Changes between Version 3 and Version 4 of NICE2017/EveningDemoSession

10/02/17 22:11:35 (7 years ago)
Vic Thomas



  • NICE2017/EveningDemoSession

    v3 v4  
     113=== Demonstration of a GENI based application to support Virtual Reality based training activities in orthopedic surgery ===
     114This demonstration involves highlighting a distributed approach to training orthopedic medical residents using Virtual Reality (VR) based simulation environments; this application exploits the capabilities of the Global Environment for Network Innovation (GENI)'s national test bed infrastructure.  Our demonstration will show how expert surgeons in different hospitals can interact with medical trainees at other locations and teach them the fundamentals of orthopedic surgery. The high-definition multimedia streaming and haptic interfaces associated with the VR environment will enable trainees to remotely observe, participate and practice surgical techniques virtually from different locations (and also provides ‘on demand’ access to such medical educational and training resources).
     115The virtual environments will enable students to learn the appropriate way of performing the LISS plating process which is an orthopedic surgical process to treat fractures of the femur. We are working with Dr. Miguel Pirela-Cruz at the Texas Tech Health Sciences Center (TTHSC) in El Paso, Texas.
     118 * J. Cecil, Oklahoma State University
     119 * Avinash Gupta, Oklahoma State University
     120 * Parmesh Ramanathan, University of Wisconsin-Madison
     121 * M. Pirela-Cruz, Texas Tech University
     123=== Demonstration of a GENI based cyber physical test bed for advanced manufacturing  ===
     124The creation of an advanced GENI based cyber physical framework for advanced manufacturing by our lab researchers has been acknowledged as a milestone in global collaborative manufacturing. Our demonstration involves the design of a GENI based Next Generation framework to support distributed cyber and physical interactions involving software and manufacturing resources; these complex activities including design analysis, assembly planning, Virtual Reality based simulation and finally physical assembly. The advent of the Next Internet holds the promise of ushering in a new era in Information Centric engineering and digital manufacturing activities. Our manufacturing domain of interest is in the emerging domain of micro devices assembly, which involves the assembly of micron sized parts using cyber and physical resources.
     127 * J. Cecil, Oklahoma State University
     128 * Joseph Tyler, Oklahoma State University
     129 * Avinash Gupta, Oklahoma State University
     130 * Parmesh Ramanathan, University of Wisconsin-Madison
     133== Testbeds and Federation ==
     134=== Designing an Exploratory Testbed for Hyperprofile-based Computation Offloading  ===
     135Recent offloading frameworks have been proposed with the goal of improving management decisions for offloading energy- and latency-sensitive tasks from mobile devices to nearby edge servers. Many of these solutions, however, are only evaluated via simulations, which may not accurately model real network behavior. We recently proposed a unique solution to the offloading problem which incorporates concepts from Knowledge-Defined Networking (KDN) to make intelligent predictions about offloading costs based on historical network data. This solution, known as hyperprofile-based computation offloading, represents each server instance as a node in a multidimensional feature space known as the hyperprofile. Each dimension of the hyperprofile corresponds to a feature that was predicted using a pre-trained machine learning model. Nodes within the hyperprofile are selected for offloading based on their proximity to a user-specified objective coordinate (e.g. the origin). A shortcoming of these data-driven approaches is that they rely on a representative training dataset and are very sensitive to training error. We created a synthetic dataset using NS-3 simulations and a real-world dataset from experiments in a GENI testbed in order to determine the viability of using synthetic data for accurately predicting real-world quantities. The data transfer characteristics of each data modality can be modeled by measuring transmission times for various sizes of data under varying network conditions. An analysis of using synthetic data for training and real data for testing is presented, as well as a comparative study between the two modalities. A discussion of the potential trade-offs and improvements for each data modality is also presented.
     138 * Jon Patman, University of Missouri
     139 * Flavio Esposito, St. Louis University
     140 * Prasad Calyam, University of Missouri
     142=== A Viral Planet-scale Infrastructure ===
     143We will demonstrate  a PlanetLab-like system based on a modern, microservices-oriented platform.  It features  significant improvements over previous distributed systems infrastructures (PlanetLab, Seattle, and GENI): it is the first general-purpose system designed for rapid, viral expansion   and deployment.  In this demonstfration, we will add a node to the infrastructure in 15 minutes and deploy an application in 5,
     146 * Rick McGeer, US Ignite
     147 * Glenn Ricart, US Ignite
     149=== Elascale: Application Monitoring and Autoscaling as a Service in SAVI Testbed ===
     150Autoscalability is one of the crucial functionalities necessary for cloud software systems nowadays. Elascale strives to adjust both micro/macroservices' resources (using it's default autoscaling engine), with respect to workload and changes in the internal state of the whole application stack. We have implemented Elascale on SAVI Testbed: an instance will be leveraged to add auto-scalability to a generic IoT application. Furthermore, all of the components of Elascale are deployed as containers.
     151In this demo, we showcase the deployment of the Elascale autoscaling system along with a simple, generic IoT application in SAVI Testbed. First, we will deploy the application that contains three main components: virtual sensors (data generators), stream processor (performs data processing) and database (storage). Next, we will deploy Elascale autoscaling engine to monitor the application. For our test, we scale the virtual sensors in order to stress the stream processor. Elascale will then automatically scale the stream processor to handle the workload. All of the information will be shown on a User Interface and on Kibana dashboard.
     154 * Rajsimman Ravichandiran, University of Toronto
     155 * Hamzeh Khazaei, University of Toronto
     156 * Thomas Lin, University of Toronto
     157 * Hadi Bannazadeh, University of Toronto
     158 * Alberto Leon-Garcia, University of Toronto
    113161== Education, Tools ==