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

Designing, Building, and Modeling Maneuverable Applications within Shared Computing Resources

Extending the military principle of maneuver into war-fighting domain of cyberspace, academic and military researchers have produced many theoretical and strategic works, though few have focused on researching actual applications and systems that apply this principle. We present our research in designing, building and modeling maneuverable applications in order to gain the system advantages of resource provisioning, application optimization, and cybersecurity improvement. We have coined the phrase “Maneuverable Applications” to be defined as distributed and parallel application that take advantage of the modification, relocation, addition or removal of computing resources, giving the perception of movement. Our work with maneuverable applications has been within shared computing resources, such as the Clemson University Palmetto cluster, where multiple users share access and time to a collection of inter-networked computers and servers. In this dissertation, we describe our implementation and analytic modeling of environments and systems to maneuver computational nodes, network capabilities, and security enhancements for overcoming challenges to a cyberspace platform. Specifically we describe our work to create a system to provision a big data computational resource within academic environments. We also present a computing testbed built to allow researchers to study network optimizations of data centers. We discuss our Petri Net model of an adaptable system, which increases its cybersecurity posture in the face of varying levels of threat from malicious actors. Lastly, we present work and investigation into integrating these technologies into a prototype resource manager for maneuverable applications and validating our model using this implementation

SD-MCAN: A Software-Defined Solution for IP Mobility in Campus Area Networks

Campus Area Networks (CANs) are a subset of enterprise networks, comprised of a network core connecting multiple Local Area Networks (LANs) across a college campus. Traditionally, hosts connect to the CAN via a single point of attachment; however, the past decade has seen the employment of mobile computing rise dramatically. Mobile devices must obtain new Internet Protocol (IP) addresses at each LAN as they migrate, wasting address space and disrupting host services. To prevent these issues, modern CANs should support IP mobility: allowing devices to keep a single IP address as they migrate between LANs with low-latency handoffs. Traditional approaches to mobility may be difficult to deploy and often lead to inefficient routing, but Software-Defined Networking (SDN) provides an intriguing alternative. This thesis identifies necessary requirements for a software-defined IP mobility system and then proposes one such system, the Software-Defined Mobile Campus Area Network (SD-MCAN) architecture. SD-MCAN employs an OpenFlow-based hybrid, label-switched routing scheme to efficiently route traffic flows between mobile hosts on the CAN. The proposed architecture is then implemented as an application on the existing POX controller and evaluated on virtual and hardware testbeds. Experimental results show that SD-MCAN can process handoffs with less than 90 ms latency, suggesting that the system can support data-intensive services on mobile host devices. Finally, the POX prototype is open-sourced to aid in future research

Demo abstract: a demonstration of automatic configuration of OpenFlow in wireless ad hoc networks

Using OpenFlow, a network can be controlled from one or more servers called controllers. In the demonstration, we show automatic configuration of OpenFlow in a wireless ad hoc network, deployed on a portable testbed, using MININET-WiFi (an emulator for software defined wireless networks). Automatic configuration is shown using a GUI (Graphical User Interface) which shows wireless nodes discovered by the controller. In addition, a video clip is streamed from one node to another and displayed in real time. The demonstration includes automatic configuration in the scenarios in which nodes move from one location to another

mMTC Deployment over Sliceable Infrastructure: The Megasense Scenario

Massive Machine Type Communication (mMTC) has long been identified as a major vertical sector and enabler of the industry 4.0 technological evolution that will seamlessly ease the dynamics of machine-to-machine communications while leveraging 5G technology. To advance this concept, we have developed and tested an mMTC network slice called Megasense. Megasense is a complete framework that consists of multiple software modules, which is used for collecting and analyzing air pollution data that emanates from a massive amount of air pollution sensors. Taking advantage of 5G networks, Megasense will significantly benefit from an underlying communication network that is traditionally elastic and can accommodate the on-demand changes in requirements of such a use case. As a result, deploying the sensor nodes over a sliceable 5G system is deemed the most appropriate in satisfying the resource requirements of such a use case scenario. In this light, in order to verify how 5G-ready our Megasense solution is, we deployed it over a network slice that is totally composed of virtual resources. We have also evaluated the impact of the network slicing platform on Megasense in terms of bandwidth and resource utilization. We further tested the performances of the Megasense system and come up with different deployment recommendations based on which the Megasense system would function optimally.Peer reviewe