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OFDM-PON-based software-defined elastic optical access network
Optical Fiber Technology 55 (2020) 102136
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Joint multi-dimensional resource allocation algorithm for a TWDM/OFDMPON-based software-defined elastic optical access network
T
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Bingchang Hua, Zhiguo Zhang , Lei Wang State Key Lab of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing, China
A R T I C LE I N FO
A B S T R A C T
Keywords: TWDM/OFDM-PON Software defined elastic optical access network Joint multi-dimensional resource allocation
With the rapid growth of high-bandwidth services such as 5G, access networks only rely on physical layer technology to build a large-scale rigid pipeline, but cannot meet the need for the intelligent, flexible, and open networks of future business. Therefore, we herein propose a fairness-aware joint multi-dimensional resource allocation algorithm. It is based on software-defined elastic optical access network architecture and is intended to improve network throughput and the quality of service users experience. The simulation results show that the network throughput and user satisfaction rate of the proposed algorithm increases by about 30% during peak traffic periods compared with fixed allocation. The proposed algorithm can ensure that all users utilize the system bandwidth fairly.
1. Introduction Over the past decade, with the emergence of 5G and the expansion of various high-bandwidth services such as high-definition video and human interaction, the bandwidth demand of the access network has exploded. The Cisco Global Mobile Data Traffic Forecast Update (2016–2021) predicts that monthly global mobile data traffic will be 49 exabytes by 2021, leading to annual traffic that will exceed half a zettabyte [1]. Unfortunately, the access network only relies on physical layer technical progress to build large and rigid pipelines, which cannot meet the demand for the intelligent, flexible, and open networks needed in the future. Therefore, for access network operators, how to achieve efficient, intelligent, flexible, and open access networks is a huge challenge. For example, an elastic optical access network architecture was proposed [2] and an orthogonal frequency division multiple access passive optical network (OFDMA-PON) was applied to the PON system. OFDMPON utilizes subcarriers as logical links, which can be occupied by different service types and users, thus enabling more flexible multiservice and multi-user bandwidth allocation [3]. The elastic optical access network system can dynamically allocate wavelength and subcarrier resources, effectively improving spectrum efficiency and bandwidth allocation flexibility, and thereby improving transmission distance and performance [4,5]. Many researchers, through the introduction of software defined network (SDN) technology, reconfigurable optical distribution networks (ODN), and wavelength
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selection switches (WSS) have tried to achieve flexibility of optical access networks and the effective integration of multiple services. These are intended to achieve the goal of saving network construction costs [6–9]. At the same time, operators need flexible resource allocation strategies to make full and efficient use of these network bandwidth resources and to avoid waste of resources caused by rigid pipeline operation and maintenance methods. For example, SDN technology was introduced to the access network, to realize efficient and flexible use of access network resources through the centralized control characteristics of SDN [10]. The NTT Access Network Service System Laboratory proposed a dynamic wavelength balancing (DLB) algorithm that distributes the traffic load between optical line terminal (OLT) ports fairly, and achieves rapid balance between OLT ports by transferring the largest matching optical network unit (ONU) in the OLT port, so as to minimize the required wavelength conversion. A fast wavelength tuning scheme was also put forward to achieve ONU without any dataframe-loss wavelength conversion, to ensure a good user experience [11,12]. Ref. [13] proposed an adaptive modulation format and a number of sub-carriers allocation algorithm using the distance from a PON section, quality of service (QoS), and bandwidth demands of each ONU for a WDM/OFDM hybrid aggregation network. The proposed algorithm can reduce the total bandwidth by selecting optical parameters adaptively to achieve steady high-spectrum efficiency in any traffic scenario. Ref. [14] proposed a fairness-aware dynamic subcarrier allocation (DSA) in a distance-adaptive modulation OFDMA-PON and
Corresponding author. E-mail address: [email protected] (Z. Zhang).
https://doi.org/10.1016/j.yofte.2019.102136 Received 13 September 2019; Received in revised form 4 December 2019; Accepted 31 December 2019 1068-5200/ © 2020 Elsevier Inc. All rights reserved.
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2. System architecture
experimentally demonstrated it for elastic lambda aggregation networks (EλANs). By applying the distance-adaptive modulation function to OFDMA-PONs, the system could change the transmission parameters (modulation level and symbol rate) for each user according to the endto-end optical loss of each user, which would yield additional bits. Meanwhile, by applying our proposed DSA algorithm, which allocates sub-carriers to users based on transmission parameters and traffic demands, the additional bits could be shared by all users in a fair manner regardless of a user’s end-to-end optical loss. There is also a great deal of research on bandwidth allocation of access networks [15–18]. These studies achieved energy saving or quality of experience (QoE) enhancement through allocation of resources such as wavelength and time slots. However, the elastic optical access network includes multiple dimensional resources (e.g. wavelength, subcarrier, modulation format, time slots). Nevertheless, the current research is mostly focused on allocation of resources in one or two dimensions, which lacks the joint multi-dimensional resource control of an elastic optical access network. In view of the inefficiency and rigidity of the current access network, we propose a fairness-aware joint multi-dimensional resource allocation algorithm (JMRA) based on software-defined elastic optical access network architecture. This includes mainly the following four sub-algorithms for wavelength redirection (WR), dynamic load balancing (DLB), maximum fit decreasing (MFD), and dynamic subcarrier allocation (DSA). Wavelength redirection is mainly to balance the wavelength resources between areas. Dynamic load balancing and maximum fit decreasing are to balance the load between wavelengths in the same area. Dynamic subcarrier allocation is to equalize the bandwidth resources of the BWG under each wavelength. Through the balance achieved with the above algorithm, the throughput and user satisfaction rate are improved. The remainder of this paper is organized as follows. Section 2 introduces the software elastic optical access network architecture. Section 3 describes in detail the fairness-aware joint multi-dimensional resource algorithm. Simulation results are presented and analyzed in Section 4 and conclusions are given in Section 5.
Fig. 1 depicts the structure of the TWDM/OFDM-PON-based software defined elastic optical access network, which consists of SDN controller, OLT, remote node, wavelength tunable ONU, and dense wavelength division multiplexing (DWDM) fiber ring. The SDN controller centrally controls the reconfigurable of optical switch array and remote node (RN), the wavelength selection of ONU, resource allocation and survival guarantee of the whole network. The OLT and the remote node (RN) are connected together by a bidirectional transmitted DWDM fiber optic ring. The RN and the ONU are connected by an optical splitter. With the fiber ring, the RNs can be deployed in different locations in the city, and ultimately deliver service to the end users through splitters. The OLT includes n service line cards (LC1, ...,LCn ), an optical switch array, and two 50 GHz channel-spaced AWGs, each of the line cards contains different fixed-wavelength transceivers to provide different wavelength options. The downstream service wavelength is multiplexed by the AWG and broadcast to all remote nodes in the counterclockwise direction of the DWDM ring. After a fault such as a DWDM ring break, the SDN controller controls the optical switch array to switch the designated service line card to the other side of the DWDM ring to ensure normal service of the remote node below the fault point node. The RN is composed of four WSS [8]. Through the WSS, multiple working wavelengths running on the DWDM ring can be dynamically allocated among the RNs under control of the SDN controller. Due to the WSS wavelength selection feature, the service wavelength on the DWDM ring cannot be shared by all RNs. When one RN delivers a wavelength, other RNs cannot use it. The service wavelength delivered by the RN is broadcast to all wavelength tunable ONUs via an optical splitter, and the wavelength bandwidth resources are shared by all ONUs in the area. In the system, all ONUs in the area share the service wavelength delivered by the RN interface. The group of ONUs using the same transmission parameters (modulation format, symbol rate) at the same wavelength is defined as a bandwidth group (BWG) [12]. The ONUs in the BWG share a series of subcarriers within the wavelength, and TDMA can also be implemented in the BWG. On the one hand, high-order
Fig. 1. TWDM/OFDM-PON-based software-defined elastic optical-access network system architecture. 2
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Step VI: Use the MFD algorithm in area j to assign wavelengths to unallocated ONUS; then turn to Step IV. Step VII: Use DLB algorithm in area j to perform load balancing between wavelengths; then turn to Step IV. Step VIII: If traverse all wavelengths, turn to Step XI. Otherwise to Step IX. Step IX: If overloaded BWGs occur in wavelength w, turn to Step X. Otherwise to Step VIII. Step X: Use the DSA algorithm in wavelength w to perform load balancing between BWGs; then turn to Step VIII. Step XI: End.
modulation format signals have higher spectrum utilization under certain conditions of system bandwidth. On the other hand, high-order modulation format signals shorten the distance between constellation points, and the corresponding signal decision regions are also reduced. Thus, the anti-jamming capability is impaired and the bit error rate is large. Thus, the adaptive modulation of distance is considered under the condition of a constant baud rate, the higher-order transmission parameters are mapped to the ONUs closer to the OLT to provide high system capacity, and the low-order transmission parameters are mapped to the ONU farther away from the OLT to improve the antijamming performance of the signals in transmission. 3. Multi-dimensional resource joint allocation algorithm
In order to facilitate understanding of the proposed JMRA algorithm, the WR, MFD, DLB, and DSA algorithms used therein are described in detail below.
Under the TWDM/OFDM-PON-based software-defined elastic optical-access-network system, we proposed a fairness-aware wavelength, sub-carrier and modulation format joint multi-dimensional resource allocation algorithm. To describe the proposed scheme, we define the following sets, parameters and variables below. Sets:
3.1. Wavelength redirection (WR) algorithm Because the ONU request bandwidth is different in each area, some areas may have a light load, while others may have network congestion. Based on this situation, we propose an inter-area wavelength redirection algorithm to redirect some wavelengths from a light-load area to a area that is congested, thereby increasing the bandwidth satisfaction rate of the network congested area. The WR algorithm achieves the purpose of balancing the load between areas by redirecting the wavelengths. This improves throughput of the network and the bandwidth satisfaction rate of the ONU through the WR algorithm. The algorithm is summarized in Table 1.
I : set of ONUs. J : set of areas. W : set of wavelengths. K : set of BWGs. Uj : set of unallocated ONU in area j. Parameters:
3.2. Dynamic load balancing (DLB) and maximum fit decreasing (MFD) algorithm
BW _ONUi is the bandwidth request of ONU i. BW _Areaj is the total request bandwidth of area j. BW _Ww is the total request bandwidth of wavelength w. BW _BWGk is the total request bandwidth of BWG k. C is the maximum bandwidth of each wavelength. Bk is the transmission rate of BWG k.
At the same time, the load of some wavelengths may be light, while other wavelengths may have network congestion. Therefore, we propose the use of DLB and MFD algorithms to balance the wavelengths in each area to reduce network congestion and improve the bandwidth satisfaction rate of the ONU. If there are unallocated ONUs in an area, the unallocated ONU is allocated to each wavelength in the area using the MFD algorithm. Otherwise, the DLB algorithm is adopted to redistribute the ONU of the heavy load wavelength to the light load wavelength. It is worth noting that the ONU transmission parameters (modulation format and symbol rate) remain the same, whether the ONU is reassigned to any wavelength or not. In step 3, the role of the target ONU with the requested bandwidth closest to 2 is selected to reduce the amount of wavelength switching of the ONU, thus effectively reducing delay caused by wavelength switching. This algorithm is summarized in Table 2.
Variables:
Numj is the number of wavelengths in area j. Xk is the number of subcarrier in BWG k. Sj is the congestion factor of area j, defined as Sj = BW _Areaj (Numj × C ) . Zk is the congestion factor of BWG k, defined as Zk = BW _BWGk (Bk × Xk ) . M (h) is the maximum congestion factor of the area over a period of time. N (h) is the maximum congestion factor of the BWGs in wavelength over a period of time.
3.3. Dynamic subcarrier allocation (DSA) algorithm
With the mapping between areas, BWGs, wavelengths and ONUs are given, the goal is to minimize the OLT congestion factor and for all ONUs to use bandwidth resources fairly. The process of the proposed algorithm is fully described below; a flowchart is illustrated in Fig. 2.
As described above, in order to ensure the transmission performance of the system, the transmission parameters of the high-order modulation format can only be mapped to the ONU that is closest to the OLT, and the ONUs that are far away from the OLT only use the low-order modulation format. However, in the case of using the same number of subcarriers, the ONUs that are farther away can carry less bandwidth, resulting in a decrease in the user bandwidth satisfaction rate. Therefore, in the case of network congestion, the DSA algorithm is used to realize fair utilization of bandwidth resources between BWGs, which can maximize the user experience. This algorithm is summarized in Table 3.
Step I: The request bandwidth from all ONUs in the OLT are given, and used to calculate load and congestion factors for all areas, wavelengths, and BWGs. Step II: If Sj > 1, indicating area j overload, turn to Step III. Otherwise turn to Step IV. Step III: Rebalance overloaded areas using the WR algorithm. When a certain wavelength from area j is redirected to another area, the ONU originally allocated to the wavelength is placed in Uj . Step IV: If traverse all areas, turn to Step VIII. Otherwise to Step V. Step V: If unallocated ONUs occur in area j, turn to Step VI. Otherwise to Step VII.
4. Simulation results The simulation system consisted of an SDN controller and a PON system, as shown in Fig. 1. The number of areas in the OLT was 4, the 3
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Fig. 2. Flowchart of joint multi-dimensional resource allocation algorithm.
according to the change of bandwidth requested by the ONU in each cycle, the number of wavelengths in each area and the number of ONUs per wavelength were changed accordingly, and the number of subcarriers between different BWGs for each wavelength varied, but the sum was still fixed at 48. Traffic requests for different Internet users in certain areas, such as the city’s central business district and residential areas, are repeatedly relocated. During the day, the ONU load is heavy in business areas while it is light in residential areas. On the contrary, at night, the ONU
number of wavelengths was 4, the number of ONUs was 512, the number of subcarriers for each wavelength was 48 and the maximum transmission rate of each wavelength was 50 Gbps. The symbol rate defined in the transmission parameters was 156 M symbol/s. Each PON port contained 4 BWGs, BWG#1 (QPSK, B), BWG#2 (8QAM, B), BWG#3 (16QAM, B), and BWG#4 (64QAM, B). Each area had 4 wavelengths and 128 ONUs during initialization. In advance, 128 ONUs were evenly distributed to 4 BWGs according to the distance between the ONU and the OLT. Each BWG had 8 ONUs and 12 subcarriers. Then, 4
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Table 1 Wavelength Redirection (WR) Algorithm.
Table 3 Dynamic subcarrier allocation (DSA) algorithm.
Algorithm 1: Wavelength Redirection (WR) Algorithm
Algorithm 3: Dynamic subcarrier allocation (DSA) algorithm
1: Initialize the mapping between ONUs, BWGs, Wavelengths, Areas 2: Calculate congestion factor Sj of areas 3: if: all of the areas under the OLT are satisfied with Sj < 1; go to step 7 else: go to step 4 4: Find the maximum Sj, put j into j_max; find the minimum Sj, put j into j_min least loaded wavelength in area j_max is redirected to area j_min Numj_max = Numj_max + 1, M(h) = Sj_max, h = h + 1 Numj_min = Numj_min − 1 5: if: Numj ≥ 1; and: M(h) ≠ M(h + 1); then: go to step 2 else: go to step 6 6: Numj_max = Numj_max-1, Numj_min = Numj_min + 1 7: The ONU originally allocated to the redirected wavelength in area j placed in Uj 8: End the wavelength redirection of this timeslot.
1: Traverse four BWGs on each wavelength, calculate Zk 2: if: all of the BWGs under the wavelength are satisfied with Zk < 1; go to step 7 else: go to step 3 3: Find the maximum Zk, put k into k_max; find the minimum Zk, put k into k_min Xj_max = Xj_max + 1,N(h) = Zk_max, h = h + 1 Xj_min = Xj_min − 1 4: Calculate BW_WW 5: if: BW_WW ≤ C; and: Xk ≥ 0; and: N(h) ≠ N(h + 1); then: go to step 2 else: go to step 6 6: Xj_max = Xj_max-1, Xj_min = Xj_min + 1 7: if: Traverse all wavelengths; then: go to step 1 else: repeat step 8 8: End the dynamic subcarrier allocation of this timeslot.
Table 2 Dynamic load balancing (DLB) and maximum fit decreasing (MFD) algorithm.
user’s request bandwidth.
Sa = BW _alloc BW _req
Algorithm 2: Dynamic load balancing (DLB) and maximum fit decreasing (MFD) algorithm
The performance comparison of total OLT throughput and user satisfaction rate between the proposed joint multi-dimensional resource allocation algorithm and the other four algorithms was verified. Definition algorithm 1 was the JMRA proposed in this paper; algorithm 2 was a DLB-DSA algorithm that only performed dynamic load balancing within areas and dynamic distribution within wavelengths. Algorithm 3 was a WR-DLB algorithm that only performed inter-area wavelength redirection and load balancing within areas. Algorithm 4 was a DSA algorithm that only performed dynamic distribution within wavelengths [14], and algorithm 5 was a fixed allocation (FA) without any algorithm optimization. Fig. 3(b) and (c) depict simulation results of the OLT total throughput and user satisfaction rate, respectively, for the five algorithms. It can be seen that the proposed JMRA algorithm performs best in terms of throughput and user satisfaction rate compared with the other four algorithms. During peak traffic periods, an increase of up to 30% bandwidth satisfaction rate and throughput (compared to DLBDSA, DSA and FA algorithms) occurred, and was close to satisfying the bandwidth requests of all users. The throughput and user satisfaction rate of the WR-DLB algorithm were also significantly better than with the other three algorithms. This is because the WR algorithm redirects the wavelength of light-load areas to heavy-load areas, effectively reducing the network congestion rate of the heavy-load areas and improving the user bandwidth satisfaction rate. Compared with the JMRA algorithm, the performance of the WR-DLB algorithm was slightly inadequate. It can be concluded that the DSA algorithm can greatly improve the user satisfaction rate and throughput by balancing the number of subcarriers between BWGs. At the same time, by comparing the performance of the DLB-DSA, DSA, and FA algorithms, it was discovered that performing the DLB and DSA algorithms does not improve network performance without inter-area wavelength redirection. It is worth noting that the DSA algorithm achieved fairness in the satisfaction rate among users, avoiding the situation in which some users are satisfied and some have no bandwidth. Fig. 4(a) describes the curve of the total request bandwidth in the areas when the JMRA algorithm is used. Fig. 4(b) shows the curve of the changing of number of area wavelengths with time when the JMRA algorithm was adopted. As can be seen, the number of wavelengths in the area varies with the requested bandwidth. As the total requested bandwidth of a area user increases, the number of wavelengths may also increase to provide more network bandwidth resources to maximize the user's bandwidth request. In Fig. 4(c)–(f), we plotted user satisfaction rate curves for areas under the five algorithms. In each area, more substantial user satisfaction rate improvement was achieved using the JMRA algorithm than with the other algorithms. However, at time
1: j = 1 2: if: Uj is empty; go to step 7 else: go to step 3 Max fit deceasing (MFD) subroutine 3: Traverse all wavelengths on area j, calculate: BW_WW=∑BW_BWGk (BWGk belong to wavelength w) 4: Sort the ONUs in Uj in decreasing order of their loads; ONUmax is the heavy-loaded ONU in Uj 5: ONUmax allocated to the light-loaded wavelength in area j, remove the ONUmax from the Uj 6: if: Uj is empty; j = j + 1 if: j < J; go to step 2 else: go to step 12 else: go to step 4 Dynamic load balancing (DLB) subroutine 7: Traverse all wavelengths on area j, calculate: BW_WW = ∑BW_BWGk (BWGk belong to wavelength w) 8: Calculate the optimal load balancing optimization value Dload according to the formula: Dload = BW_ONUi_max-BW_ONUi_min 9: Find the target ONU with BW_ONUi_target closest to Dload/2 from the heavy-loaded wavelength 10: if: BW_ONUi_target < Dload Move target ONU to the light-loaded wavelength; then: go to step 11 else: go to step 12 11: if: Dload < BW_ONUi_min; j = j + 1 if: j < J; go to step 2 else: go to step 12 else: go to step 7 12: End the Dynamic load balancing (DLB) and max fit deceasing (MFD) of this timeslot
load is light in business areas but it is heavy in residential areas. There is a phenomenon of complementary traffic requests between the commercial areas and residential areas. This tidal effect factor leads to the need for real-time adjustment of bandwidth. Hence, the simulation sets the user traffic model of the business area and residential area, as shown in Fig. 3(a). There were 256 ONU users in the business area and 256 ONU users in a residential area. The users in area#1 and area#2 were all in the business area, and the users in area#3 and area#4 were all in the residential area. All users generate a bandwidth request before each time slot begins. And users request bandwidth are normally distributed, the expectation at each timeslot is shown in Fig. 3(a), with a variance of 0.3. We divided 24 h a day into an average of 24 time slots. We defined the user satisfaction rate as the ratio of the bandwidth provided by the system to user traffic requests, as shown in the following formula, where Sa indicates the user satisfaction rate, BW_alloc indicates the bandwidth allocated to the user, and BW_req indicates the 5
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Fig. 3. (a) Average traffic of users in two areas, (b) Total throughput for the OLT, (c) Total satisfaction rate of users.
Fig. 4. (a) Request bandwidth for the area (b) Number of wavelengths of the area under JRMA algorithm (c) (d) (e) (f) Satisfaction rate of users in area#1–4, respectively.
Fig. 5. (a) (b) (c) Fairness index for the OLT, wavelength#1 and wavelength#4, respectively.
resources of the whole area were greatly reduced, thus causing network congestion. Jain's fairness index [19] is used to measure the fairness of resource allocation results, as shown in the following formula, where x n indicates the user satisfaction rate of user n.
21 in area#2, when the DLB-DSA, DSA, and RA algorithms were all 1, the user satisfaction rate of the JMRA and WR-DLB algorithms was not 1 (i.e., the user bandwidth request could be fully satisfied). This is because the JMRA and WR-DLB algorithms performed wavelength redirection, and the number of wavelengths in area#2 was no longer 4 (see Fig. 4b). Compared with the other three algorithms, the network
6
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Fig. 6. (a) Subcarrier number of each BWG in wavelength#1 under JMRA algorithm (b)–(f), the user satisfaction rate of each BWG in wavelength 1 under the JMRA, DLB-DSA, WR-DLB, DSA, and FA algorithms, respectively.
J (x1, x2, ⋯, x n ) =
n
(∑
i=1
xi
)
2
n×
n
5. Conclusions
∑i =1 (xi2)
We proposed a fairness-aware joint multi-dimensional resource allocation algorithm based on a software-defined elastic optical access network architecture. This new algorithm included mainly the following four sub-algorithms for wavelength redirection, dynamic load balancing, maximum fit decreasing, and dynamic subcarrier allocation. The proposed algorithm can flexibly allocate wavelength and subcarrier resources according to network congestion, thereby improving the user satisfaction rate and ensuring a good user experience. The simulation results show that the network throughput and user satisfaction rate of the proposed algorithm were increased by about 30% during peak traffic periods compared with fixed allocation. Moreover, the bandwidth allocation fairness index of the proposed algorithm also greatly improved, compared with those of the other algorithms. The bandwidth distribution fairness index of the proposed algorithm is basically 1, which proves that the algorithm can guarantee fair use of the system bandwidth.
Jain’s fairness index can be used to measure the relative fairness of a range of values, with the range of values being 0 to 1. The higher the fairness index value, the closer the value of the series and the higher the fairness, whereas the smaller the value, the greater the difference between the values of the series and the lower the fairness. In Fig. 5(a) (b) and (c), we plotted bandwidth allocation fairness index curves for OLT, wavelength#1 and wavelength#4 under the five algorithms. Compared with the other algorithms, the bandwidth allocation fairness index of the proposed algorithm was greatly improved. At the same time, we note that in OLT, the WR-DLB algorithm has a good fairness index, but it is significantly worse than that of the DLBDSA and DSA algorithm in wavelength#4. This is because the WR-DLB algorithm has a relatively consistent user satisfaction rate in OLT, while the DLB-DSA and DSA algorithms have some areas with user satisfaction rate of 1 and some < 0.5, which can be seen in Fig. 4. However, for a single wavelength, due to adoption of the DSA algorithm, the subcarrier is redistributed from BWGs with a low congestion factor to BWGs with a high congestion factor. This also results in relatively consistent user satisfaction for each BWG. Fig. 6(a) shows that the number of subcarriers in each BWG will vary over time, and as the BWG load increases, the number of subcarriers increases accordingly, thus improving the user satisfaction rate. From Fig. 6(b), (c), and (e), it can be found that the user satisfaction rate of each BWG was relatively balanced. Whereas, in Fig. 6(d) and (f), the satisfaction rate of each BWG varied greatly at the same time. This is also consistent with the results in Fig. 5(b). For instance, Fig. 6(d) shows that the user satisfaction rate is not all 1 at time 10, 11, 14, 15, 19, 20 and 21 under the WR-DLB algorithm. Correspondingly, the fairness index of the DLB curve in Fig. 5(b) at these time is not 1. It can be seen that the DSA algorithm achieved fairness in the satisfaction rate among users, avoiding the situation that some users were fully satisfied and others had no bandwidth.
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgment This study are supported by National Natural Science Foundation of China (No. 61671076)
Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.yofte.2019.102136. 7
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References
[10] A. Dupas, P. Layec, D. Verchère, S. Bigo, Bandwidth variable transmitter for software defined networks, Optical Fiber Communication Conference, Optical Society of America, Los Angeles, California, 2017, p. W1I.6. [11] Y. Senoo, K. Asaka, J.-I. Kani, Dynamic Wavelength Allocation and Rapid Wavelength Tuning for Load Balancing in λ-tunable WDM/TDM-PON, Optical Fiber Communication Conference, Optical Society of America, Los Angeles, California, 2017, p. M3I.1. [12] Y. Senoo, et al., 512-ONU real-time dynamic load balancing with few wavelength reallocations in 40 Gbps λ-tunable WDM/TDM-PON [Invited], IEEE/OSA J. Opt. Commun. Networking 7 (12) (2015) B202–B211. [13] H. Saito, N. Minato, S. Kobayashi, H. Tamai, Adaptive bandwidth allocation algorithm for WDM/OFDM-PON-based elastic lambda aggregation network, Optical Fiber Communication Conference, Optical Society of America, Anaheim, California, 2016, p. Th3C.2. [14] Y. Senoo, et al., Fairness-aware dynamic sub-carrier allocation in distance-adaptive modulation OFDMA-PON for elastic lambda aggregation networks, IEEE/OSA J. Opt. Commun. Network. 9 (7) (2017) 616–624. [15] X. Gong, L. Guo, Q. Zhang, Joint resource allocation and software-based reconfiguration for energy-efficient OFDMA-PONs, J. Optical Commun. Network. 10 (8) (2018) C75–C85. [16] D. Chitimalla, et al., QoE enhancement schemes for video in converged OFDMA wireless networks and EPONS, IEEE/OSA J. Opt. Commun. Networking 10 (3) (2018) 229–239. [17] J. Neaime, A.R. Dhaini, Resource management in cloud and tactile-capable nextgeneration optical access networks, J. Optical Commun. Network. 10 (11) (2018) 902. [18] S.B. Hussain, W. Hu, H. Xin, A.M. Mikaeil, Low-latency dynamic wavelength and bandwidth allocation algorithm forNG-EPON, J. Opt. Commun. Netw. 9 (12) (Dec. 2017) 1108–1115. [19] R. Jain, D. Chiu, W. Hawe, A quantitative measure of fairness and discrimination for resource allocation in shared computer systems, Comp. Sci. (1998) cs.ni/9809099.
[1] Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2016–2021, Cisco White Paper. Available: https://www.cisco.com/c/en/us/ solutions/collateral/service-provider/visual-networking-index-vni/mobile-whitepaper-c11-520862.html. [2] S. Kimura, Elastic lambda aggregation network (EλAN)-Proposal for future optical access network, 2013 18th OptoElectronics and Communications Conference held jointly with 2013 International Conference on Photonics in Switching (OECC/PS), 2013, pp. 1–2. [3] K. Kanonakis, N. Cvijetic, I. Tomkos, T. Wang, Dynamic software-defined resource optimization in next-generation optical access enabled by OFDMA-based meta-MAC provisioning, J. Lightwave Technol. 31 (14) (2013) 2296–2306. [4] N. Cvijetic, SDN for Optical Access Networks, Advanced Photonics for Communications, Optical Society of America, San Diego, California, 2014, p. PM3C.4. [5] T. Sato, K. Tokuhashi, H. Takeshita, S. Okamoto, N. Yamanaka, A study on network control method in Elastic Lambda Aggregation Network (EλAN), 2013 IEEE 14th International Conference on High Performance Switching and Routing (HPSR), 2013, pp. 29–34. [6] L. Zhou et al., A novel optical access network: Software-defined FlexPON, in: 2014 OptoElectronics and Communication Conference and Australian Conference on Optical Fibre Technology, 2014, pp. 688–690. [7] K. Huang, W. Ji, X. Xue, X. Li, Design and evaluation of elastic optical access network based on WDM-PON and OFDM technology, J. Optical Commun. Network. 7 (10) (2015) 987. [8] M.R. Raza et al., Dynamic Resource Sharing for C-RANs with Joint Orchestration of Radio and Transport, in: ECOC 2016; 42nd European Conference on Optical Communication, 2016, pp. 1–3. [9] M. Ruffini, et al., Access and metro network convergence for flexible end-to-end network design [invited], J. Optical Commun. Network. 9 (6) (2017) 524.
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Contents lists available at ScienceDirect
Optical Fiber Technology journal homepage: www.elsevier.com/locate/yofte
Joint multi-dimensional resource allocation algorithm for a TWDM/OFDMPON-based software-defined elastic optical access network
T
⁎
Bingchang Hua, Zhiguo Zhang , Lei Wang State Key Lab of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing, China
A R T I C LE I N FO
A B S T R A C T
Keywords: TWDM/OFDM-PON Software defined elastic optical access network Joint multi-dimensional resource allocation
With the rapid growth of high-bandwidth services such as 5G, access networks only rely on physical layer technology to build a large-scale rigid pipeline, but cannot meet the need for the intelligent, flexible, and open networks of future business. Therefore, we herein propose a fairness-aware joint multi-dimensional resource allocation algorithm. It is based on software-defined elastic optical access network architecture and is intended to improve network throughput and the quality of service users experience. The simulation results show that the network throughput and user satisfaction rate of the proposed algorithm increases by about 30% during peak traffic periods compared with fixed allocation. The proposed algorithm can ensure that all users utilize the system bandwidth fairly.
1. Introduction Over the past decade, with the emergence of 5G and the expansion of various high-bandwidth services such as high-definition video and human interaction, the bandwidth demand of the access network has exploded. The Cisco Global Mobile Data Traffic Forecast Update (2016–2021) predicts that monthly global mobile data traffic will be 49 exabytes by 2021, leading to annual traffic that will exceed half a zettabyte [1]. Unfortunately, the access network only relies on physical layer technical progress to build large and rigid pipelines, which cannot meet the demand for the intelligent, flexible, and open networks needed in the future. Therefore, for access network operators, how to achieve efficient, intelligent, flexible, and open access networks is a huge challenge. For example, an elastic optical access network architecture was proposed [2] and an orthogonal frequency division multiple access passive optical network (OFDMA-PON) was applied to the PON system. OFDMPON utilizes subcarriers as logical links, which can be occupied by different service types and users, thus enabling more flexible multiservice and multi-user bandwidth allocation [3]. The elastic optical access network system can dynamically allocate wavelength and subcarrier resources, effectively improving spectrum efficiency and bandwidth allocation flexibility, and thereby improving transmission distance and performance [4,5]. Many researchers, through the introduction of software defined network (SDN) technology, reconfigurable optical distribution networks (ODN), and wavelength
⁎
selection switches (WSS) have tried to achieve flexibility of optical access networks and the effective integration of multiple services. These are intended to achieve the goal of saving network construction costs [6–9]. At the same time, operators need flexible resource allocation strategies to make full and efficient use of these network bandwidth resources and to avoid waste of resources caused by rigid pipeline operation and maintenance methods. For example, SDN technology was introduced to the access network, to realize efficient and flexible use of access network resources through the centralized control characteristics of SDN [10]. The NTT Access Network Service System Laboratory proposed a dynamic wavelength balancing (DLB) algorithm that distributes the traffic load between optical line terminal (OLT) ports fairly, and achieves rapid balance between OLT ports by transferring the largest matching optical network unit (ONU) in the OLT port, so as to minimize the required wavelength conversion. A fast wavelength tuning scheme was also put forward to achieve ONU without any dataframe-loss wavelength conversion, to ensure a good user experience [11,12]. Ref. [13] proposed an adaptive modulation format and a number of sub-carriers allocation algorithm using the distance from a PON section, quality of service (QoS), and bandwidth demands of each ONU for a WDM/OFDM hybrid aggregation network. The proposed algorithm can reduce the total bandwidth by selecting optical parameters adaptively to achieve steady high-spectrum efficiency in any traffic scenario. Ref. [14] proposed a fairness-aware dynamic subcarrier allocation (DSA) in a distance-adaptive modulation OFDMA-PON and
Corresponding author. E-mail address: [email protected] (Z. Zhang).
https://doi.org/10.1016/j.yofte.2019.102136 Received 13 September 2019; Received in revised form 4 December 2019; Accepted 31 December 2019 1068-5200/ © 2020 Elsevier Inc. All rights reserved.
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2. System architecture
experimentally demonstrated it for elastic lambda aggregation networks (EλANs). By applying the distance-adaptive modulation function to OFDMA-PONs, the system could change the transmission parameters (modulation level and symbol rate) for each user according to the endto-end optical loss of each user, which would yield additional bits. Meanwhile, by applying our proposed DSA algorithm, which allocates sub-carriers to users based on transmission parameters and traffic demands, the additional bits could be shared by all users in a fair manner regardless of a user’s end-to-end optical loss. There is also a great deal of research on bandwidth allocation of access networks [15–18]. These studies achieved energy saving or quality of experience (QoE) enhancement through allocation of resources such as wavelength and time slots. However, the elastic optical access network includes multiple dimensional resources (e.g. wavelength, subcarrier, modulation format, time slots). Nevertheless, the current research is mostly focused on allocation of resources in one or two dimensions, which lacks the joint multi-dimensional resource control of an elastic optical access network. In view of the inefficiency and rigidity of the current access network, we propose a fairness-aware joint multi-dimensional resource allocation algorithm (JMRA) based on software-defined elastic optical access network architecture. This includes mainly the following four sub-algorithms for wavelength redirection (WR), dynamic load balancing (DLB), maximum fit decreasing (MFD), and dynamic subcarrier allocation (DSA). Wavelength redirection is mainly to balance the wavelength resources between areas. Dynamic load balancing and maximum fit decreasing are to balance the load between wavelengths in the same area. Dynamic subcarrier allocation is to equalize the bandwidth resources of the BWG under each wavelength. Through the balance achieved with the above algorithm, the throughput and user satisfaction rate are improved. The remainder of this paper is organized as follows. Section 2 introduces the software elastic optical access network architecture. Section 3 describes in detail the fairness-aware joint multi-dimensional resource algorithm. Simulation results are presented and analyzed in Section 4 and conclusions are given in Section 5.
Fig. 1 depicts the structure of the TWDM/OFDM-PON-based software defined elastic optical access network, which consists of SDN controller, OLT, remote node, wavelength tunable ONU, and dense wavelength division multiplexing (DWDM) fiber ring. The SDN controller centrally controls the reconfigurable of optical switch array and remote node (RN), the wavelength selection of ONU, resource allocation and survival guarantee of the whole network. The OLT and the remote node (RN) are connected together by a bidirectional transmitted DWDM fiber optic ring. The RN and the ONU are connected by an optical splitter. With the fiber ring, the RNs can be deployed in different locations in the city, and ultimately deliver service to the end users through splitters. The OLT includes n service line cards (LC1, ...,LCn ), an optical switch array, and two 50 GHz channel-spaced AWGs, each of the line cards contains different fixed-wavelength transceivers to provide different wavelength options. The downstream service wavelength is multiplexed by the AWG and broadcast to all remote nodes in the counterclockwise direction of the DWDM ring. After a fault such as a DWDM ring break, the SDN controller controls the optical switch array to switch the designated service line card to the other side of the DWDM ring to ensure normal service of the remote node below the fault point node. The RN is composed of four WSS [8]. Through the WSS, multiple working wavelengths running on the DWDM ring can be dynamically allocated among the RNs under control of the SDN controller. Due to the WSS wavelength selection feature, the service wavelength on the DWDM ring cannot be shared by all RNs. When one RN delivers a wavelength, other RNs cannot use it. The service wavelength delivered by the RN is broadcast to all wavelength tunable ONUs via an optical splitter, and the wavelength bandwidth resources are shared by all ONUs in the area. In the system, all ONUs in the area share the service wavelength delivered by the RN interface. The group of ONUs using the same transmission parameters (modulation format, symbol rate) at the same wavelength is defined as a bandwidth group (BWG) [12]. The ONUs in the BWG share a series of subcarriers within the wavelength, and TDMA can also be implemented in the BWG. On the one hand, high-order
Fig. 1. TWDM/OFDM-PON-based software-defined elastic optical-access network system architecture. 2
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Step VI: Use the MFD algorithm in area j to assign wavelengths to unallocated ONUS; then turn to Step IV. Step VII: Use DLB algorithm in area j to perform load balancing between wavelengths; then turn to Step IV. Step VIII: If traverse all wavelengths, turn to Step XI. Otherwise to Step IX. Step IX: If overloaded BWGs occur in wavelength w, turn to Step X. Otherwise to Step VIII. Step X: Use the DSA algorithm in wavelength w to perform load balancing between BWGs; then turn to Step VIII. Step XI: End.
modulation format signals have higher spectrum utilization under certain conditions of system bandwidth. On the other hand, high-order modulation format signals shorten the distance between constellation points, and the corresponding signal decision regions are also reduced. Thus, the anti-jamming capability is impaired and the bit error rate is large. Thus, the adaptive modulation of distance is considered under the condition of a constant baud rate, the higher-order transmission parameters are mapped to the ONUs closer to the OLT to provide high system capacity, and the low-order transmission parameters are mapped to the ONU farther away from the OLT to improve the antijamming performance of the signals in transmission. 3. Multi-dimensional resource joint allocation algorithm
In order to facilitate understanding of the proposed JMRA algorithm, the WR, MFD, DLB, and DSA algorithms used therein are described in detail below.
Under the TWDM/OFDM-PON-based software-defined elastic optical-access-network system, we proposed a fairness-aware wavelength, sub-carrier and modulation format joint multi-dimensional resource allocation algorithm. To describe the proposed scheme, we define the following sets, parameters and variables below. Sets:
3.1. Wavelength redirection (WR) algorithm Because the ONU request bandwidth is different in each area, some areas may have a light load, while others may have network congestion. Based on this situation, we propose an inter-area wavelength redirection algorithm to redirect some wavelengths from a light-load area to a area that is congested, thereby increasing the bandwidth satisfaction rate of the network congested area. The WR algorithm achieves the purpose of balancing the load between areas by redirecting the wavelengths. This improves throughput of the network and the bandwidth satisfaction rate of the ONU through the WR algorithm. The algorithm is summarized in Table 1.
I : set of ONUs. J : set of areas. W : set of wavelengths. K : set of BWGs. Uj : set of unallocated ONU in area j. Parameters:
3.2. Dynamic load balancing (DLB) and maximum fit decreasing (MFD) algorithm
BW _ONUi is the bandwidth request of ONU i. BW _Areaj is the total request bandwidth of area j. BW _Ww is the total request bandwidth of wavelength w. BW _BWGk is the total request bandwidth of BWG k. C is the maximum bandwidth of each wavelength. Bk is the transmission rate of BWG k.
At the same time, the load of some wavelengths may be light, while other wavelengths may have network congestion. Therefore, we propose the use of DLB and MFD algorithms to balance the wavelengths in each area to reduce network congestion and improve the bandwidth satisfaction rate of the ONU. If there are unallocated ONUs in an area, the unallocated ONU is allocated to each wavelength in the area using the MFD algorithm. Otherwise, the DLB algorithm is adopted to redistribute the ONU of the heavy load wavelength to the light load wavelength. It is worth noting that the ONU transmission parameters (modulation format and symbol rate) remain the same, whether the ONU is reassigned to any wavelength or not. In step 3, the role of the target ONU with the requested bandwidth closest to 2 is selected to reduce the amount of wavelength switching of the ONU, thus effectively reducing delay caused by wavelength switching. This algorithm is summarized in Table 2.
Variables:
Numj is the number of wavelengths in area j. Xk is the number of subcarrier in BWG k. Sj is the congestion factor of area j, defined as Sj = BW _Areaj (Numj × C ) . Zk is the congestion factor of BWG k, defined as Zk = BW _BWGk (Bk × Xk ) . M (h) is the maximum congestion factor of the area over a period of time. N (h) is the maximum congestion factor of the BWGs in wavelength over a period of time.
3.3. Dynamic subcarrier allocation (DSA) algorithm
With the mapping between areas, BWGs, wavelengths and ONUs are given, the goal is to minimize the OLT congestion factor and for all ONUs to use bandwidth resources fairly. The process of the proposed algorithm is fully described below; a flowchart is illustrated in Fig. 2.
As described above, in order to ensure the transmission performance of the system, the transmission parameters of the high-order modulation format can only be mapped to the ONU that is closest to the OLT, and the ONUs that are far away from the OLT only use the low-order modulation format. However, in the case of using the same number of subcarriers, the ONUs that are farther away can carry less bandwidth, resulting in a decrease in the user bandwidth satisfaction rate. Therefore, in the case of network congestion, the DSA algorithm is used to realize fair utilization of bandwidth resources between BWGs, which can maximize the user experience. This algorithm is summarized in Table 3.
Step I: The request bandwidth from all ONUs in the OLT are given, and used to calculate load and congestion factors for all areas, wavelengths, and BWGs. Step II: If Sj > 1, indicating area j overload, turn to Step III. Otherwise turn to Step IV. Step III: Rebalance overloaded areas using the WR algorithm. When a certain wavelength from area j is redirected to another area, the ONU originally allocated to the wavelength is placed in Uj . Step IV: If traverse all areas, turn to Step VIII. Otherwise to Step V. Step V: If unallocated ONUs occur in area j, turn to Step VI. Otherwise to Step VII.
4. Simulation results The simulation system consisted of an SDN controller and a PON system, as shown in Fig. 1. The number of areas in the OLT was 4, the 3
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Fig. 2. Flowchart of joint multi-dimensional resource allocation algorithm.
according to the change of bandwidth requested by the ONU in each cycle, the number of wavelengths in each area and the number of ONUs per wavelength were changed accordingly, and the number of subcarriers between different BWGs for each wavelength varied, but the sum was still fixed at 48. Traffic requests for different Internet users in certain areas, such as the city’s central business district and residential areas, are repeatedly relocated. During the day, the ONU load is heavy in business areas while it is light in residential areas. On the contrary, at night, the ONU
number of wavelengths was 4, the number of ONUs was 512, the number of subcarriers for each wavelength was 48 and the maximum transmission rate of each wavelength was 50 Gbps. The symbol rate defined in the transmission parameters was 156 M symbol/s. Each PON port contained 4 BWGs, BWG#1 (QPSK, B), BWG#2 (8QAM, B), BWG#3 (16QAM, B), and BWG#4 (64QAM, B). Each area had 4 wavelengths and 128 ONUs during initialization. In advance, 128 ONUs were evenly distributed to 4 BWGs according to the distance between the ONU and the OLT. Each BWG had 8 ONUs and 12 subcarriers. Then, 4
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Table 1 Wavelength Redirection (WR) Algorithm.
Table 3 Dynamic subcarrier allocation (DSA) algorithm.
Algorithm 1: Wavelength Redirection (WR) Algorithm
Algorithm 3: Dynamic subcarrier allocation (DSA) algorithm
1: Initialize the mapping between ONUs, BWGs, Wavelengths, Areas 2: Calculate congestion factor Sj of areas 3: if: all of the areas under the OLT are satisfied with Sj < 1; go to step 7 else: go to step 4 4: Find the maximum Sj, put j into j_max; find the minimum Sj, put j into j_min least loaded wavelength in area j_max is redirected to area j_min Numj_max = Numj_max + 1, M(h) = Sj_max, h = h + 1 Numj_min = Numj_min − 1 5: if: Numj ≥ 1; and: M(h) ≠ M(h + 1); then: go to step 2 else: go to step 6 6: Numj_max = Numj_max-1, Numj_min = Numj_min + 1 7: The ONU originally allocated to the redirected wavelength in area j placed in Uj 8: End the wavelength redirection of this timeslot.
1: Traverse four BWGs on each wavelength, calculate Zk 2: if: all of the BWGs under the wavelength are satisfied with Zk < 1; go to step 7 else: go to step 3 3: Find the maximum Zk, put k into k_max; find the minimum Zk, put k into k_min Xj_max = Xj_max + 1,N(h) = Zk_max, h = h + 1 Xj_min = Xj_min − 1 4: Calculate BW_WW 5: if: BW_WW ≤ C; and: Xk ≥ 0; and: N(h) ≠ N(h + 1); then: go to step 2 else: go to step 6 6: Xj_max = Xj_max-1, Xj_min = Xj_min + 1 7: if: Traverse all wavelengths; then: go to step 1 else: repeat step 8 8: End the dynamic subcarrier allocation of this timeslot.
Table 2 Dynamic load balancing (DLB) and maximum fit decreasing (MFD) algorithm.
user’s request bandwidth.
Sa = BW _alloc BW _req
Algorithm 2: Dynamic load balancing (DLB) and maximum fit decreasing (MFD) algorithm
The performance comparison of total OLT throughput and user satisfaction rate between the proposed joint multi-dimensional resource allocation algorithm and the other four algorithms was verified. Definition algorithm 1 was the JMRA proposed in this paper; algorithm 2 was a DLB-DSA algorithm that only performed dynamic load balancing within areas and dynamic distribution within wavelengths. Algorithm 3 was a WR-DLB algorithm that only performed inter-area wavelength redirection and load balancing within areas. Algorithm 4 was a DSA algorithm that only performed dynamic distribution within wavelengths [14], and algorithm 5 was a fixed allocation (FA) without any algorithm optimization. Fig. 3(b) and (c) depict simulation results of the OLT total throughput and user satisfaction rate, respectively, for the five algorithms. It can be seen that the proposed JMRA algorithm performs best in terms of throughput and user satisfaction rate compared with the other four algorithms. During peak traffic periods, an increase of up to 30% bandwidth satisfaction rate and throughput (compared to DLBDSA, DSA and FA algorithms) occurred, and was close to satisfying the bandwidth requests of all users. The throughput and user satisfaction rate of the WR-DLB algorithm were also significantly better than with the other three algorithms. This is because the WR algorithm redirects the wavelength of light-load areas to heavy-load areas, effectively reducing the network congestion rate of the heavy-load areas and improving the user bandwidth satisfaction rate. Compared with the JMRA algorithm, the performance of the WR-DLB algorithm was slightly inadequate. It can be concluded that the DSA algorithm can greatly improve the user satisfaction rate and throughput by balancing the number of subcarriers between BWGs. At the same time, by comparing the performance of the DLB-DSA, DSA, and FA algorithms, it was discovered that performing the DLB and DSA algorithms does not improve network performance without inter-area wavelength redirection. It is worth noting that the DSA algorithm achieved fairness in the satisfaction rate among users, avoiding the situation in which some users are satisfied and some have no bandwidth. Fig. 4(a) describes the curve of the total request bandwidth in the areas when the JMRA algorithm is used. Fig. 4(b) shows the curve of the changing of number of area wavelengths with time when the JMRA algorithm was adopted. As can be seen, the number of wavelengths in the area varies with the requested bandwidth. As the total requested bandwidth of a area user increases, the number of wavelengths may also increase to provide more network bandwidth resources to maximize the user's bandwidth request. In Fig. 4(c)–(f), we plotted user satisfaction rate curves for areas under the five algorithms. In each area, more substantial user satisfaction rate improvement was achieved using the JMRA algorithm than with the other algorithms. However, at time
1: j = 1 2: if: Uj is empty; go to step 7 else: go to step 3 Max fit deceasing (MFD) subroutine 3: Traverse all wavelengths on area j, calculate: BW_WW=∑BW_BWGk (BWGk belong to wavelength w) 4: Sort the ONUs in Uj in decreasing order of their loads; ONUmax is the heavy-loaded ONU in Uj 5: ONUmax allocated to the light-loaded wavelength in area j, remove the ONUmax from the Uj 6: if: Uj is empty; j = j + 1 if: j < J; go to step 2 else: go to step 12 else: go to step 4 Dynamic load balancing (DLB) subroutine 7: Traverse all wavelengths on area j, calculate: BW_WW = ∑BW_BWGk (BWGk belong to wavelength w) 8: Calculate the optimal load balancing optimization value Dload according to the formula: Dload = BW_ONUi_max-BW_ONUi_min 9: Find the target ONU with BW_ONUi_target closest to Dload/2 from the heavy-loaded wavelength 10: if: BW_ONUi_target < Dload Move target ONU to the light-loaded wavelength; then: go to step 11 else: go to step 12 11: if: Dload < BW_ONUi_min; j = j + 1 if: j < J; go to step 2 else: go to step 12 else: go to step 7 12: End the Dynamic load balancing (DLB) and max fit deceasing (MFD) of this timeslot
load is light in business areas but it is heavy in residential areas. There is a phenomenon of complementary traffic requests between the commercial areas and residential areas. This tidal effect factor leads to the need for real-time adjustment of bandwidth. Hence, the simulation sets the user traffic model of the business area and residential area, as shown in Fig. 3(a). There were 256 ONU users in the business area and 256 ONU users in a residential area. The users in area#1 and area#2 were all in the business area, and the users in area#3 and area#4 were all in the residential area. All users generate a bandwidth request before each time slot begins. And users request bandwidth are normally distributed, the expectation at each timeslot is shown in Fig. 3(a), with a variance of 0.3. We divided 24 h a day into an average of 24 time slots. We defined the user satisfaction rate as the ratio of the bandwidth provided by the system to user traffic requests, as shown in the following formula, where Sa indicates the user satisfaction rate, BW_alloc indicates the bandwidth allocated to the user, and BW_req indicates the 5
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Fig. 3. (a) Average traffic of users in two areas, (b) Total throughput for the OLT, (c) Total satisfaction rate of users.
Fig. 4. (a) Request bandwidth for the area (b) Number of wavelengths of the area under JRMA algorithm (c) (d) (e) (f) Satisfaction rate of users in area#1–4, respectively.
Fig. 5. (a) (b) (c) Fairness index for the OLT, wavelength#1 and wavelength#4, respectively.
resources of the whole area were greatly reduced, thus causing network congestion. Jain's fairness index [19] is used to measure the fairness of resource allocation results, as shown in the following formula, where x n indicates the user satisfaction rate of user n.
21 in area#2, when the DLB-DSA, DSA, and RA algorithms were all 1, the user satisfaction rate of the JMRA and WR-DLB algorithms was not 1 (i.e., the user bandwidth request could be fully satisfied). This is because the JMRA and WR-DLB algorithms performed wavelength redirection, and the number of wavelengths in area#2 was no longer 4 (see Fig. 4b). Compared with the other three algorithms, the network
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Fig. 6. (a) Subcarrier number of each BWG in wavelength#1 under JMRA algorithm (b)–(f), the user satisfaction rate of each BWG in wavelength 1 under the JMRA, DLB-DSA, WR-DLB, DSA, and FA algorithms, respectively.
J (x1, x2, ⋯, x n ) =
n
(∑
i=1
xi
)
2
n×
n
5. Conclusions
∑i =1 (xi2)
We proposed a fairness-aware joint multi-dimensional resource allocation algorithm based on a software-defined elastic optical access network architecture. This new algorithm included mainly the following four sub-algorithms for wavelength redirection, dynamic load balancing, maximum fit decreasing, and dynamic subcarrier allocation. The proposed algorithm can flexibly allocate wavelength and subcarrier resources according to network congestion, thereby improving the user satisfaction rate and ensuring a good user experience. The simulation results show that the network throughput and user satisfaction rate of the proposed algorithm were increased by about 30% during peak traffic periods compared with fixed allocation. Moreover, the bandwidth allocation fairness index of the proposed algorithm also greatly improved, compared with those of the other algorithms. The bandwidth distribution fairness index of the proposed algorithm is basically 1, which proves that the algorithm can guarantee fair use of the system bandwidth.
Jain’s fairness index can be used to measure the relative fairness of a range of values, with the range of values being 0 to 1. The higher the fairness index value, the closer the value of the series and the higher the fairness, whereas the smaller the value, the greater the difference between the values of the series and the lower the fairness. In Fig. 5(a) (b) and (c), we plotted bandwidth allocation fairness index curves for OLT, wavelength#1 and wavelength#4 under the five algorithms. Compared with the other algorithms, the bandwidth allocation fairness index of the proposed algorithm was greatly improved. At the same time, we note that in OLT, the WR-DLB algorithm has a good fairness index, but it is significantly worse than that of the DLBDSA and DSA algorithm in wavelength#4. This is because the WR-DLB algorithm has a relatively consistent user satisfaction rate in OLT, while the DLB-DSA and DSA algorithms have some areas with user satisfaction rate of 1 and some < 0.5, which can be seen in Fig. 4. However, for a single wavelength, due to adoption of the DSA algorithm, the subcarrier is redistributed from BWGs with a low congestion factor to BWGs with a high congestion factor. This also results in relatively consistent user satisfaction for each BWG. Fig. 6(a) shows that the number of subcarriers in each BWG will vary over time, and as the BWG load increases, the number of subcarriers increases accordingly, thus improving the user satisfaction rate. From Fig. 6(b), (c), and (e), it can be found that the user satisfaction rate of each BWG was relatively balanced. Whereas, in Fig. 6(d) and (f), the satisfaction rate of each BWG varied greatly at the same time. This is also consistent with the results in Fig. 5(b). For instance, Fig. 6(d) shows that the user satisfaction rate is not all 1 at time 10, 11, 14, 15, 19, 20 and 21 under the WR-DLB algorithm. Correspondingly, the fairness index of the DLB curve in Fig. 5(b) at these time is not 1. It can be seen that the DSA algorithm achieved fairness in the satisfaction rate among users, avoiding the situation that some users were fully satisfied and others had no bandwidth.
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgment This study are supported by National Natural Science Foundation of China (No. 61671076)
Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.yofte.2019.102136. 7
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References
[10] A. Dupas, P. Layec, D. Verchère, S. Bigo, Bandwidth variable transmitter for software defined networks, Optical Fiber Communication Conference, Optical Society of America, Los Angeles, California, 2017, p. W1I.6. [11] Y. Senoo, K. Asaka, J.-I. Kani, Dynamic Wavelength Allocation and Rapid Wavelength Tuning for Load Balancing in λ-tunable WDM/TDM-PON, Optical Fiber Communication Conference, Optical Society of America, Los Angeles, California, 2017, p. M3I.1. [12] Y. Senoo, et al., 512-ONU real-time dynamic load balancing with few wavelength reallocations in 40 Gbps λ-tunable WDM/TDM-PON [Invited], IEEE/OSA J. Opt. Commun. Networking 7 (12) (2015) B202–B211. [13] H. Saito, N. Minato, S. Kobayashi, H. Tamai, Adaptive bandwidth allocation algorithm for WDM/OFDM-PON-based elastic lambda aggregation network, Optical Fiber Communication Conference, Optical Society of America, Anaheim, California, 2016, p. Th3C.2. [14] Y. Senoo, et al., Fairness-aware dynamic sub-carrier allocation in distance-adaptive modulation OFDMA-PON for elastic lambda aggregation networks, IEEE/OSA J. Opt. Commun. Network. 9 (7) (2017) 616–624. [15] X. Gong, L. Guo, Q. Zhang, Joint resource allocation and software-based reconfiguration for energy-efficient OFDMA-PONs, J. Optical Commun. Network. 10 (8) (2018) C75–C85. [16] D. Chitimalla, et al., QoE enhancement schemes for video in converged OFDMA wireless networks and EPONS, IEEE/OSA J. Opt. Commun. Networking 10 (3) (2018) 229–239. [17] J. Neaime, A.R. Dhaini, Resource management in cloud and tactile-capable nextgeneration optical access networks, J. Optical Commun. Network. 10 (11) (2018) 902. [18] S.B. Hussain, W. Hu, H. Xin, A.M. Mikaeil, Low-latency dynamic wavelength and bandwidth allocation algorithm forNG-EPON, J. Opt. Commun. Netw. 9 (12) (Dec. 2017) 1108–1115. [19] R. Jain, D. Chiu, W. Hawe, A quantitative measure of fairness and discrimination for resource allocation in shared computer systems, Comp. Sci. (1998) cs.ni/9809099.
[1] Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2016–2021, Cisco White Paper. Available: https://www.cisco.com/c/en/us/ solutions/collateral/service-provider/visual-networking-index-vni/mobile-whitepaper-c11-520862.html. [2] S. Kimura, Elastic lambda aggregation network (EλAN)-Proposal for future optical access network, 2013 18th OptoElectronics and Communications Conference held jointly with 2013 International Conference on Photonics in Switching (OECC/PS), 2013, pp. 1–2. [3] K. Kanonakis, N. Cvijetic, I. Tomkos, T. Wang, Dynamic software-defined resource optimization in next-generation optical access enabled by OFDMA-based meta-MAC provisioning, J. Lightwave Technol. 31 (14) (2013) 2296–2306. [4] N. Cvijetic, SDN for Optical Access Networks, Advanced Photonics for Communications, Optical Society of America, San Diego, California, 2014, p. PM3C.4. [5] T. Sato, K. Tokuhashi, H. Takeshita, S. Okamoto, N. Yamanaka, A study on network control method in Elastic Lambda Aggregation Network (EλAN), 2013 IEEE 14th International Conference on High Performance Switching and Routing (HPSR), 2013, pp. 29–34. [6] L. Zhou et al., A novel optical access network: Software-defined FlexPON, in: 2014 OptoElectronics and Communication Conference and Australian Conference on Optical Fibre Technology, 2014, pp. 688–690. [7] K. Huang, W. Ji, X. Xue, X. Li, Design and evaluation of elastic optical access network based on WDM-PON and OFDM technology, J. Optical Commun. Network. 7 (10) (2015) 987. [8] M.R. Raza et al., Dynamic Resource Sharing for C-RANs with Joint Orchestration of Radio and Transport, in: ECOC 2016; 42nd European Conference on Optical Communication, 2016, pp. 1–3. [9] M. Ruffini, et al., Access and metro network convergence for flexible end-to-end network design [invited], J. Optical Commun. Network. 9 (6) (2017) 524.
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