defined
              mobile metro-core networks", May 2017.

   [Ref2]     Zhong, Z., Hua, N., and H. Liu, "Considerations of
              effective tidal traffic dispatching in software-defined
              metro IP over optical networks", July 2015.



Yang, et al.            Expires November 6, 2019                [Page 9]


Internet-DrMultiple Layer Resource Optimization for Optical a   May 2019


   [Ref3]     Yu, A., Yang, H., and W. Bai, "Leveraging deep learning to
              achieve efficient resource allocation with traffic
              evaluation in datacenter optical networks", March 2018.

   [Ref4]     Zhong, Z., Hua, N., and M. Tornatore, "Energy efficiency
              and blocking reduction for tidal traffic via stateful
              grooming in IP-over-optical networks", September 2016.

Authors' Addresses

   Hui Yang
   Beijing University of Posts and Telecommunications
   No.10,Xitucheng Road,Haidian District
   Beijing  100876
   P.R.China

   Phone: +8613466774108
   Email: yang.hui.y@126.com
   URI:   http://www.bupt.edu.cn/


   Kaixuan Zhan
   Beijing University of Posts and Telecommunications
   No.10,Xitucheng Road,Haidian District
   Beijing  100876
   P.R.China

   Phone: +8618401695826
   Email: zhankai@bupt.edu.cn
   URI:   http://www.bupt.edu.cn/


   Ao Yu
   Beijing University of Posts and Telecommunications
   No.10,Xitucheng Road,Haidian District
   Beijing  100876
   P.R.China

   Email: yuao@bupt.edu.cn
   URI:   http://www.bupt.edu.cn/











Yang, et al.            Expires November 6, 2019               [Page 10]


Internet-DrMultiple Layer Resource Optimization for Optical a   May 2019


   Qiuyan Yao
   Beijing University of Posts and Telecommunications
   No.10,Xitucheng Road,Haidian District
   Beijing  100876
   P.R.China

   Email: yqy89716@bupt.edu.cn
   URI:   http://www.bupt.edu.cn/


   Jie Zhang
   Beijing University of Posts and Telecommunications
   No.10,Xitucheng Road,Haidian District
   Beijing  100876
   P.R.China

   Phone: +8613911060930
   Email: lgr@bupt.edu.cn
   URI:   http://www.bupt.edu.cn/
































Yang, et al.            Expires November 6, 2019               [Page 11]


Html markup produced by rfcmarkup 1.129d, available from https://tools.ietf.org/tools/rfcmarkup/