Design and Analysis of Scheduling Algorithms for Optically Groomed Data Center Networks

We highlight the advantages of dynamic wavelength allocation and path rerouting in hybrid optical flow-switched data center networks compared with static electronic packet-switched data center networks. Current electronic packet-switched networks perform routing based on lookup tables and shortest path routing over high speed optical connections. However, load balancing and path rerouting are not designed in coordination with the optical data plane. In this work, we design and analyze the performance of load balancing and resource allocation in hybrid flow data center architectures. In the hybrid flow architecture, the transport layer and control plane are responsible for load balancing, congestion control and reliable data delivery in wavelength division multiplexed networks. The transport layer abstracts the underlying network and data link layers from user applications. Applications are unaware of whether flows are transmitted via the electronic data plane, optical data plane, or both, where the relevant metric for applications is transaction delay. We show that the data center architecture that minimizes delay in the presence of elephant flows and dynamic loads is the hybrid flow architecture with dynamic resource allocation.

The explosive growth of data center (DC) traffic imposes unprecedented challenges on the current electrical switch-based data center networks (DCNs) with the bottleneck of limited bandwidth and high latency. Benefitting from transparency to the data rate and format, optical switching with theoretically infinite bandwidth could overcome the bandwidth bottleneck of electrical switching DCNs. However, DCNs normally deploy multitenant applications with a variety of DC traffic. It is hard to reconfigure the optical interconnections in real-time to provide adaptable bandwidth to the traffic with heterogeneous characteristics. Moreover, to improve the bandwidth utilization, statistical multiplexing is generally deployed in optical DCNs to forward the traffic flow in a time slot, which requires the network time to be precisely synchronized to all network nodes. For fast optical switches with a nanosecond switching configuration time, overall end node times must be synchronized at subnanosecond magnitude. In this paper, we propose and experimentally investigate a reconfigurable and picosecond-synchronized optical DCN (ReSAW) based on an arrayed waveguide grating router (AWGR) and the White Rabbit (WR) protocol. A scheduler based on a distributed field-programmable gate array is implemented in the proposed ReSAW to realize flexible wavelength configuration by controlling the fast laser array based on semiconductor optical amplifiers (SOAs) according to time slot and traffic priority. Moreover, the WR protocol is implemented in optical DCNs for what we believe is the first time to synchronize the time of the distributed top of racks (ToRs). The experimental demonstration validates that ReSAW achieves an average end-to-end latency of 317.44 ns and a precise synchronized time with an average 386 ps skew. When the load is 0.4, the packet loss after ReSAW reconfiguration is less than 1.83 × 10 − 6 , and the network latency is less than 1.73 µs. Based on the experimental parameters and results, the OMNeT++ simulation model is built to further verify the reconfigurability and scalability of the ReSAW network. Results show that the packet loss rate and latency performance increase 8.24% and 12.47%, respectively, at a load of 0.6 as the ReSAW network scales from 2560 to 40,960 servers compared to before the reconfiguration.

Optical switching technology arises as the dominant technology for data center networks (DCNs), grace to its inherent capability to serve time-sensitive traffic with low power consumption. In this study, we introduce an optical DCN that groups the servers into computing clusters. An effective MAC algorithm is adopted for the communication within a cluster, based on wavelength and time division multiple access (WDMA and TDMA) techniques. The proposed MAC algorithm avoids the collisions over the channels and achieves high effectiveness, while it serves different traffic types. We investigate the proposed MAC protocol performance under diverse servers’ population per cluster to determine its scalability and performance limits. The simulation study proves that the proposed DCN architecture with the MAC protocol reaches high performance, i.e. high throughput, low delay, low dropping rate and almost 100% bandwidth exploitation, for diverse servers’ population. Finally, comparative performance and power consumption study is given.

Traditional wired data center networks (DCNs) suffer from cabling complexity, lack flexibility, and are limited by the speed of digital switches. In this paper, we alternatively develop a top-down traffic grooming (TG) approach to the design and provisioning of mission-critical optical wireless DCNs. While switches are modeled as hybrid optoelectronic cross-connects, links are modeled as wavelength division multiplexing capable free-space optic channels. Using the standard TG terminology, we formulate the optimal mixed-integer TG problem considering the virtual topology, flow conversation, connection topology, non-bifurcation, and capacity constraints. Thereafter, we develop a fast yet efficient sub-optimal solution, which grooms mice flows (MFs), mission-critical flows (CFs), and forward on predetermined rack-to-rack (R2R) lightpaths. On the other hand, elephant flows (EFs) are forwarded over dedicated server-to-server express lightpaths whose routes and capacity are dynamically determined based on the availability of wavelength and capacity. To prioritize the CFs, we consider low and high-priority queues and analyze the delay characteristics such as waiting times, maximum hop counts, and blocking probability. As a result of grooming, the sub-wavelength traffic and adjusting the wavelength capacities, numerical results show that the proposed solutions can achieve significant performance enhancement by utilizing the bandwidth more efficiently, completing the flows faster than delay sensitivity requirements, and avoiding the traffic congestion by treating EFs and MFs separately.

Passive Optical Data Centre Networks have been developed due to the performance limitations in current data centres to provide high performance within data centre networks. An AWGR based passive optical network data centre architecture is evaluated using a TDM/WDM multiple access technique to provision the flow of traffic among the network efficiently. A Mixed Integer Linear Programming model is developed to optimise resource allocation in the architecture. Using WDM-TDM as a multiple access technique helps in solving issues such as oversubscription and congestion by allowing servers to make simultaneous transmissions of packets in different time slots. The results show that the provisioning / allocation of resource within the architecture is improved with improvements of up to 75% in resource utilisation.

LightFDG is an integrated approach to flow detection (FD) and flow grooming (FG) in optical wireless data center networks (DCNs), which is interconnected via wavelength division multiplexing (WDM) based free-space optical (FSO) links. Since forwarding bandwidth-hungry elephant flows (EFs) and delay-sensitive mice flows (MFs) on the same path can cause severe performance degradation, the LightFDG optically grooms flows of each class into rack-to-rack (R2R) flows. Then, R2R-MF and R2R-EF flows are separately forwarded over lightpaths of separate MF and EF virtual topologies, respectively. Lightpaths are provisioned by jointly determining the capacity and route based on flows' arrival rate, size, and completion time request. To prevent EFs from congesting the MF lightpaths, high speed and accurate flow-detection mechanisms are also necessary for classifying EFs as soon as possible. Therefore, a fast-lightweight-and-accurate flow detection framework is developed by leveraging the transmission control protocol (TCP) behaviors. The proposed FD scheme has the flexibility of being implemented as in-network or centralized to classify flows of modifiable and unmodifiable hosts, respectively. Since the centralized scheme incurs considerable overhead, the processing and communication overhead is also mitigated by proposed techniques. Numerical results show that LightFDG outperforms traditional load balancers by about 3× for EFs and 10× for MFs. Along with the developed overhead mitigation methods, the centralized scheme is shown to provide up to 62× lower overhead with 100% accuracy and with about 224× higher detection speeds than the existing centralized solutions.

In this talk, optical switching technologies will be reviewed for possible deployments in data center networks. Recent research in intra/inter DCN, such as DCN architecture and experimental demonstrations, will be summarized. Due to its low power consumption, optical switching technology will be one of the key enabling technologies for future large-scale data center networks. In this talk, recent research in data center architectures is reviewed and discussed to explore the potential deployment of optical switching technology in optical data center networks. Optical switching technologies include time division multiplexing (TDM), wavelength division multiplexing (WDM) and space division multiplexing (SDM) will be discussed. In addition, some latest research in inter-DCN communication technologies is reported.

We present an optical wireless data center network based on photonic integrated SOA based multicast switch (MCS) and arrayed waveguide grating router. Results show lossless and WDM MCS operation with <0.5dB power penalty at 10 Gb/s.

The rapid increasing traffic in data centers (DCs) puts tremendous pressure to the present multi-tier network architectures and electrical switching techniques. Switching traffic in the optical domain featuring ultra-high bandwidth, therefore, has been intensively investigated to build the high capacity data center networks (DCNs). To handle the variable traffic pattern in DCs, the network reconfigurability with adaptable optical bandwidth allocation is of key importance to flexibly assign the optical bandwidth. To this end, we propose and experimentally evaluate a software-defined networking enabled reconfigurable optical DCN with dynamic bandwidth allocation in this work, based on novel optical top of racks exploiting a wavelength selective switch. Experimental assessments show that the proposed solution can automatically reallocate the optical bandwidth in real-time to adapt the dynamic traffic pattern. Compared with the conventional optical DCN with static bandwidth provision, the end-to-end latency performance of the reconfigurable scheme with adaptable bandwidth allocation improves of 58.3% and the average packet loss decreases one order of magnitude. Moreover, the reconfigurable optical DCN features deterministic latency performance, with much lower time variations of packets delivery completion. Based on the experimental parameters, the simulation platform is also built to validate the good scalability of the proposed reconfigurable DCN. Numerical results illustrate the negligible performance degradation (11%) as the network scales from 2560 to 40 960 servers.

We propose a new structure for multilevel pulse amplitude modulation (PAM-4) signal generation for on-chip optical interconnects and data center networks. Our proposed architecture use only one 3 × 3 multimode interference (MMI) Based ring resonator with two segmented phase shifters. Two segmented phase shifters applied the plasma dispersion effect in silicon waveguide are used in the feedback ring resonator waveguide. The structure provides a very steep slope compared with the conventional structure based on Mach Zehnder Interferometer (MZM). As a result, an extreme reduction of power consumption is achieved. Based on this structure, an extreme high bandwidth and compact footprint are also achieved. The device is designed and analyzed using the Beam Propagation Method (BPM) integrated with the Finite Difference-Time Difference (FDTD) simulations.KeywordsMultimode interference (MMI)Microring resonatorIntegrated opticsPAM-4High performance computing systemsData center networksOptical interconnects

Maintaining high-performance operation under dynamic and nonuniform network traffic has been a technical challenge in current data center networks (DCNs). With the aim to provide better quality of service (QoS) for diverse applications, this work presents a dynamic and adaptive DCN reconfiguration framework based on deep reinforcement learning (DCR2L). The proposed framework is integrated into the SDN control plane of the DCN, implementing real-time and automatic DCN reconfiguration. Performance of the DCR2L framework is experimentally demonstrated in our DCN lab, including 4 racks and 16 servers. Experimental results show a network latency improvement of 6.9% based on the DCR2L at an average network bandwidth of 2.3 Gb/s. Based on measured traffic and physical parameters in the experiment, performance of the DCR2L framework is numerically assessed with the realistic traffic of diverse QoS requirements for both electrical and optical DCNs and for different data center scales. Leaf–spine electrical and OPSquare optical networks are set up in an OMNeT++ simulator. For a data plane network consisting of 16 racks and 320 servers, results indicate that the DCR2L framework improves the network latency of up to 16.4% under leaf–spine and up to 24.6% under OPSquare for the overall traffic with respect to the classical heuristic method. For a DCN scale of 10,240 servers, the DCR2L framework provides up to 12.5% lower latency for the leaf–spine electrical network and up to 17.8% latency improvement for the OPSquare optical network.

Traffic prediction and utilization of past information are essential requirements for intelligent and efficient management of resources, especially in optical data center networks (ODCNs), which serve diverse applications. In this paper, we consider the problem of traffic aggregation in ODCNs by leveraging the predictable or exact knowledge of application-specific information and requirements, such as holding time, bandwidth, traffic history, and latency. As ODCNs serve diverse flows (e.g., long/ elephant and short/mice), we utilize machine learning (ML) for prediction of time-varying traffic and connection blocking inODCNs.Furthermore,withthe predictedmean service time, passed time is utilized to estimate the mean residual life (MRL) of an active flow (connection). The MRL information is used for dynamic traffic aggregation while allocating resources to a new connection request. Additionally, blocking rate is predicted for a future time interval based on the predicted traffic and past blocking information, which is used to trigger a spectrumreallocation process (also called defragmentation) to reduce spectrum fragmentation resulting from the dynamic connection setup and tearing-down scenarios. Simulation results showthatML-based prediction and initial setup times (history) of traffic flows can be used to further improve connection blocking and resource utilization in space-division multiplexed ODCNs.

We review an optical flat datacenter network (DCN) based on scalable optical switching system with port-count independent low latency. Generation/reconfiguration of virtual DCN by utilizing statistical multiplexing offered by the optical switching system is demonstrated.

Data center networks, using a reliable, high speed communication network, is a core component of cloud computing. Recently, optical networks using Space Division Multiplexing has been proposed as a solution to the speed bottleneck anticipated in data center networks. To our knowledge the new challenges of designing such communication systems have not been addressed yet. In this paper we have proposed an optimal approach to the problem of developing a path-protection scheme to handle communication requests in data center (DC) networks using elastic optical networking and space division multiplexing. We have formulated our problem as an Integer Linear Program (ILP). We have also proposed a heuristic that can handle problems of practical size. Our simulations explore important features of our approach.

As the infrastructure of cloud computing and big data, data centers have been deployed widely. Then, how to make full use of the computing and storage resources in data centers will be the focus. Data center networks are considered important solution for the problem above, which include intra-data center and inter-data center networks. Both of them will depend on the optical networking due to its advantages, such as high bandwidth, low latency, and low energy consumption. Data center interconnected by flexi-grid optical networks is a promising scenario to allocate spectral resources for applications in a dynamic, tunable and efficient control manner. Compared with inter-data center, optical interconnect in intra-data center networks is a more pressing need and promising scenario to accommodate these applications in a dynamic, flexible and efficient manner. OpenFlow based Software Defined Networking (SDN) is considered as a good technology, which is very suitable for data center networks. This paper mainly focuses on the data center optical networks based on software defined networking (SDN), which can control the heterogeneous networks with unified resource interface. Architecture and experimental demonstration of OpenFlow-based optical interconnects in intra-data center Networks and OpenFlow-based flexi-grid optical networks for inter-data center are presented in the paper respectively. Some future works are listed finally.