Reconfigurable Long-Reach UltraFlow Access ... - OSA Publishing

JOURNAL OF LIGHTWAVE TECHNOLOGY, VOL. 32, NO. 13, JULY 1, 2014

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Reconfigurable Long-Reach UltraFlow Access Network: A Flexible, Cost-Effective, and Energy-Efficient Solution Thomas Shun Rong Shen, Student Member, IEEE, Shuang Yin, Student Member, IEEE, Ahmad R. Dhaini, Member, IEEE, and Leonid G. Kazovsky, Fellow, IEEE

Abstract—In this paper, we propose and experimentally demonstrate a reconfigurable long-reach (R-LR) UltraFlow access network to provide flexible dual-mode (IP and Flow) service with lower capital expenditure (CapEx) and higher energy efficiency. UltraFlow is a research project involves the collaboration of Stanford, MIT, and UT-Dallas. The design of the R-LR UltraFlow access network enables seamless integration of the Flow service with IP passive optical networks deployed with different technologies. To fulfill the high-wavelength demand incurred by the extended service reach, we propose the use of multiple feeder fibers to form subnets within the UltraFlow access network. Two layers of custom switching devices are installed at the central office (CO) and remote node to provide flexibility in resource allocation and user grouping. With a centralized software-defined network (SDN) controller at the CO to control the dual-mode service, numerical analysis indicates that the reconfiguration architecture is able to reduce the CapEx during initial deployment by about 30%. A maximum of around 50% power savings is also achieved during low traffic period. The feasibility of the new architecture and the operation of the SDN controller are both successfully demonstrated on our experimental testbed. Index Terms—Long-reach passive optical networks (PONs), optical access network, optical flow switching.

I. INTRODUCTION N recent years, constantly increasing large network transactions such as high definition/3-D video streaming, cloud computing, and communications between large scale data centers, have pushed existing networks to their capacity limit. Optical coherent communication systems using advanced modulation formats such as quadrature amplitude modulation and orthogonal frequency-division multiplexing [1], [2] have been proposed to boost the throughput of individual channel. However, increasing the physical data rate alone may no longer be sufficient. The system capacity of electronic packet switching based network is limited by the speed of electronic switching

I

Manuscript received December 22, 2013; revised March 20, 2014; accepted March 7, 2014. Date of publication May 18, 2014; date of current version June 20, 2014. This work was supported by the National Science Foundation under Award 1111374. The authors are with the Photonics Networking and Research Laboratory, Department of Electrical Engineering, Stanford University, Stanford, CA 94305 USA (e-mail: [email protected]; [email protected]; [email protected]; [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/JLT.2014.2324551

and routing. The effective throughput of a large network transaction is also throttled by the insertion of IP headers. Optical Flow Switching (OFS) [3], was proposed to break these limitations by reserving dedicated optical lightpaths between the end-users across the entire network path. In OFS, no electronic routing and buffering occurs at intermediate nodes during the Flow transmission. Hence, the channel throughput is not throttled by slow electronics. Moreover, the much larger size of a Flow frame compared to a regular IP packet reduces the packet overhead and leads to increased effective channel throughput [4]. The UltraFlow project is a comprehensive research program on OFS-enabled networks that involves the collaboration of three universities: Stanford, MIT and UT-Dallas. At Stanford, we are responsible for designing and experimentally demonstrating the UltraFlow Access network architecture and testbed, as well as appropriate control and management software to support the testbed. In our previous works [5], we proposed and experimentally demonstrated the Stanford UltraFlow access network, a novel optical access network that offers dual-mode service, IP and Flow, to end-users. The new architecture overlays optical Flow service over the existing optical IP access networks (e.g. EPON and GPON) using novel network nodes, including optical Flow network unit (OFNU), optical Flow line terminal (OFLT) and gateway. The OFNU is a Flow service adapter located at the user premises. To provide simultaneous IP service, the OFNU forwards all IP packets to the IP PON through a connected ONU. The IP connection is also used for communication of Flow control messages among the Flow nodes. The OFNU transmits/receives Flow traffic using a tunable transceiver/filter and the optical connection to the OFLT in the central office (CO) is established via a feeder fiber shared with the IP PON. The OFLT serves as an interface between the Flow access and metro/core networks. With a bank of tunable transceivers, the OFLT is able to perform dynamic wavelength allocation in the access, and provides network-monitoring function such as power and wavelength characterization, and channel impairment detection. The gateways placed in front of the ONU/OFNU and OLT/OFLT are constructed with edged filters to separate and/or combine the Flow and IP traffics in legacy PON that occupy the C-band and 1310/1490 nm, respectively [5]. In collaboration with WDM IP PON, the gateway can be modified to isolate the waveband of the two services, or can be removed if the ONU is equipped with tunable filter.

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In this paper, we propose and experimentally demonstrate a new reconfigurable long-reach (R-LR) UltraFlow architecture that offers a flexible, robust, cost effective and energy efficient dual-mode service, legacy IP and Flow, with a service distance of 100 km or more. Compared to our previous UltraFlow access architecture with a 20 to 25 km service reach, the long-reach configuration is able to provide more users (e.g., 1024) with the Flow service in a wider geographical area. It consolidates the UltraFlow resources such as the OFLT into the CO so as to simplify the network management and reduce the capital expenditure (CapEx) [6]–[8]. The new highly centralized architecture also facilitates future system upgrade and expansion. On the other hand, the increased number of Flow users can easily exhaust all available wavelengths specified in a WDM grid (e.g. the ITU-T DWDM grid [9]). In collaboration with WDM IP PON, the wavelength demands from the IP channels that also operate in the C-band [7],.[8] may worsen the competition for wavelength. To avoid excessive service delay due to the lack of channels, we propose the use of two or more feeder fibers to create isolated subUltraFlow access networks so that wavelength can be reused to mitigate wavelength scarcity. In addition, two layers of reconfigurable switching modules are inserted at the CO and remote node (RN) to ensure network reliability and provide freedom in sub-access network selection and user grouping. The flexibility in the network configuration enables pay-as-you-grow for around 30% savings in initial OFLT installation or proportionally CapEx compared to a WDM PON using arrayed waveguide grating (AWG) [10] during the early stage of UltraFlow deployment. We also investigate the influence of the system capacity (i.e., the maximum number of users supported in the network) and the number of installed transceivers on each OFLT over the CapEx saving. To efficiently manage the R-LR UltraFlow access network, a software-defined control plane architecture is proposed and experimentally demonstrated on our testbed. Instead of an embedded controller, Flow transmissions are managed by a Flow service application in the software-defined network (SDN) controller which is decoupled from the underlying hardware. This arrangement helps reduce the complexity and the cost of OFLT. Moreover, an integer linear programming (ILP) based network resource assignment algorithm is proposed to reconfigure the source-destination connection according to real-time traffic loads that constantly varies in a day [11]–[13] to obtain optimal network energy efficiency. Numerical analysis suggests a maximum of more than 50% savings in power consumption during low network loads. The impacts of system capacity and OFLT capacity on the energy efficiency are also studied. The rest of the paper is organized as follows. The design of R-LR UltraFlow access network, including the data plane and control plane, are presented in Section II. Section III studies its scalability in terms of both wavelength availability and channel power budget. The testbed and experimental results of the data plane and control plane are shown in Section IV. Section V analyzes the cost-effectiveness of the proposed architecture. Energy efficiency of the R-LR UltraFlow access is studied in Section VI. Section VII concludes the paper.

JOURNAL OF LIGHTWAVE TECHNOLOGY, VOL. 32, NO. 13, JULY 1, 2014

II. RECONFIGURABLE LONG-REACH SOFTWARE-DEFINED ULTRAFLOW ACCESS NETWORK The architecture of the proposed R-LR UltraFlow access network is shown in Fig. 1. As the introduction of the SDN controller decouples the physical data plane from that of the control plane, the design of the two network layers are discussed separately in the following subsections. A. Data Plane The long-reach architecture of the R-LR UltraFlow access network centralizes the Flow service provisioning at a single CO by extending the service reach to 100 km with the aid of erbium doped fiber amplifiers (EDFAs). This helps simplify the deployment and management of UltraFlow access network [14]. On the other hand, with extended Flow service distance, many more users (e.g. 1024 or more) are expected to be supported by the UltraFlow resources at one node and each of them will require a dedicated wavelength for Flow transmission. However, a transceiver can only support a finite number of wavelengths depending on the WDM grid that it complies with (e.g., 80 channels in ITU-T DWDM C-band grid with 50 GHz channel spacing). To overcome the scarcity of wavelengths, the optical channels originated from different OFLTs are first grouped into several source groups using an AWG. The number of wavelengths Nwavelength in each source group would exploit the whole WDM grid, and the same wavelength(s) can be reused among different source groups (i.e., groups of OFLTs). To isolate these source groups in subsequent Flow transmissions, more than one feeder fiber may be used to form sub-UltraFlow access networks. The number of required feeder fibers (Nfeeder ) depends on the number of subscribers, the average Flow service demand and traffic latency requirements. To improve the network reliability and the resource utilization among the isolated subnets, two custom switching modules (SW-1 and SW-2) are introduced at the CO and RN, respectively. The switching module SW-1 serves as a protection structure that connects each of the Nsource group source groups to one of the feeder fibers for normal operation (i.e., no fault occurs) and/or the feeder fibers that are designated as backup feeder fiber (e.g., those attached to a switch in SW-1). The splitting ratio in SW-1 and the number of backup feeder fibers may depend on the component failure rate, service agreement with users and specific requirement from the operators. The switching module SW-2 divides the whole user pool into Nuser group sub-groups. Depending on the number of active users in each sub-group and the maximum wavelength supported by each source group, SW-2 may connect multiple user groups to the same feeder fiber to share the same source group. The dynamic resource assignment realized by the two switching modules enable the UltraFlow access to satisfy Flow traffic demand with minimum active equipment. Idle devices can be switched off/sleep to save energy. The joint work of SW-1 and SW-2 enables dynamic connection switching between duplicated feeder fibers. This implements the ITU-T type A protection scheme [15] in the UltraFlow access network. In case of any the optical components (e.g., EDFA or feeder fiber) in the original connection malfunction, SW-1 is able to

SHEN et al.: RECONFIGURABLE LONG-REACH ULTRAFLOW ACCESS NETWORK: A FLEXIBLE, COST-EFFECTIVE, AND ENERGY-EFFICIENT

Fig. 1.

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Architecture of the R-LR UltraFlow access network.

reroute the Flow channels to a backup feeder fiber, while SW-2 will also switch the involved user groups to the new feeder fiber accordingly to reestablish the failed connections. The wider service area also implies that the R-LR UltraFlow access network may need to collaborate with multiple IP PONs deployed with different technologies (e.g., EPON/GPON or WDM/long-reach PON) to seamlessly integrate the dualmode services and utilize existing PON structure for lower Flow service cost. Three different scenarios of collaboration are considered in Fig. 1. 1) Scenario 1 (From Node A to Node C): The IP service is also deployed with long-reach architecture and operates in the C-band as well. Similar to OFNU, the ONU is equipped with tunable transceivers so that it can send/receive optical signals on any wavelength. The IP channels originated from the OLTs in the same CO are combined with the Flow channels at the downstream output of SW-1 using a 1 × 2 splitter. At the receiving end, the signal is split into the ONU and OFNU respectively, and the correct channel is selected by filtering out the other wavelengths. The full tunability in the IP PON offers higher flexibility in wavelength sharing between the IP service and Flow service. However, using the splitters to combine and separate the optical signals at the transmitting and receiving ends will impose tighter power budget on the Flow channel which in turn may result in less supported users. Moreover, fully tunable receiver also increases the cost at user premises. 2) Scenario 2 (From Node A to Node D): If the IP service and Flow service operate in different portions of the C-band, the splitters at point A and point C can be replaced by the gateways discussed above. By doing so, the attenuation caused by combining and separation of IP channels and Flow channels can be reduced. Fixed receiver may also be used in OFNU to

reduce the cost at the expense of less flexibility in wavelength allocation. 3) Scenario 3 (From Node B to Node E): In the case that some of the Flow users are subscribed to legacy IP PON service, the central office of the legacy IP PON may be used as a remote node for the R-LR UltraFlow access network. The original feeder fiber can also be used as the distribution fiber to the end users. Consequently, the CapEx for this part of the UltraFlow Flow access network can be significantly reduced. Similar to Scenario 2, the IP channels operating at 1310/1490 nm are combined with or separated from the Flow channels using a pair of gateways placed in front of the ONU/OFNU and at the entry to the distribution fiber. B. Control Plane In a conventional PON, the network controllers embedded in the OLTs function independently and maintain local databases to track the availability and usage of attached network resources and users. Such management approach is sufficient given that the network resources installed in each PON are not shared with the other PONs. However, in the proposed R-LR UltraFlow access network, the assignment of OFNUs to OFLTs is not fixed. It is dynamically modified to maximize the network utilization and energy efficiency. As a result, a locally operated resource management unit is no longer applicable. One possible solution is to setup periodical data broadcast among the OFLTs so that information about traffic loads and resource usage in each subnet can be synchronized. The drawback of this approach is that the burden of maintaining and processing of these data may complicate the design of OFLTs and consequently, increase the CapEx and OpEx of the UltraFlow access network. Duplicated

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network information also causes unnecessary waste of storage. In addition, distributed control of the switching modules could possibly incur excessive switching delays due to the complexity of control synchronization. To address the aforementioned drawbacks, we deploy an SDN controller in the CO to achieve higher flexibility and efficiency in UltraFlow access control. As illustrated in Fig. 1, the SDN controller decouples the control plane from the physical plane and centralizes the network resource management in a single node. The Flow service application in the SDN controller receives Flow requests from users and sends instructions to corresponding UltraFlow nodes, including OFNUs, OFLTs and switching modules, through the IP interface. When new OFNUs become active, they directly register with the SDN controller, and therefore, re-registration is not required when these OFNUs are reassigned to different source groups (i.e. groups of OFLTs). If the coexisting IP PON is also SDN compatible [16], the IP and Flow services can be integrated into the SDN controller. The dual-mode service is then managed in a unified UltraFlow access control plane and the Flow control messages are forwarded to Flow control application from the IP control application in the software level. The IP control application should be able to operate independent of the Flow control application. No modifications to existing IP control protocol is required for the introduction of Flow service. On the other hand, with a comprehensive view of the UltraFlow access network, the SDN controller can facilitate more efficient network resource allocation (e.g., assignment of transceiver and wavelength) for both the IP and Flow services. The abstraction from the physical plane may help reduce inventory costs for the UltraFlow access deployment by allowing interchange of the OLTs and OFLTs, if the device specifications are matched. The SDN controller also facilitates the management of the two reconfigurable switching modules, SW-1 and SW-2. The communication between the switching modules and the SDN controller is achieved through the IP network. The switches only need to be operated when changes in network traffic warrants modification in user grouping or fault occurs. Therefore, the system should have high tolerance to delay in control signaling, and the provision of IP network access to the switch could be any commercially available solution, including wired Ethernet, WiFi or even cellular network. III. SCALABILITY ANALYSIS OF R-LR ULTRAFLOW NETWORK The scalability of the UltraFlow access network is mainly constrained by two factors: number of available wavelengths and channel power budget. Wavelength reuse can be achieved in the proposed reconfigurable architecture as long as the two channels on the same wavelength are assigned to different feeder fibers. Therefore, the constraint of available wavelength is translated into the number of available feeder fibers. Assuming the 80 ITU-T DWDM grids with 50 GHz channel spacing [9] is used and Nfeeder feeder fibers are installed, the maximum number of users supported is 80Nfeeder . Given enough feeder fibers, the scalability of the R-LR UltraFlow access is determined by the channel power budget. Fiber

JOURNAL OF LIGHTWAVE TECHNOLOGY, VOL. 32, NO. 13, JULY 1, 2014

attenuation, device insertion loss and high splitting ratio are the main contributors to signal degradation. To determine the maximum number of users supported by either of the three system configurations proposed in Fig. 1 (i.e., Flow channels between nodes A and C, A and D as well as B and E), we analyze the power budget in these three scenarios respectively. Since the upstream signal does not suffer from massive splitting attenuation that exists in the downstream transmission, the power budget requirement of the upstream Flow channel should be automatically fulfilled once the downstream power budget is satisfied. Hence, our power budget analysis only focuses on the downstream. The following variables are defined for the power budget analysis:

Ptx GEDFA m ax PEDFA RN Lfeeder Ldist Lcirc Lgw Lawg Lfilter

output power of transceiver; maximum gain of the EDFA; maximum output power of the EDFA; loss in the feeder fiber; loss in the distribution fiber; insertion loss of circulator; insertion loss of gateway; insertion loss of AWG; insertion loss of the tunable filter at the receiving end; insertion loss of the optical switch with switching Lsw N ratio 2N ; Lsp in/ex N insertion loss of the optical splitter including/excluding the 1 × 2N splitting loss; total loss on the user premises in downstream; Luser total downstream loss in the central office; LCO insertion loss of SW-1 in downstream; LSW -1 insertion loss of SW-2 in the downstream; LSW -2 Nbk/ext number of backup feeder fibers or external interconnections; number of source groups (i.e. number of SW-1); Nsrc group Nuser group number of supported user groups; Ngroup size number of users per user group; number of feeder fibers; Nfeeder sensitivity of the receiver. Rsens To ensure error free transmission, the received signal power in the downstream should be larger than the receiver sensitivity Ptx − LCO + GEDFA

m ax

− Lfeeder + GEDFA

m ax

− LSW −2 − Lsp

in log 2 N g r o u p

siz e 

− Luser

ds

− Ldist > Rsens (1)

and for the Flow channel between A and B LCO = Lawg + Lcirc

LSW −1 = Lsp

ex

+ Lsp

log 2 N b k / e x t  + Lsw in 2

(2)

log 2 N s r c

group 

(3)

SHEN et al.: RECONFIGURABLE LONG-REACH ULTRAFLOW ACCESS NETWORK: A FLEXIBLE, COST-EFFECTIVE, AND ENERGY-EFFICIENT

TABLE I INSERTION LOSS OF ULTRAFLOW COMPONENTS Symbol G E D FA P E D FA Ptx α Lc ir

m ax m ax

LSW −2 = Lsp

TABLE II MAXIMUM NUMBER OF SUPPORTED USER

Value

Unit

Symbol

Value

Unit

28 20.5 3 0.2 0.5

dB dBm dBm dB/km dB

Lgw Ltx

0.5 2 0.5–1 3.6–16.2 0.6–1

dB dB dB dB dB

L s w 2 −6 4 L s p i n 2 −3 2 L s p e x 2 −3 2

in log 2 N u s e r

Luser = Lsp

group 

in 2

+ Lsw

log 2 N f e e d e r 

+ Lcirc + Lfilter

Flow Connection Nu ser

log 2 N b k / e x t  + Lsw

log 2 N s r c

group 

Luser = Lgw + Lcirc + Lfilter .

(7)

The replacement of a 1 × 2 splitter with a gateway on user premises boosts the power budget for about 3 dB and increases the maximum number of supported users to 1024. Similarly, the power budget of the Flow channel between points B and E can be computed by using the user loss defined in Eq. (7) and adding the additional gateway loss Lgw to the left hand side of Eq. (1) Ptx − LCO + GEDFA

m ax

− Lfeeder + GEDFA

m ax

− LSW −2 − Lgw − Lsp

in log 2 N g r o u p

siz e 

− Luser

ds

A to D

B to E

512

1024

1024

(5)

+ Lgw (6) and the power loss on the user side in Eq. (5) is reformed as ex

m ax

A to C

(4)

where xis the ceiling of x. The component specifications used in the numerical power budget analysis such as fiber attenuation coefficient α are listed in Table I. These values are extracted from the data sheets of off-the-shelf products, experimental measurements and other literatures [17]–[23]. The length of the feeder fiber is 80 km, and the length of the distribution fiber connecting the RN to the end users is 20 km. The input power to the feeder fiber is set to 3 dBm. In an R-LR UltraFlow access network with thirteen feeder fibers (Nfeeder = 13, the minimum number of feeder fibers required to support 1024 simultaneous Flow access) and a user group size of 32 (Ngroup size = 32), numerical calculation indicates that the Flow channel configuration between points A and C can support up to 512 users in the downstream without violating the inequality in Eq. (1). It is noted that changing the splitting ratio in the distribution network between the RN and end users requires corresponding changes in the splitting ratio in SW-2 to keep the overall splitting ratio larger or equal to the total number of users. Therefore, varying the size of user groups will not affect the network power budget. To calculate the power budget of the Flow channel between points A and D, the loss in SW-1 should be modified as LSW −1 = Lsp

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− Ldist > Rsens (8)

Fig. 2. Experimental setup for the optical Flow channel. The feeder fiber is 80 km long SMF and the distribution fiber is 20 km long SMF. The wavelength used for the Flow channel is 1550.12 nm. Attenuation in the SW-2 is emulated by a VOA.

It is found that the power budget between points A and E can also afford 1024 Flow users. Table II summarizes the maximum number of users Nuser m ax supported by the three different Flow connection scenarios. IV. TESTBED AND EXPERIMENTAL RESULTS A. Data Plane To demonstrate the Flow channels in the data plane of R-LR UltraFlow access network, we experimentally measured the BERs for the three different Flow channel configurations on the testbed. As illustrated in Fig. 2, a 10 Gbps tunable transceiver is installed on a test board receiving NRZ PRBS 215 –1 data stream from a pattern generator. The optical signal is passed through an AWG that supports 40 input channels, and the output is fed into SW-1. The SW-1 consists of one 1 × 2 splitter and one 1 × 2 switch so that one remote/backup feeder fiber is supported. After propagating through another 1 × 2 splitter used for combining IP and Flow channels, the optical signal is pre-amplified to 3 dBm before traveling through the 80 km single mode fiber (SMF). Attenuations in the SW-2 are emulated by an optical attenuators (VOA) and is set to 18 dB. This value can be translated into a combination of 1 × 32 splitting ratio and a 1 × 16 switching ratio in SW-2 which corresponds to a system setting that contains maximum 16 feeder fibers for simultaneous Flow access from 32 Flow user groups and each user group contains 32 users (assuming the ITU-T DWDM grid with 50 GHz channel spacing in C-band). The cross-talk in the switches in SW-1 and SW-2 is ignored due to the low cross-talk in commercial optical switch (e.g., -80 dB). The Flow channel is amplified again at the entry of the 20 km distribution fiber. A third VOA is used to emulate either the loss of a 1 × 32 splitting ratio for a user group size of 32 (i.e., the connection

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IP/Ethernet to PNRL03, which emulates the SDN controller. The software Flow controller allocates the required resources, including wavelength and transmission time slot, after checking their availability in a local database, and distributes the grants to both machines. Upon the start of the scheduled Flow session, the transport control software in the OFNU and OFLT will be invoked to conduct the Flow transmission and ensure the integrity of the file being transmitted/received. Our experiments demonstrated successful transmissions of three files with different sizes of 1 GB, 9GB and 18 GB between PNRL01 and PNRL02 with a near optimal throughput around 9.8 Gbps over a 10 Gbps link configured as in Fig. 2. Fig. 3.

BER measurements of the R-LR UltraFlow access testbed.

V. COST EFFECTIVENESS OF R-LR ULTRAFLOW ACCESS

between points A and D in Fig. 1, or a 1 × 16 splitting ratio for a user group size of 16 and a 1 × 2 splitter for the combination/separation of IP and Flow channel (i.e. the connection between points A and C in Fig. 1). Fig. 3 shows the BER measurements obtained in the experiments. Chromatic dispersion (CD), amplified spontaneous noise and other sources of signal distortions cause a 1.9 dB power penalty at the receiver.

Cost effectiveness is one of the main concerns of network operators in the design and deployment of their networks. To maximize the return on investment, new network deployment needs to be carefully planned to avoid unnecessary waste of resources. Meanwhile, CapEx on initial service deployment should also be minimized to reduce the financial burden and investment risk faced by the operators. Multiple proposals have been presented for cost effective introduction of new access network architectures [10], [17]. The Stanford University aCCESS architecture discussed in [10] not only decreases the total transceiver counts, but also prepares the network for low cost upgrade in the future. Authors in [17] proposed a channel combine/split module to reduce the introduction cost of a long reach WDM PON by dynamically assigning scattered users to already active OLTs. In this section, we first establish a service take-up rate model to trace out the yearly increase in Flow service subscription after the initial deployment. Based on this model, we demonstrate how the proposed R-LR UltraFlow access architecture can help reduce the CapEx in early deployment of optical Flow service and enable pay-as-you-grow in the subsequent years. As the capacity of IP channel does not affect the performance of Flow channel, planning and deployment of the IP service can be separated from that of the Flow service if no IP PON already exists in the targeted area. Therefore, without loss of generality, our cost-effectiveness analysis only focuses on the Flow part of the UltraFlow access network.

B. Control Plane

A. Service Take-Up Rate

We demonstrate the proposed SDN control plane by constructing the testbed shown in Fig. 4. A user connected to an OFNU (emulated by the computer PNRL02) downloads a large file from a file server connected to the OFLT (both emulated by the computer PNRL01). The Flow transmission between the OFLT and OFNU is tested against the physical channel as per the configuration between points ‘X’ and ‘Y’ in Fig. 2. The control link is established over IP via a network switch using standard copper Ethernet cable. In real deployment, the SDN controller (PNRL03) and the OFLT (PNRL01) are housed in the central office. The experiments are conducted as follows: the user connecting via PNRL02 initiates a download request from PNRL01 through a web browser. Once the file server on PNRL01 responds, both ends send a Flow request through

One major drawback of conventional one-time deployment of access network is the waste of unused network resources. Statistical data from multiple FTTH councils and research institutes suggests an approximately 20–30% take-up rate in the first few years of operation and the number gradually increases at a reduced rate in the following years [24], [25]. Since no statistical user data exists for the optical Flow network, an approximation of the service take-up rate of existing access networks [24], [25] is used for the yearly subscription rate of UltraFlow access network. As depicted in Fig. 5, the take-up rate increases by 10% every year after reaching 20% in the first year. The increasing rate drops gradually to 1% in subsequent years until a full take-up rate is achieved in the 20th year. A cost-effective UltraFlow access architecture should be able to grow with this

Fig. 4.

Control plane testbed.

SHEN et al.: RECONFIGURABLE LONG-REACH ULTRAFLOW ACCESS NETWORK: A FLEXIBLE, COST-EFFECTIVE, AND ENERGY-EFFICIENT

Fig. 5.

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User take-up rate in a 20-year period.

take-up model and install minimal number of devices based on user demands. B. Cost Effective Deployment of R-LR UltraFlow Access The reconfigurable architecture breaks the geometric boundaries between user groups in the UltraFlow access network. Any user can be dynamically assigned to an arbitrary OFLT. This allows the operator to install less components when the service take-up rate is low. Since fiber installation is common to all long-reach PON deployment and the cost of feeder fiber is negligible compared to that of fiber installation, fiber related cost is excluded in the following cost analysis. In addition, the exact market prices of network equipment could vary dramatically depending on the geographical location, and due to the novel nature of UltraFlow networks, no commercial OFLT and OFNU are currently available. Meanwhile, the number of OFLT line cards, or OFLTs for short, scales with the increasing subscriptions along with the number of transceivers, optical amplifiers and other major UltraFlow access components. That indicates a proportional relationship between the number of OFLTs and the CapEx of the UltraFlow access network. Hence, instead of directly comparing the CapEx, we analyze the costeffectiveness of the R-LR UltraFlow access architecture by calculating the number of OFLTs required in each year after initial deployment. In the numerical analysis of cost-effectiveness, we consider a system consisting of Nuser m ax Flow service subscribers. The transceivers support 80 WDM channels (i.e., 80 channels in ITU-T DWDM C-band grid with 50 GHz channel spacing) and each OFLT contains Ntrx OFLT transceiver ports. It is also assumed that the new subscribers in each user group arrive uniformly. To benchmark the cost-effectiveness of the proposed R-LR UltraFlow access architecture, the number of required OFLTs in a fixed long-reach (F-LR) UltraFlow access network is also calculated for comparison. An F-LR UltraFlow access network is an R-LR UltraFlow access network without the switching capability (i.e., no SW-1 and SW-2). This leads to several standalone UltraFlow access networks with maximum subscriptions equal to the maximum channels supported by the adopted WDM grid. Two alternative deployment strategies, namely

Fig. 6. Number of installed OFLT in each year after initial deployment of fixed/reconfigurable LR UltraFlow access network with different (a) N u se r m a x and (b) N trx O F LT .

one-time installation and evolutionary installation are considered for the deployment of the F-LR UltraFlow access network. In the case of one-time installation, the full UltraFlow access capacity is deployed in the first year and no future installation is needed, while the evolutionary strategy attaches additional OFLTs to the AWG whenever demands raise [10]. Fig. 6(a) shows the percentage of reduction in number of installed OFLTs achieved by the R-LR UltraFlow access architecture with respect to the F-LR UltraFlow access architecture deployed using either the one-installation strategy or the evolutionary installation strategy given different values of Nuser m ax (i.e., the maximum number of users supported by the network) and a fixed value of Ntrx OFLT = 16 (i.e., the number of transceivers installed on one OFLT). In practice, each transceiver may serve multiple users through time division multiplexing. However, this is equivalent to proportionally reduce Nuser m ax . Hence, without loss of generality, each user is assumed to occupy one transceiver in the subsequent analysis for simplicity. As observed in Fig. 6(a), evolutionary deployment of the R-LR UltraFlow access architecture significantly reduces the number of installed OFLTs in the early years of deployment compared to one-time installation of the F-LR UltraFlow access network, when the take-up rate is low. More than 75% savings in the number of OFLTs and proportionally the total system CapEx is achieved. The amount of savings is maintained above

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10% for 10 years. Comparing to the evolutionary deployment of F-LR UltraFlow access network, the reconfigurable architecture achieves almost 30% more savings in OFLT installation during the first year and retains its advantage in CapEx saving for another 4 years. The additional savings is due to the sharing of OFLTs from the same source group. SW-2 is able to dynamically group the Flow users in different user groups and assign them to the same source group. On the contrary, the network resources in one sub-network cannot be accessed from the other subnetworks in the F-LR UltraFlow access network. As a result, new OFLTs needs to be installed for each individual sub-network when the demands exceed the current supply. It is also noted that the value of Nuser m ax has no significant impact on the percentage of savings in CapEx due to the normalization of savings with respect to the CapEx of full UltraFlow access capacity. The influence of Ntrx OFLT on the CapEx savings is studied in Fig. 6(b) with Nuser m ax fixed to 1024. The increasing number of transceivers installed on each OFLT reduces the total amount of OFLTs required for full system capacity and therefore, the initial savings in the number of installed OFLT or equivalently the overall CapEx decreases with increasing Ntrx OFLT . On the other hand, a larger value of Ntrx OFLT results in more redundant resources in each subnet of the F-LR UltraFlow access network in which only AWG is used for evolutionary deployment. Hence, the percentage of savings in the number of OFLTs achieved by the reconfigurable architecture with respect to that of the fixed architecture increases from 10% to around 30% when Ntrx OFLT increases from 4 to 16. VI. ENERGY EFFICIENCY ANALYSIS OF R-LR ULTRAFLOW ACCESS NETWORKS In recent years, energy efficiency has become an important aspect in the design of next-generation optical access network [26], [27]. One popular approach is to turn the ONUs in idle state to sleep mode so that energy can be saved when no network traffic is being requested or generated by the users [28]. More recently, new access network planning approach [13] and architecture [17] have been proposed to implement traffic-aware network operations that reduce the power consumption based on user behaviors. As user activities constantly vary in a day, shutting down unnecessary network equipments in low traffic periods is useful to conserve energy. In this section, a user behavior model is presented using a daily network traffic profile. Based on this model, we propose and analyze a source assignment scheme to conserve energy in the daily operation of the R-LR UltraFlow access network. A. Network Traffic Profile The daily network traffic demands vary dramatically with time. The study in [29] suggests that the network traffic reaches the lowest level early in the morning when most people are inactive (typically sleeping). It rises to the highest level when people are active and using the Internet for work or entertainment (i.e., between 8 a.m. and 9 p.m.). The difference between the maximum and minimum traffic demands in a day is about

JOURNAL OF LIGHTWAVE TECHNOLOGY, VOL. 32, NO. 13, JULY 1, 2014

Fig. 7.

Normalized daily network traffic profile.

60% in Europe and 50% in America. This large variation in daily network traffic can definitely be exploited to reduce the power consumption in the access network. Without loss of generality, we assume the daily network traffic profile Traf(t) used in the power saving analysis to be a piecewise sinusoidal approximation of the usage curves in [29] and is given by: ⎧1 sin [2π (t + 8) /16] + 35 for 0 ≤ t ≤ 8 ⎪ ⎪ ⎨5 2 3 for 9 ≤ t ≤ 20 T raf (t) = 5 sin [2π (t − 8) /48] + 5 ⎪ ⎪ ⎩ 25 sin [2π (t − 16) /16] + 35 for 21 ≤ t ≤ 24 (9) The resultant daily network traffic Traf(t) is plotted in Fig. 7. B. Energy-Efficient Source Assignment in R-LR UltraFlow Access Networks In the R-LR UltraFlow access network, the reconfigurable switching modules enable dynamic reconfiguration of the connections between the source and the users. OFNUs from different user groups can be combined and assigned to any of the source groups formed by the AWGs in the CO. Consequently, idle devices such as OFLT, EDFA and chassis can be set to sleep mode or turned off to conserve energy during low traffic periods. On the other hand, frequent network reconfigurations may cause service interruption due to the switching overhead. Therefore, the resource assignment scheme used by the network management system should not be executed in small intervals. The optimal reconfiguration interval will depend on many factors such as the rate of changes in traffic load and switching efficiency. In this study, we conduct the reconfiguration hourly to comply with the approximated daily traffic model presented in Fig. 7. The relatively long configuration interval allows the use of a sophisticated offline scheme to optimize the source assignment in the R-LR UltraFlow access network. We propose an integer linear programming (ILP) based source assignment algorithm to compute the most energy efficient network reconfiguration for a given set of active users. The principle of the ILP algorithm is to minimize the total power consumption of the R-LR

SHEN et al.: RECONFIGURABLE LONG-REACH ULTRAFLOW ACCESS NETWORK: A FLEXIBLE, COST-EFFECTIVE, AND ENERGY-EFFICIENT

UltraFlow access network by minimizing the number of active equipment used to support the instantaneous Flow traffic. The power consumption of control/management system in the CO and the equipment installed at the user premises are common for all architectures and are, therefore, excluded from the power analysis. The switching modules are also neglected due to their much smaller power consumption [18]. The following parameters are defined for the ILP formulation. Input parameters: maximum number of channels in one feeder fiber; Nuf m ax number of active users in the ith user group; Nugı total number of user groups; Nug number of users served by each OFLT; Nuo number of OFLT line cards installed in each No c chassis; number of users served by each tunable Nut transceiver; number of installed feeder fibers; Nf PEDFA m ax maximum output power of EDFA; Pprior channel power after pre-amplification in CO; channel power after post-amplification at RN; Pp ost power consumption of OFLT; POFLT power consumption of chassis; Pchassis power consumption of tunable transceiver; Ptrx power consumption of EDFA. PEDFA Output parameters:  1, if user group i is assigned to the jth feeder fiber xij = 0, otherwise. Nuf j No j

number of users attached to the jth feeder fiber; number of active OFLTs attached to the jth feeder fiber; Ne RN j number of active EDFAs attached to the jth feeder fiber in the remote node; Ne CO j number of active EDFAs attached to the jth feeder fiber in the central office; number of active tunable transceivers jth feeder fiber; Nt j number of active chassis. Nc The ILP source assignment algorithm is formulated as Minimize Ptotal = PEDFA (Ne

Subject to N f xij = 1 j =1

N u g

Nuf

j

=

Nuf

j

≤ Nuf

i=1

CO

+ Ne

RN )

+ POFLT No

+ Pchassis Nc + Ptrx Nt

(10)

for i = 1, 2, . . . , Nug

(11)

Nug i xij

m ax Nut

Nuf j Pprior /Nut PEDFA

for j = 1, 2, . . . , Nf

for j = 1, 2, . . . , Nf m ax

≤ Ne

CO j

(12) (13)

for j = 1, 2, . . . , Nf (14)

Nuf j Pp ost /Nut PEDFA

m ax

≤ Ne

RN j

for j = 1, 2, . . . , Nf (15)

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TABLE III VALUES OF MAIN SYSTEM PARAMETERS Symbol

Value

Unit

Symbol

Value

Unit

PO F LT P E D FA Ptrx Pch a sis Pp rio r P E D FA

89 3.5 8 130 1 112

W W W W mW mW

Pp o st Nu o No c Nu t Nf Nu f m ax

23 16 8 1 11 102

mW

m ax

Nuf j /Nut ≤ Nt

for j = 1, 2, . . . , Nf

(16)

Nuf j /Nuo ≤ No j for j = 1, 2, . . . , Nf N f No j /No c ≤ Nc .

(17)

j =1

j

(18)

The objective Ptotal is the total network power consumption. Constraint (11) guarantees each user group is only connected to one feeder fiber. Constraints (12) and (13) calculate the number of users assigned to each feeder fiber and ensures that it is less than the maximum number of optical channels supported by the WDM grid. Constraints (14) to (18) force the number of EDFAs, tunable transceivers, OFLTs and chassis to be larger or equal to the minimum required amounts. Assuming the Flow service demand is proportional to the number of active users, the normalized daily network traffic profile shown in Fig. 7 is equivalent to the fraction of users that remain active in each hour. It is also assumed that the active users are uniformly distributed among the user groups. For hourly changes in the Flow traffic demand, the ILP problem formulated above is solved for network reconfiguration. Since no commercial OFLT is yet available, the power consumption of OFLT is approximated by that of off-the-shelf OLT and its value is listed in Table III with other main system parameters [22], [23], [30], [31]. Fig. 8(a) compares the normalized power consumption of an R-LR UltraFlow access network and that of an F-LR UltraFlow access network given different maximum number of users (Nuser m ax ). The number of users supported by each OFLT (Nu o ) is fixed to 16. The assignment of source group (i.e., a set of OFLTs) to OFNUs cannot be altered in the fixed UltraFlow access network and devices in the CO such as OFLTs and chassis are turned off to save power, if all users attached to that particular source group are inactive. As shown in the figure, the power consumption curve of the R-LR UltraFlow access network follows closely the network traffic profile in Fig. 7. The reconfigurable architecture achieves maximum power saving of more than 40% at low traffic hours, compared to the fixed architecture. Although the simple power saving technique also helps the F-LR UltraFlow access network adapt its power consumption to the time varying network traffic, the performance is limited by the fixed binding between the source groups and user groups. The two power consumption curves overlap between 15:00 and 20:00 when the traffic demand reaches the highest level in a day. Moreover, since the daily variation in the number of active users is less significant in an UltraFlow access

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the two main sources of power consumptions are already active before the demands reach the highest level. VII. CONCLUSION In this paper, we propose a new software-defined R-LR UltraFlow access network to provide dual-mode service, IP and Flow, to a large number of users in a wide area with reduced CapEx for initial deployment and daily power consumption. A maximum 100 km Flow connection to 1024 users and the implementation of the SDN controller are experimentally demonstrated on our testbed. Numerical analysis indicates that the reconfigurable architecture can achieve more than 75% savings in the number of OFLT installation or proportionally in CapEx in the early years of deployment compared to one time installation of a fixed longreach UltraFlow access network, and around 30% savings compared to previously proposed pay-as-you-grow schemes. It is also found that the network capacity has no noticeable influence on the CapEx savings, while installing more transceivers on the OFLT increases the advantage of the reconfigurable architecture over the fixed architecture in CapEx saving. Moreover, a new ILP-based resource allocation algorithm is proposed. It is proved to be effective in the adjustment of R-LR UltraFlow access power consumption according to the network traffic profile. REFERENCES

Fig. 8. Normalized daily power consumption of F-/R-LR UltraFlow access networks with (a) different maximum numbers of users N u se r m a x in the system and (b) different number of users supported by each OFLT N u o .

network with less subscribers, the corresponding variation in the amount of active equipment is also smaller compared to a system supporting more users. Hence, the energy efficiency of the reconfigurable architecture slightly decreases with the maximum capacity of the UltraFlow access network as observed in Fig. 8(a). To study the impact of OFLT capacity (i.e., the number of users supported by each OFLT) on the system energy efficiency, the normalized power consumption of an R-LR UltraFlow access network and that of an F-LR UltraFlow access network is compared against different values of Nu o with a fixed value of Nuser m ax = 1024 in Fig. 8(b). As can be seen in the figure, the energy efficiency of the fixed architecture deteriorates with the decreasing OFLT capacity due to the increased number of isolated subnets. Meanwhile, the resource sharing capability enabled by the reconfigurable architecture makes the power consumption indifferent to the changes in OFLT capacity when the traffic demand is low. During the high traffic periods, the power consumption of the R-LR UltraFlow access increases to overlap with that of the F-LR UltraFlow access earlier given a higher OFLT capacity, as the control granularity over the network resources is reduced and all OFLTs and chassis which are

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Authors’ biographies not available at the time of publication.