Evolving Optical Networks for Latency-Sensitive Smart-Grid Communications via Optical Time Slice Switching (OTSS) Technologies

In this paper, we proposed a novel OTSS-assisted optical network architecture for smart-grid communication networks, which has unique requirements for low-latency connections. Illustrative results show that, OTSS can provide extremely better performance in latency and blocking probability than conventional flexi-grid optical networks.Comment: IEEE Photonics Society 1st Place Best Poster Award, on CLEO-PR/OECC/PGC 201

On QoS-assured degraded provisioning in service-differentiated multi-layer elastic optical networks

The emergence of new network applications is driving network operators to not only fulfill dynamic bandwidth requirements, but offer various grades of service. Degraded provisioning provides an effective solution to flexibly allocate resources in various dimensions to reduce blocking for differentiated demands when network congestion occurs. In this work, we investigate the novel problem of online degraded provisioning in service-differentiated multi-layer networks with optical elasticity. Quality of Service (QoS) is assured by service-holding-time prolongation and immediate access as soon as the service arrives without set-up delay. We decompose the problem into degraded routing and degraded resource allocation stages, and design polynomial-time algorithms with the enhanced multi-layer architecture to increase the network flexibility in temporal and spectral dimensions. Illustrative results verify that we can achieve significant reduction of network service failures, especially for requests with higher priorities. The results also indicate that degradation in optical layer can increase the network capacity, while the degradation in electric layer provides flexible time-bandwidth exchange.Comment: accepted by IEEE GLOBECOM 201

High hydrostatic pressure harnesses the biosynthesis of secondary metabolites via the regulation of polyketide synthesis genes of hadal sediment-derived fungi

Deep-sea fungi have evolved extreme environmental adaptation and possess huge biosynthetic potential of bioactive compounds. However, not much is known about the biosynthesis and regulation of secondary metabolites of deep-sea fungi under extreme environments. Here, we presented the isolation of 15 individual fungal strains from the sediments of the Mariana Trench, which were identified by internal transcribed spacer (ITS) sequence analysis as belonging to 8 different fungal species. High hydrostatic pressure (HHP) assays were performed to identify the piezo-tolerance of the hadal fungi. Among these fungi, Aspergillus sydowii SYX6 was selected as the representative due to the excellent tolerance of HHP and biosynthetic potential of antimicrobial compounds. Vegetative growth and sporulation of A. sydowii SYX6 were affected by HHP. Natural product analysis with different pressure conditions was also performed. Based on bioactivity-guided fractionation, diorcinol was purified and characterized as the bioactive compound, showing significant antimicrobial and antitumor activity. The core functional gene associated with the biosynthetic gene cluster (BGC) of diorcinol was identified in A. sydowii SYX6, named as AspksD. The expression of AspksD was apparently regulated by the HHP treatment, correlated with the regulation of diorcinol production. Based on the effect of the HHP tested here, high pressure affected the fungal development and metabolite production, as well as the expression level of biosynthetic genes which revealed the adaptive relationship between the metabolic pathway and the high-pressure environment at the molecular level

Netcast: Low-Power Edge Computing with WDM-defined Optical Neural Networks

This paper analyzes the performance and energy efficiency of Netcast, a recently proposed optical neural-network architecture designed for edge computing. Netcast performs deep neural network inference by dividing the computational task into two steps, which are split between the server and (edge) client: (1) the server employs a wavelength-multiplexed modulator array to encode the network's weights onto an optical signal in an analog time-frequency basis, and (2) the client obtains the desired matrix-vector product through modulation and time-integrated detection. The simultaneous use of wavelength multiplexing, broadband modulation, and integration detection allows large neural networks to be run at the client by effectively pushing the energy and memory requirements back to the server. The performance and energy efficiency are fundamentally limited by crosstalk and detector noise, respectively. We derive analytic expressions for these limits and perform numerical simulations to verify these bounds.Comment: 11 pages, 8 figures. Submitted to JSTQE OC2023 Special Issue (invited

Provisioning Short-Term Traffic Fluctuations in Elastic Optical Networks

Transient traffic spikes are becoming a crucial challenge for network operators from both user-experience and network-maintenance perspectives. Different from long-term traffic growth, the bursty nature of short-term traffic fluctuations makes it difficult to be provisioned effectively. Luckily, next-generation elastic optical networks (EONs) provide an economical way to deal with such short-term traffic fluctuations. In this paper, we go beyond conventional network reconfiguration approaches by proposing the novel lightpath-splitting scheme in EONs. In lightpath splitting, we introduce the concept of Split-Points to describe how lightpath splitting is performed. Lightpaths traversing multiple nodes in the optical layer can be split into shorter ones by SplitPoints to serve more traffic demands by raising signal modulation levels of lightpaths accordingly. We formulate the problem into a mathematical optimization model and linearize it into an integer linear program (ILP). We solve the optimization model on a small network instance and design scalable heuristic algorithms based on greedy and simulated annealing approaches. Numerical results show the tradeoff between throughput gain and negative impacts like traffic interruptions. Especially, by selecting SplitPoints wisely, operators can achieve almost twice as much throughput as conventional schemes without lightpath splitting

TopoOpt: Optimizing the Network Topology for Distributed DNN Training

We explore a novel approach for building DNN training clusters using commodity optical devices. Our proposal, called TopoOpt, co-optimizes the distributed training process across three dimensions: computation, communication, and network topology. TopoOpt uses a novel alternating optimization technique and a group theory-inspired algorithm to find the best network topology and routing plan, together with parallelization strategy, for distributed DNN training. To motivate our proposal, we measure the communication patterns of distributed DNN workloads at a large online service provider. Experiments with a 12-node prototype demonstrate the feasibility of TopoOpt. Simulations on real distributed training models show that, compared to similar-cost FatTree interconnects, TopoOpt reduces DNN training time by up to 3x