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  1. Edge computing is an attractive architecture to efficiently provide compute resources to many applications that demand specific QoS requirements. The edge compute resources are in close geographical proximity to where the applications’ data originate from and/or are being supplied to, thus avoiding unnecessary back and forth data transmission with a data center far away. This paper describes a federated edge computing system in which compute resources at multiple edge sites are dynamically aggregated together to form distributed super-cloudlets and best respond to varying application-driven loads. In its simplest form a super-cloudlet consists of compute resources available at two edge computingmore »sites or cloudlets that are (temporarily) interconnected by dedicated optical circuits deployed to enable low-latency and high-rate data exchanges. A super-cloudlet architecture is experimentally demonstrated over the largest public OpenROADM optical network testbed up to date consisting of commercial equipment from six suppliers. The software defined networking (SDN) PROnet Orchestrator is upgraded to both concurrently manage the resources offered by the optical network equipment, compute nodes, and associated Ethernet switches and achieve three key functionalities of the proposed super-cloudlet architecture, i.e., service placement, auto-scaling, and offloading.« less
    Free, publicly-accessible full text available July 1, 2022
  2. Deployment of 5G requires increased data trans-mission capacity in the metro fiber network. Besides deploying new dark fiber operators are also looking into solutions that improve fiber spectrum utilization by means of high-order modulation formats, flexible grid, and subcarrier multiplexing (SCM) technologies. An important factor that limits fiber spectrum utilization in metro network is the penalty inflicted on the optical signals that are routed by wavelength selective switches (WSS). In this paper, an intelligent WSS filtering penalty estimator is proposed based on neural network. With the achieved accuracy of 0.34 dB of mean absolute error in estimating the optical signal-to-noisemore »ratio penalties caused by WSS filtering, the trained neural network is applied to estimate the fiber throughput gains that can be obtained by optimally selecting the signal symbol rate in a number of use cases.« less
  3. The exponential growth of IoT end devices creates the necessity for cost-effective solutions to further increase the capacity of IEEE802.15.4g-based wireless sensor networks (WSNs). For this reason, the authors present a wireless sensor network concentrator (WSNC) that integrates multiple collocated collectors, each of them hosting an independent WSN on a unique frequency channel. A load balancing algorithm is implemented at the WSNC to uniformly distribute the number of aggregated sensor nodes across the available collectors. The WSNC is implemented using a BeagleBone board acting as the Network Concentrator (NC) whereas collectors and sensor nodes realizing the WSNs are built usingmore »the TI CC13X0 LaunchPads. The system is assessed using a testbed consisting of one NC with up to four collocated collectors and fifty sensor nodes. The performance evaluation is carried out under race conditions in the WSNs to emulate high dense networks with different network sizes and channel gaps. The experimental results show that the multicollector system with load balancing proportionally scales the capacity of the network, increases the packet delivery ratio, and reduces the energy consumption of the IoT end devices.« less
  4. Key functionalities of NOP (Network Operations Platform) are demonstrated with the latest multi-vendor OpenROADM equipment. Using open source packages, the NOP inter-operates with TransportPCE and other controllers, bringing together information about topology, events, and metrics.