Internet DRAFT - draft-multiple-layer-resource-optimization
draft-multiple-layer-resource-optimization
Cross Stratum Optimization Research Group H. Yang
Internet-Draft K. Zhan
Intended status: Informational A. Yu
Expires: 20 April 2023 Q. Yao
J. Zhang
Beijing University of Posts and Telecommunications
17 October 2022
Multiple Layer Resource Optimization for Optical as a Service
draft-multiple-layer-resource-optimization-08
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.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
1.1. Conventions Used in This Document . . . . . . . . . . . . 3
2. PREDICTION STRATEGY . . . . . . . . . . . . . . . . . . . . . 3
2.1. Artificial neural network model . . . . . . . . . . . . . 4
2.2. Adaptive artificial fish swarm artificial neural networks
(AAFS-ANN ) . . . . . . . . . . . . . . . . . . . . . . . 4
3. MULTI-PATH PRE-RESERVED RESOURCE ALLOCATION . . . . . . . . . 5
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
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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.
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 .
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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
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
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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
configuration time calculation and estimate the future network
resource demand to pre-reserve resource for traffic request.
-------------------------------------
| --- --- |
| | A |--------------| B | |
| --- \ --- \ |
| \ \ |
| \ \ |
| \ \ |
| --- --- |
| | C |---------------| D | |
| --- --- |
-------------------------------------
Figure 1: Fig.1(a) Sample network
|------------------------------------
T4 | | | | | | | | | |
|------------------------------------
T3 | | * | * | | | | | | |
|------------------------------------
T2 | | * | * | | | | | | |
|------------------------------------
T1 | | | | | | | | | |
|------------------------------------
T0 | | | | | | | | | |
-------------------------------------
S0 S1 S2 S3 S4
Figure 2: Fig.1(b) Requested resources
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|------------------------------------
T4 | | | | | | | | | |
|------------------------------------
T3 | # | * | # | # | # | | | | |
|------------------------------------
T2 | # | * | # | # | # | | | | |
|------------------------------------
T1 | # | | | # | # | | | | |
|------------------------------------
T0 | # | | | # | # | | | | |
------------------------------------
S0 S1 S2 S3 S4
Figure 3: Fig.1(c) Requested resources
|------------------------------------
T4 | | | | | | | | | |
|------------------------------------
T3 | # | # | * | | # | # | | | |
|------------------------------------
T2 | # | # | * | # | # | # | | | |
|------------------------------------
T1 | # | | | # | | | | | |
|------------------------------------
T0 | # | | | | | | | | |
------------------------------------
S0 S1 S2 S3 S4
Figure 4: 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
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slicing capability, i.e. it can provide multiple optical carriers for
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
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introduction of the adaptive step size and the visible field, making
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 |
+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
Figure 5: Tab.1 Network blocking probability of four strategies
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+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
| | 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 |
+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
Figure 6: 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,
<https://www.rfc-editor.org/rfc/rfc2119>.
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.
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[Ref2] Zhong, Z., Hua, N., and H. Liu, "Considerations of
effective tidal traffic dispatching in software-defined
metro IP over optical networks", July 2015.
[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
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
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
Email: yuao@bupt.edu.cn
URI: http://www.bupt.edu.cn/
Qiuyan Yao
Beijing University of Posts and Telecommunications
No.10,Xitucheng Road,Haidian District
Beijing
Email: yqy89716@bupt.edu.cn
URI: http://www.bupt.edu.cn/
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Jie Zhang
Beijing University of Posts and Telecommunications
No.10,Xitucheng Road,Haidian District
Beijing
Phone: +8613911060930
Email: lgr@bupt.edu.cn
URI: http://www.bupt.edu.cn/
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