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Abstract—This paper discusses prospects and challenges of optical networking designed to meet exponentially growing demands of data traffic. We discuss both intra and inter data-center networking. For intra-datacenter networking, a single arrayed waveguide grating router based optical switch offers fully connected all-to-all parallel interconnection amongst thousands of racks to achieve high throughput and low-latency interconnection in a new flattened network topology. This new interconnection network named LION provides significant performance advantages over other electrical, optical, or hybrid interconnection networks in terms of available bandwidth per channel, end to end latency and throughput. For inter-datacenter networking, we investigate flexible bandwidth elastic optical networks designed to adaptively accommodate bursty and high capacity traffic between the data centers while supporting the traditional traffic in the background. Further, we demonstrate adaptive and real-time control of the spectral assignment and modulation format for each slice while achieving defragmentation to optimize the spectrum utilization under the dynamically varying traffic demands. The paper covers the intra- and inter- datacenter networking architecture, algorithm, control plane and simulation and experimental studies from the network testbed.

Index Terms—Data-center networking, network control and management, flexible bandwidth elastic optical networks, resource allocation, impairment-aware networking, network defragmentation.

I. INTRODUCTION

ARGE-SCALE computing platforms (called data centers or warehouse-scale computing) that drive the information

technology have significantly transformed our lives over the past decades. We rely increasingly on these computers for all aspects of our lives like healthcare, banking, entertainment, communications, news, shopping etc. Emerging applications are even more data intensive. In the healthcare sector alone, we are seeing rapid transitions in data processing from two-dimensional images to three-dimensional or hyper-spectral real-time 3D images. Such data services based on data centers (or cloud computing) are becoming more pervasive and ubiquitous through expansion [2].

However, today’s data centers have already reached scalability limits by consuming megawatts of power in large data centers. Power consumption is a serious roadblock for

scaling the data centers even if we can keep up with Moore’s Law [3] in terms of integrating more devices on each chip since the Dennard’s scaling Law [4] for obtaining simultaneous improvements in transistor density, switching speed and power dissipation have failed nearly a decade ago. While the emergence of multi-core processors has provided temporary relief in the performance/watt, networking and communication have become the bottleneck in scalability and performance. Amdahl’s law suggests that a system with balanced processing, memory, and communications performs best across most applications, but today’s computing systems are typically unbalanced by more than two orders of magnitude. The balanced system with 100 Tera FLOPs would need 100 TeraBytes of memory and 100 TeraByte/second (800 Tb/s) bisection bandwidth. This is already more than two orders of magnitude greater than the average Internet traffic in the U.S. at 5 Tb/s today, indicating that the communication bandwidth inside the data center can far exceed that outside the data center for it to achieve optimized and balanced operation for power efficiency and scalability. Optical networking exploiting optical parallelism is of key importance in data centers with balanced and concurrent parallel computing. Hence, a new intra-datacenter networking architecture is essential for the future scalable data infrastructure.

On the other hand, the inter-datacenter networking shares traffic with other multi-media, data, and voice traffic while requiring periodic data mirroring and backup between multiple datacenters. Such scheduled data mirroring and backup should achieve vast amount of data transfer rapidly and reliably between the data centers normally widely distributed around the globe. Due to the high peak rate and the limited temporal usage, together with the rapid and global expansion of ubiquitous and cloud computing, it is likely to see the inter-datacenter networking to take place in the public networks in the foreseeable future. This naturally means that the public network must share the network resources and accommodate bursty and huge inter-data center communication demands. Figure 1 shows the heterogeneity of the intra and inter-datacenter networks. In light of such context, a networking technology for flexible and elastic assignments of the shared bandwidth resources in core optical networks is greatly in need.

Intra and Inter Datacenter Networking: The Role of Optical Packet Switching and Flexible Bandwidth Optical Networking

S. J. B. Yoo, Fellow, IEEE, Fellow, OSA, Yawei Yin, Member, IEEE, Ke Wen, Member, IEEE

(Invited Paper)

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Flexible bandwidth optical networking emerged in the recent few years as a promising solution for implementing flexible bandwidth channels (flexpaths) that efficiently match allocated bandwidth with demand using agile granularities of spectrum allocation beyond the rigid ITU-T grid (G.694.1). [5]. Such networks have the capability of provisioning both subwavelength and superwavelength channels with arbitrary bandwidth according to the user demands in contrast to conventional WDM networks that are limited by the fixed bandwidth per channel due to the ITU grid spacing. Additionally, flexible bandwidth networking supports adaptively changing the modulation format of individual channels to retain high-bitrate and high-quality transmission under varying link conditions [6-8]. This approach has also been shown to achieve higher spectral utilization efficiency by eliminating the stranded bandwidth due to bandwidth mismatch and by reducing the spectral guard bands [5]. However, the added flexibility in such networks also raises challenges regarding the network’s control and management of spectral resources. Firstly, the use of arbitrary bandwidth channels (flexpaths) can results in spectral fragmentation [1, 9], which increases the blocking probability and limits the overall traffic capacity as a result. Spectral defragmentation is already a topic of interest in the research community [1, 9, 10]. Secondly, the potentially large bandwidth flexpaths (> 100 GHz) generated in flexible bandwidth networks are increasingly susceptible to physical layer impairments (PLIs) [11, 12]. A major challenge for flexible bandwidth networks is dynamically adapting to time-varying PLIs using adaptive

network control and managements. The remainder of this paper is organized as follows. Section

II describes the challenges and recent progresses of the intra data center networking technologies, exploiting the low-latency and high-throughput features of the light interconnect optical network (LION) switches. Section III discusses the enabling technologies of flexible bandwidth optical network architectures for inter data center communications. Section IV discusses about the spectral defragmentation requirements and methods in flexible bandwidth networking scenarios. Section V concludes the paper.

II. LIGHT INTERCONNECT OPTICAL NETWORK SWITCHES FOR

INTRA-DATACENTER NETWORKING

Arrayed waveguide grating router (AWGR) [13, 14] is a passive wavelength routing component that enables all-optical switching. The main difference between the electronic crossbar switch and the AWGR switch is that (a) the N x N AWGR switch provides simultaneously fully connected all-to-all interconnection thus providing N2 simultaneous connection links, (b) there is no switching component within the core of the AWGR switch fabric (AWGR is a passive element), (c) the switching component scales linearly as the number of connection links supported by the switch, and (d) the AWGR supports parallel wavelength interconnection covering huge (>20 THz) optical bandwidths.

Figure 2 illustrates the (a) physical device construction, (b) wavelength routing property (N = 5 example) and (c) wavelength assignment table for switching of an AWGR

Figure 2. N x N arrayed waveguide grating router’s (a) physical device construction and (b) wavelength routing property (N = 5 example), and (c) wavelengtassignment table (figures courtesy of NEL).

Figure 1. Heterogeneous inter- datacenter networks.

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component. As Figure 2 (b) demonstrates, the well-known wavelength routing property of the N x N AWGR supports simultaneous and non-blocking interconnections of each of N input ports with all of N output ports by using N wavelengths. For the N = 5 example, there are N 2 = 25 simultaneous interconnections. Each input port can utilize a wavelength (instead of electronic headers) to address the output port. For the example in Figure 2 (b), the input port 3 can communicate with the output port a, b, c, d, and e, by use of 5, 4, 3, 2, and 1, respectively. When using the input port 2, the cyclic routing nature of the AWGR allows addressing of the output port a, b, c, d, and e, by use of 1, 5, 4, 3, and 2. Seen from the output port c, the packets from input port 1, 2, 3, 4, and 5 arrive on 5, 4, 3, 2, and 1, hence, wavelength demultiplexing can separate packets from each input port into separate receivers. For this reason, any input port can use a tunable transmitter to address each output port by tuning the transmitter to the corresponding wavelength without contention if each output port is equipped with a wavelength demultiplexers and N receivers. Alternatively, the N x N AWGR can be used in all-to-all interconnection without contention by employing N transmitters and N receivers at each port.

The AWGR technology supports scalable wavelength routing demonstrated up to 2010 ports [15] and this enables transformation of the traditional multi-stage data center interconnection of various size electronic switches (Figure 3) to a single hop and flattened interconnection topology (Figure 4) while supporting all-to-all interconnection.

Figure 3. Typical data center interconnection topology involving various sizes of electronic switches. [16]

Figure 4. Flattened data center interconnection topology involving N x N AWGR based LION switch.

Figure 5. The system diagram of the proposed rack to rack LIONS optical switch, LD: Label Detector; OLG: Optical Label Generator; PE: Packet Encapsulation; LE: Label Extractor; FDL: Fiber Delay Line; TX: Transmitter; RX: Receiver; PFC: packet Format Converter; O/E: Optical-to-Electrical converter; E/O: Electrical-to-Optical Converter.

Figure 5 shows an AWGR based hybrid interconnecting architecture, referred to as lightwave interconnect optical network switch (LION switch, or LIONS), which also includes optical channel adapters (OCA), tunable wavelength converters (TWC), loopback buffers and the control plane for the switching fabric. LIONS uses label switching with the optical label transmitted on a different wavelength. We compared the performance of rack to rack LIONS with other state-of-art rack to rack switches including the electrical switching network architecture employing flattened butterfly topology, the OSMOSIS optical switch and the Data Vortex optical switch [17-20]. Figure 6 (a) and (b) show the effective bandwidth and the end-to-end latency of LIONS comparing with a flattened butterfly network under a 32- and 128-node for a message size of 128 bytes. The average end-to-end latency of the flattened butterfly network increases much faster than that of the LIONS system under moderate network load. The flattened butterfly network saturates more easily with increasing network size, while the latency of LIONS is almost independent of the size of the network. The effective bandwidth comparison shows that LIONS can support heavy network load of up to 90% and beyond. We simulated the case where the LIONS has a radix of 64 and the message size is 256 bytes (the packet size is 324 bytes) so that the setting is comparable with that of OSMOSIS demonstrator. At the load of 90%, the latency of LIONS is still less than 190 ns. In comparison, the minimum achieva`ble latency of OSMOSIS is above 700 nanoseconds by only considering data path delay as well as STX arbitration [21]. Figure 6 (c) shows the effective bandwidth comparison. LIONS achieves a little higher effective bandwidth than OSMOSIS, because the InfiniBand label is smaller than the total overhead of the OSMOSIS. Figure 6 (d) shows the effective bandwidth comparison. Notice that Data Vortex saturates before the uniform network load reaches 0.5 [21]. Clearly LIONS outperforms both OSMOSIS and Data Vortex in terms of effective bandwidth. The results also indicate label rate actually affects latency performance of LIONS, higher label rate can reduce the end-to-end latency.

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Figure 6. LION switch comparison with other switches. (a) the effective bandwidth LIONS comparing with a flattened butterfly network under a 32- and 128-node for a message size of 128 bytes, (b) the end-to-end latency of LIONS comparing with a flattened butterfly network under a 32- and 128-node for a message size of 128 bytes, (c) the effective bandwidth comparison for LIONS vs. OSMOSIS, and (d) the effective bandwidth comparison for LIONS vs. Data Vortex.

III. FLEXIBLE BANDWIDTH NETWORKING TECHNOLOGIES FOR

INTER-DATACENTER COMMUNICATIONS

As discussed before, the inter-datacenter communication requires an elastic and flexible control and management of the limited spectrum resources in the optical backbone network. The recently proposed flexible bandwidth elastic optical networking technologies [5] have the capability of provisioning both subwavelength and superwavelength channels with arbitrary bandwidth according to the user demands. Since flexible bandwidth networking requires the ability to generate arbitrary bandwidth flexpaths, the physical layer must employ a flexible bandwidth technology scalable to terahertz bandwidths. Multicarrier solutions such as coherent wavelength division multiplexing (CoWDM) and orthogonal frequency division multiplexing (OFDM) have been proposed as possible implementations of flexible bandwidth networking. These solutions rely on the generation of many low speed subcarriers to form broadband data waveforms using lower speed modulators. CoWDM maintains orthogonality between closely packed subcarriers by individually modulating a set of coherent subcarrier tones, with each subcarrier's symbol rate equal to the subcarrier spacing. OFDM maintains the orthogonality between subcarriers using inverse Fourier transform at the transmitter and Fourier transform at the receiver. Both CoWDM and OFDM systems can change the modulation format of individual subcarriers, but lack the ability for arbitrary control over subcarrier symbol rate and spacing with a single physical architecture.

A more general method for flexible broadband waveform generation is called dynamic optical arbitrary waveform generation (OAWG) [24-26]. It can generate data waveforms in both single carrier modulation formats and multicarrier modulation formats such as CoWDM and OFDM. Also, in contrast to multi-carrier systems, OAWG utilizes the parallel synthesis and coherent combination of many lower bandwidth spectral slices to create broadband waveforms [27, 28], hence the spectral slice bandwidth is not related to the subcarrier

bandwidth of generated waveforms. This removes any restrictions on the subcarrier bandwidth and its modulation format. Further, the parallel nature of OAWG enables bandwidth scalability (>1THz) without increasing the bandwidth requirement on the supporting electronics.

As Figure 7 illustrates, dynamic OAWG begins with a coherent optical frequency comb (OFC), which is spectrally demultiplexed with narrow passbands placing each comb line at a separate spatial location. A set of in-phase and quadrature-phase modulators (I/Q modulators) each with a bandwidth of ΔfG apply temporal I/Q modulations to broaden the comb lines to create the spectral slices. Coherently combining the spectral slices using a gapless spectral multiplexer with broad overlapping passbands ensures a continuous bandwidth output waveform. At the receiver side, a complementary technology called optical arbitrary waveform measurement (OAWM) is used to detect the waveform. It divides the broadband continuous waveform into many spectral slices for parallel measurement using independent digital coherent receivers [29].

IV. SPECTRAL DEFRAGMENTATION IN FLEXIBLE BANDWIDTH

OPTICAL NETWORKS

Spectral fragmentation inevitably occurs in flexible bandwidth networks due to the dynamic allocation of spectrum resources for new connections and releases of those for expired connections [1, 9, 10]. Since spectral fragments are neither contiguous in the frequency domain nor aligned along the links, it is difficult to utilize them or to avoid them from becoming stranded bandwidths. This can potentially cause high blocking probabilities and limit the maximum traffic volume the network can accommodate. Spectral defragmentation aims at consolidating these fragmented spectral resources and converting them into usable bandwidths. The application can be classified into two categories: static and dynamic on-demand defragmentation. Static defragmentation can take place via offline computation during network re-optimization. In contrast, dynamic on-demand defragmentation can take place when a connection may be blocked due to the fragments on its path. Compared

(a)

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Figure 3 (a) OAWG transmitter principle, and a schematic of the dynamic (b) OAWG and (c) OAWM techniques for bandwidth scalable generation and detection of arbitrary modulation format data transmissions. BPD: balances photodiodes, FQD: Four-quadrature detector.

Optical Arbitrary Waveform Generation Optical Arbitrary Waveform Measurement

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with the static case, operations of dynamic defragmentation should adapt to the demand of any specific connection requests. In this paper, we address the dynamic spectral defragmentation problem using wavelength conversion technologies, with both algorithm and proof-of-concept demonstration perspectives.

The goal of dynamic defragmentation is to consolidate the fragmented spectral resources and to create sufficient contiguous spectral bands for accommodating the incoming connection requests. Since defragmentation involves wavelength conversion operations on some existing connections, the defragmentation algorithm should also attempt to minimize the number of these interrupted connections. Hence, the algorithm starts with searching for a spectral range (SR) of the new connection’s bandwidth along the path, with the goal that the number of contending connections in this SR is minimized. It then computes defragmentation operations and searches for alternative available SRs for each of these contending connections. In [30], we have successfully mapped the problem of finding the one-to-one, non-overlap matching between the interrupted connections and available SRs into finding the maximum independent set (MIS) in a constructed auxiliary graph. MIS is a well-known graph problem, which has many effective algorithms [31-34] in literature. A time complexity as low as O((log n)4) can be achieved by a parallel algorithm in [34], where n is the number of nodes in the graph.

There are several wavelength conversion technologies that can be used to implement spectral defragmentation [35]. Wave-mixing based techniques, such as four wave mixing (FWM) and difference frequency generation (DFG), are capable of converting coherent (amplitude/phase) information for multi-channels simultaneously and operating over THz bandwidth. A major challenge for spectral defragmentation is the implementation of wavelength conversions in a scalable fashion. The following demonstrates FWM based wavelength conversion as the means to perform broadband wavelength conversion for spectral defragmentation in flexible bandwidth networks. As Figure 8(a) indicates, network nodes equipped with FWM elements and wavelength selective switches (WSS) support spectral defragmentation functionality. With M parallel FWM convertors on a link, the node has the capability to convert M connections’ spectra simultaneously (defined as a defragmentation degree of M). This node architecture requires the spectral non-overlapping constraint on both the output link and the link between the M×1 and N×N WSS; on the other hand, the defragmentation operations for a particular output link are dispatched to the defragmentation modules of multiple input links, which potentially mitigates the burden of implementing many FWM components on a single output link in order to perform multiple wavelength conversion operations simultaneously.

In [36], we show the implementation of a flexible bandwidth wavelength cross connects (FB-WXC) with a spectral defragmentation degree of 1 using FWM based wavelength conversion. The proof-of-concept demo performs spectral defragmentation over 500 GHz by spectrally shifting a 200 GHz channel by 200 GHz using FWM technique. Figure 9 (a) and (b) show the spectrum before and after spectral defragmentation respectively. The defragmentation operation shifted channel B (200 GHz) to B′, in which case the stranded and fragmented bandwidth between channel A and B was moved by +200 GHz to be merged with other unused spectrum resources in the frequency domain to accommodate other big bandwidth requests, e.g. channel C in Figure 9 (b). The bit-error rate (BER) results for channel B, B′ and C which indicates successful wavelength conversion was plotted in Figure 9(c). The slight power penalty for the Even vs. Odd channels results from slightly uneven power levels entering the FWM process. Nonetheless, all channels achieved a BER less than the forward error correct (FEC) limit of 2×10-3 for the Reed-Solomon (255,239) FEC code for each measured subcarrier. Built upon this 1-degree defragmentation technique, the scalable FB-WXC with higher defragmentation degrees will further reduce blocking probability for new incoming requests and enable greater flexibility.

We also use simulation-based performance evaluation to verify the reduced blocking probability enabled by FB-WXCs with different level of defragmentation degrees. The simulation utilized the 14-node NSFNET topology, with the assumption that each fiber has 5 THz total spectrum and the bandwidth of each subcarrier is 12.5 GHz. The required bandwidth for connections is uniformly distributed from the smallest granularity (12.5 GHz) to 500 GHz. New connection

Fig. 3. (a) Pre-switch node architecture for FB-WXC with defragmentationfunctionality [1]. (b) Blocking probability using the pre-switch architecture.

Fig. 4. Spectrum (a) before (B) and (b) after (B’) defragmentation to enablethe addition of flexpath C. (c) BER performance of both even and offchannels for B, B’ and C.

Figure 4. (a) Pre-switch node architecture for FB-WXC with defragmentation functionality. (b) Blocking probability using the pre-switch architecture.

Figure 5. Spectrum (a) before (B) and (b) after (B’) defragmentation to enablthe addition of flexpath C. (c) BER performance of both even and off channelfor B, B’ and C.

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requests arrive according to a Poisson process at a rate of λ, and their holding time conforms to a negative exponential distribution with an average holding time of 730 time units in the simulation. Figure 8(b) shows a significant reduction in network blocking probability. In particular, with only one FWM element on one link, the blocking probability decreases by about 50%.

V. CONCLUSION

This paper discusses the challenges and prospects of intra- and inter-datacenter networks. The AWGR based LION switch can help realize a flat topology intra-datacenter network with > 1000 racks with a single switch achieving ultra low latency and high throughput. The comparative study between LION, electrical switching networks as the Flattened Butterfly, the Data Vortex Switch and the OSMOSIS switch showed the benefits of our LION switch in terms of available bandwidth per channel, end to end latency and throughput. For inter-datacenter networking, flexible bandwidth elastic optical networking technology can play a vital role in supporting spectrally-efficient and adaptive networking necessary for future data centers. The OAWG/OAWM transceivers, together with defragmentation technologies and control plane algorithms allow optimized spectrum utilization while minimizing disruptions as demonstrated and evaluated in this paper.

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