Rumba : a Python framework for automating large-scale recursive internet experiments on GENI and FIRE+

It is not easy to design and run Convolutional Neural Networks (CNNs) due to: 1) finding the optimal number of filters (i.e., the width) at each layer is tricky, given an architecture; and 2) the computational intensity of CNNs impedes the deployment on computationally limited devices. Oracle Pruning is designed to remove the unimportant filters from a well-trained CNN, which estimates the filters’ importance by ablating them in turn and evaluating the model, thus delivers high accuracy but suffers from intolerable time complexity, and requires a given resulting width but cannot automatically find it. To address these problems, we propose Approximated Oracle Filter Pruning (AOFP), which keeps searching for the least important filters in a binary search manner, makes pruning attempts by masking out filters randomly, accumulates the resulting errors, and finetunes the model via a multi-path framework. As AOFP enables simultaneous pruning on multiple layers, we can prune an existing very deep CNN with acceptable time cost, negligible accuracy drop, and no heuristic knowledge, or re-design a model which exerts higher accuracy and faster inferenc

Pre-study on Network Function Virtualization at Communication Technology Lab: Emulator

Network Function Virtualization (NFV) is a network architecture framework that virtualizes functions that were traditionally tied to hardware and allows for higher flexibility of deployment of network services. It is growing in popularity as more research is being conducted in the field, and because of this it is beneficial to investigate its possible uses in academia. The objectives of this thesis are to research NFV as a whole to investigate its different aspects, as well as research different NFV emulators. Later, the aim is to design tests that could be run in a selected emulator to showcase NFV academically to potential students. Out of the three thoroughly investigated emulators, the MeDICINE emulator was selected based on several criteria, most notably the ease of installation. However, due to some major technical issues and time constraints, the emulator could not be run, meaning no tests could be executed. Six tests were designed with two different topologies to showcase as many aspects and functionalities of NFV as possible. These tests open up possibilities for future work in the area, where potential students could use these tests to investigate NFV further through running them and checking parameters, but also adapting the topologies to other emulators and comparing the process of running the tests in several different emulators.Network Function Virtualization (NFV) is a network architecture framework that virtualizes functions that were traditionally tied to hardware and allows for higher flexibility of deployment of network services. It is growing in popularity as more research is being conducted in the field, and because of this it is beneficial to investigate its possible uses in academia. The objectives of this thesis are to research NFV as a whole to investigate its different aspects, as well as research different NFV emulators. Later, the aim is to design tests that could be run in a selected emulator to showcase NFV academically to potential students. Out of the three thoroughly investigated emulators, the MeDICINE emulator was selected based on several criteria, most notably the ease of installation. However, due to some major technical issues and time constraints, the emulator could not be run, meaning no tests could be executed. Six tests were designed with two different topologies to showcase as many aspects and functionalities of NFV as possible. These tests open up possibilities for future work in the area, where potential students could use these tests to investigate NFV further through running them and checking parameters, but also adapting the topologies to other emulators and comparing the process of running the tests in several different emulators

A survey of general-purpose experiment management tools for distributed systems

International audienceIn the field of large-scale distributed systems, experimentation is particularly difficult. The studied systems are complex, often nondeterministic and unreliable, software is plagued with bugs, whereas the experiment workflows are unclear and hard to reproduce. These obstacles led many independent researchers to design tools to control their experiments, boost productivity and improve quality of scientific results. Despite much research in the domain of distributed systems experiment management, the current fragmentation of efforts asks for a general analysis. We therefore propose to build a framework to uncover missing functionality of these tools, enable meaningful comparisons be-tween them and find recommendations for future improvements and research. The contribution in this paper is twofold. First, we provide an extensive list of features offered by general-purpose experiment management tools dedicated to distributed systems research on real platforms. We then use it to assess existing solutions and compare them, outlining possible future paths for improvements

OROS: onlin operation and orchestration of collaborative robots using 5G

© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThe 5G mobile networks extend the capability for supporting collaborative robot operations in outdoor scenarios. However, the restricted battery life of robots still poses a major obstacle to their effective implementation and utilization in real scenarios. One of the most challenging situations is the execution of mission-critical tasks that require the use of various onboard sensors to perform simultaneous localization and mapping (SLAM) of unexplored environments. Given the time-sensitive nature of these tasks, completing them in the shortest possible time is of the highest importance. In this paper, we analyze the benefits of 5G-enabled collaborative robots by enhancing the intelligence of the robot operation through joint orchestration of Robot Operating System (ROS) and 5G resources for energysaving goals, addressing the problem from both offline and online manners. We propose OROS, a novel orchestration approach that minimizes mission-critical task completion times as well as overall energy consumption of 5G-connected robots by jointly optimizing robotic navigation and sensing together with infrastructure resources. We validate our 5G-enabled collaborative framework by means of Matlab/Simulink, ROS software and Gazebo simulator. Our results show an improvement between 3.65in exploration task by exploiting 5G orchestration features for battery savings when using 3 robots.Peer ReviewedPostprint (author's final draft