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1. PRAGMA‐ENT: An International SDN testbed for cyberinfrastructure in the Pacific Rim

   https://doi.org/10.1002/cpe.4138  

   Ichikawa, Kohei ; U‐Chupala, Pongsakorn ; Huang, Che ; Nakasan, Chawanat ; Liu, Te‐Lung ; Chang, Jo‐Yu ; Ku, Li‐Chi ; Tsai, Whey‐Fone ; Haga, Jason ; Yamanaka, Hiroaki ; et al ( March 2017 , Concurrency and Computation: Practice and Experience)

   The Pacific Rim Application and Grid Middleware Assembly (PRAGMA) is an international community of researchers that actively collaborate to address problems and challenges of common interest in eScience. The PRAGMA Experimental Network Testbed (PRAGMA‐ENT) was established with the goal of constructing an international software‐defined network (SDN) testbed to offer the necessary networking support to the PRAGMA cyberinfrastructure. PRAGMA‐ENT is isolated, and PRAGMA researchers have complete freedom to access network resources to develop, experiment, and evaluate new ideas without the concerns of interfering with production networks.

   In the first phase, PRAGMA‐ENT focused on establishing an international L2 backbone. With support from the Florida Lambda Rail, Internet2, PacificWave, Japan Gigabit Network, and TaiWan Advanced Research and Education Network, PRAGMA‐ENT backbone connects openflow‐enabled switches at University of Florida, University of California, San Diego, Nara Institute of Science and Technology (Japan), Osaka University (Japan), National Institute of Advanced Industrial Science and Technology (Japan), and National Applied Research Laboratories (Taiwan).

   The second phase of PRAGMA‐ENT consisted of an evaluation of technologies for the control plane that enables multiple experiments (ie, OpenFlow controllers) to coexist. Preliminary experiments with FlowVisor revealed some limitations leading to the development of a new approach, called AutoVFlow.

   This paper describes our experience in the establishment of PRAGMA‐ENT backbone (with international L2 links), its current status, and plans for the control plane. Discussion of preliminary application ideas, including optimization of routing control; multipath routing control; extending the backbone using overlay network; and remote visualization are also discussed.

    

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2. Output Range Analysis for Deep Feedforward Neural Networks

   Dutta, Souradeep ; Jha, Susmit ; Sankaranarayanan, Sriram ; Tiwari, Ashish ( April 2018 , Proceedings of NASA Formal Methods Symposium (NFM))

   Given a neural network (NN) and a set of possible inputs to the network described by polyhedral constraints, we aim to compute a safe over-approximation of the set of possible output values. This operation is a fundamental primitive enabling the formal analysis of neural networks that are extensively used in a variety of machine learning tasks such as perception and control of autonomous systems. Increasingly, they are deployed in high-assurance applications, leading to a compelling use case for formal verification approaches. In this paper, we present an efficient range estimation algorithm that iterates between an expensive global combinatorial search using mixed-integer linear programming problems, and a relatively inexpensive local optimization that repeatedly seeks a local optimum of the function represented by the NN. We implement our approach and compare it with Reluplex, a recently proposed solver for deep neural networks. We demonstrate applications of our approach to computing flowpipes for neural network-based feedback controllers. We show that the use of local search in conjunction with mixed-integer linear programming solvers effectively reduces the combinatorial search over possible combinations of active neurons in the network by pruning away suboptimal nodes. 

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3. Output Range Analysis for Deep Feedforward Neural Networks

   https://doi.org/10.1007/978-3-319-77935-5_9  

   Dutta, Souradeep ; Jha, Susmit ; Sankaranarayanan, Sriram ; Tiwari, Ashish ( January 2018 , NASA Formal Methods)

   Given a neural network (NN) and a set of possible inputs to the network described by polyhedral constraints, we aim to compute a safe over-approximation of the set of possible output values. This operation is a fundamental primitive enabling the formal analysis of neural networks that are extensively used in a variety of machine learning tasks such as perception and control of autonomous systems. Increasingly, they are deployed in high-assurance applications, leading to a compelling use case for formal verification approaches. In this paper, we present an efficient range estimation algorithm that iterates between an expensive global combinatorial search using mixed-integer linear programming problems, and a relatively inexpensive local optimization that repeatedly seeks a local optimum of the function represented by the NN. We implement our approach and compare it with Reluplex, a recently proposed solver for deep neural networks. We demonstrate applications of our approach to computing flowpipes for neural network-based feedback controllers. We show that the use of local search in conjunction with mixed-integer linear programming solvers effectively reduces the combinatorial search over possible combinations of active neurons in the network by pruning away suboptimal nodes. 

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4. Dynamic Routing and Spectrum Assignment in Co-Existing Fixed/Flex-Grid Optical Networks

   https://doi.org/10.1109/ANTS.2018.8710140  

   Ahmed, Tanjila ; Rahman, Sabidur ; Tornatore, Massimo ; Yu, Xiaosong ; Kim, Kwangjoon ; Mukherjee, Biswanath ( December 2018 , 2018 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS))

   A traditional wavelength-division multiplexed (WDM) backbone network with its rigid features is unsuitable for emerging diverse and high bitrate (400 Gb/s, 1 Tb/s) traffic needs. Flexible solutions employ new technologies such as bandwidth-variable optical cross connects (BV-OXC) with liquid crystal (LCoS) wavelength-selective switches (WSS), sliceable bandwidth-variable transponders (SBVT), etc. in a flex-grid network. Flex-grid network operates on variable spectral granularities (e.g., 12.5 GHz), and higher modulation formats (quadrature amplitude modulation). However, a greenfield deployment of flex-grid technologies may not be practical, due to cost of technology and usability. This leads to a brown-field network where both fixed-grid and flex-grid technologies co-exist with seamless interoperability. Thus traditional traffic routing and resource allocation techniques need to evolve in a mixed-grid infrastructure. Our study considers the dynamic routing and spectrum assignment (RSA) problem in a fixed/flex-grid co-existing optical network. It provisions routes for dynamic, heterogeneous traffic, ensuring maximum spectrum utilization and minimum blocking. 

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5. Detecting Ephemeral Optical Events with OpTel

   Miao, Congcong ; Chen Minggang ; Gupta, Arpit ; Meng, Zili ; Chen, Jie ; He Zekun ; Luo, Xulong ; Wang, Jilong ; Yu, Heng ( January 2022 , 19th USENIX Symposium on Networked Systems Design and Implementation)

   Degradation or failure events in optical backbone networks affect the service level agreements for cloud services. It is critical to detect and troubleshoot these events promptly to minimize their impact. Existing telemetry systems rely on arcane tools (e.g., SNMP) and vendor-specific controllers to collect optical data, which affects both the flexibility and scale of these systems. As a result, they fail to collect the required data on time to detect and troubleshoot degradation or failure events in a timely fashion. This paper presents the design and implementation of OpTel, an optical telemetry system, that uses a centralized vendor-agnostic controller to collect optical data in a streaming fashion. More specifically, it offers flexible vendor-agnostic interfaces between the optical devices and the controller and offloads data-management tasks (e.g., creating a queryable database) from the devices to the controller. As a result, OpTel enables the collection of fine-grained optical telemetry data at the one-second granularity. It has been running in Tencent's optical backbone network for the past six months. The fine-grained data collection enables the detection of short-lived events (i.e., ephemeral events). Compared to existing telemetry systems, OpTel accurately detects 2x more optical events. It also enables troubleshooting of these optical events in a few seconds, which is orders of magnitude faster than the state-of-the-art. 

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