scispace - formally typeset
Search or ask a question
Author

Roberto Proietti

Bio: Roberto Proietti is an academic researcher from University of California, Davis. The author has contributed to research in topics: Optical switch & Wavelength-division multiplexing. The author has an hindex of 29, co-authored 192 publications receiving 3052 citations. Previous affiliations of Roberto Proietti include Sant'Anna School of Advanced Studies & University of California.


Papers
More filters
Proceedings ArticleDOI
25 Oct 2010
TL;DR: The architecture and performance studies of Datacenter Optical Switch designed for scalable and high-throughput interconnections within a data center are discussed and it is shown that even with 2 to 4 wavelengths, the performance of DOS is significantly better than an electrical switch network based on state-of-the-art flattened butterfly topology.
Abstract: This paper discusses the architecture and performance studies of Datacenter Optical Switch (DOS) designed for scalable and high-throughput interconnections within a data center. DOS exploits wavelength routing characteristics of a switch fabric based on an Arrayed Waveguide Grating Router (AWGR) that allows contention resolution in the wavelength domain. Simulation results indicate that DOS exhibits lower latency and higher throughput even at high input loads compared with electronic switches or previously proposed optical switch architectures such as OSMOSIS [4, 5] and Data Vortex [6, 7]. Such characteristics, together with very high port count on a single switch fabric make DOS attractive for data center applications where the traffic patterns are known to be bursty with high temporary peaks [13]. DOS exploits the unique characteristics of the AWGR fabric to reduce the delay and complexity of arbitration. We present a detailed analysis of DOS using a cycle-accurate network simulator. The results show that the latency of DOS is almost independent of the number of input ports and does not saturate even at very high (approx 90%) input load. Furthermore, we show that even with 2 to 4 wavelengths, the performance of DOS is significantly better than an electrical switch network based on state-of-the-art flattened butterfly topology.

240 citations

Journal ArticleDOI
TL;DR: This tutorial paper surveys the photonic switching hardware solutions in support of evolving optical networking solutions enabling capacity expansion based on the proposed approaches and presents the first cost comparisons, to the knowledge, of the different approaches in an effort to quantify such tradeoffs.
Abstract: As traffic volumes carried by optical networks continue to grow by tens of percent year over year, we are rapidly approaching the capacity limit of the conventional communication band within a single-mode fiber. New measures such as elastic optical networking, spectral extension to multi-bands, and spatial expansion to additional fiber overlays or new fiber types are all being considered as potential solutions, whether near term or far. In this tutorial paper, we survey the photonic switching hardware solutions in support of evolving optical networking solutions enabling capacity expansion based on the proposed approaches. We also suggest how reconfigurable add/drop multiplexing nodes will evolve under these scenarios and gauge their properties and relative cost scalings. We identify that the switching technologies continue to evolve and offer network operators the required flexibility in routing information channels in both the spectral and spatial domains. New wavelength-selective switch designs can now support greater resolution, increased functionality and packing density, as well as operation with multiple input and output ports. Various switching constraints can be applied, such as routing of complete spatial superchannels, in an effort to reduce the network cost and simplify the routing protocols and managed pathway count. However, such constraints also reduce the transport efficiency when the network is only partially loaded, and may incur fragmentation. System tradeoffs between switching granularity and implementation complexity and cost will have to be carefully considered for future high-capacity SDM–WDM optical networks. In this work, we present the first cost comparisons, to our knowledge, of the different approaches in an effort to quantify such tradeoffs.

191 citations

Journal ArticleDOI
TL;DR: This paper proposes an all-optical negative acknowledgement (AO-NACK) architecture in order to remove the need for loopback buffers in LIONS, and its different loopback buffering schemes are compared with the performance of the flattened butterfly electrical switching network.
Abstract: This paper discusses the architecture of an arrayed waveguide grating router (AWGR)-based low-latency interconnect optical network switch called LIONS, and its different loopback buffering schemes. A proof of concept is demonstrated with a 4 × 4 experimental testbed. A simulator was developed to model the LIONS architecture and was validated by comparing experimentally obtained statistics such as average end-to-end latency with the results produced by the simulator. Considering the complexity and cost in implementing loopback buffers in LIONS, we propose an all-optical negative acknowledgement (AO-NACK) architecture in order to remove the need for loopback buffers. Simulation results for LIONS with AO-NACK architecture and distributed loopback buffer architecture are compared with the performance of the flattened butterfly electrical switching network.

161 citations

Journal ArticleDOI
TL;DR: In this paper, a deep reinforcement learning framework for routing, modulation and spectrum assignment (RMSA) in elastic optical networks (EONs) is proposed, where deep neural networks (DNNs) are trained with experiences of dynamic lightpath provisioning.
Abstract: This paper proposes DeepRMSA, a deep reinforcement learning framework for routing, modulation and spectrum assignment (RMSA) in elastic optical networks (EONs). DeepRMSA learns the correct online RMSA policies by parameterizing the policies with deep neural networks (DNNs) that can sense complex EON states. The DNNs are trained with experiences of dynamic lightpath provisioning. We first modify the asynchronous advantage actor-critic algorithm and present an episode-based training mechanism for DeepRMSA, namely, DeepRMSA-EP. DeepRMSA-EP divides the dynamic provisioning process into multiple episodes (each containing the servicing of a fixed number of lightpath requests) and performs training by the end of each episode. The optimization target of DeepRMSA-EP at each step of servicing a request is to maximize the cumulative reward within the rest of the episode. Thus, we obviate the need for estimating the rewards related to unknown future states. To overcome the instability issue in the training of DeepRMSA-EP due to the oscillations of cumulative rewards, we further propose a window-based flexible training mechanism, i.e., DeepRMSA-FLX. DeepRMSA-FLX attempts to smooth out the oscillations by defining the optimization scope at each step as a sliding window, and ensuring that the cumulative rewards always include rewards from a fixed number of requests. Evaluations with the two sample topologies show that DeepRMSA-FLX can effectively stabilize the training while achieving blocking probability reductions of more than 20.3% and 14.3%, when compared with the baselines.

135 citations

Journal ArticleDOI
TL;DR: In this paper, all-optical ultra-fast and reconfigurable logic gates have been implemented exploiting simple and low-cost schemes based on nonlinear optical loop mirrors, and their regenerative properties have been demonstrated.
Abstract: All-optical ultra-fast and reconfigurable logic gates have been implemented exploiting simple and low-cost schemes based on nonlinear optical loop mirrors. Regenerative properties of the realised optical logic gates have been demonstrated.

103 citations


Cited by
More filters
Journal ArticleDOI

[...]

08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

33,785 citations

Journal ArticleDOI
TL;DR: The background and state-of-the-art of big data are reviewed, including enterprise management, Internet of Things, online social networks, medial applications, collective intelligence, and smart grid, as well as related technologies.
Abstract: In this paper, we review the background and state-of-the-art of big data. We first introduce the general background of big data and review related technologies, such as could computing, Internet of Things, data centers, and Hadoop. We then focus on the four phases of the value chain of big data, i.e., data generation, data acquisition, data storage, and data analysis. For each phase, we introduce the general background, discuss the technical challenges, and review the latest advances. We finally examine the several representative applications of big data, including enterprise management, Internet of Things, online social networks, medial applications, collective intelligence, and smart grid. These discussions aim to provide a comprehensive overview and big-picture to readers of this exciting area. This survey is concluded with a discussion of open problems and future directions.

2,303 citations

Journal ArticleDOI
TL;DR: The drivers, building blocks, architecture, and enabling technologies for a whole new elastic optical networking paradigm are described, as well as early standardization efforts.
Abstract: Optical networks are undergoing significant changes, fueled by the exponential growth of traffic due to multimedia services and by the increased uncertainty in predicting the sources of this traffic due to the ever changing models of content providers over the Internet. The change has already begun: simple on-off modulation of signals, which was adequate for bit rates up to 10 Gb/s, has given way to much more sophisticated modulation schemes for 100 Gb/s and beyond. The next bottleneck is the 10-year-old division of the optical spectrum into a fixed "wavelength grid," which will no longer work for 400 Gb/s and above, heralding the need for a more flexible grid. Once both transceivers and switches become flexible, a whole new elastic optical networking paradigm is born. In this article we describe the drivers, building blocks, architecture, and enabling technologies for this new paradigm, as well as early standardization efforts.

1,448 citations

Journal ArticleDOI
TL;DR: This paper presents a systematic framework to decompose big data systems into four sequential modules, namely data generation, data acquisition, data storage, and data analytics, and presents the prevalent Hadoop framework for addressing big data challenges.
Abstract: Recent technological advancements have led to a deluge of data from distinctive domains (e.g., health care and scientific sensors, user-generated data, Internet and financial companies, and supply chain systems) over the past two decades. The term big data was coined to capture the meaning of this emerging trend. In addition to its sheer volume, big data also exhibits other unique characteristics as compared with traditional data. For instance, big data is commonly unstructured and require more real-time analysis. This development calls for new system architectures for data acquisition, transmission, storage, and large-scale data processing mechanisms. In this paper, we present a literature survey and system tutorial for big data analytics platforms, aiming to provide an overall picture for nonexpert readers and instill a do-it-yourself spirit for advanced audiences to customize their own big-data solutions. First, we present the definition of big data and discuss big data challenges. Next, we present a systematic framework to decompose big data systems into four sequential modules, namely data generation, data acquisition, data storage, and data analytics. These four modules form a big data value chain. Following that, we present a detailed survey of numerous approaches and mechanisms from research and industry communities. In addition, we present the prevalent Hadoop framework for addressing big data challenges. Finally, we outline several evaluation benchmarks and potential research directions for big data systems.

1,002 citations