Ryan Izard

A Fast Cloud-Based Network Selection Scheme Using Coalition Formation Games in Vehicular Networks

Leveraging multiple wireless technologies and radio access networks (RANs), vehicles on the move have the potential to get robust connectivity and continuous service. To support the demands of as many vehicles as possible, an efficient and fast network selection scheme is critically important to achieve high performance and efficiency. So far, prior works have primarily focused on design of optimization algorithms and utility functions for either user or network performance. Most such studies do not address the complexities involved in the acquisition of needed information and the execution of algorithms, making them unsuitable for practical implementations in vehicles. This paper proposes a fast cloud-based network selection scheme for vehicular networks. By leveraging a compute clouds abundant computing and data storage resources, vehicles can leverage wider scope network information for decision-making. Vehicles select best access networks through a coalition formation game approach. A one-iteration fast convergence algorithm is proposed to achieve the final state of coalition structure in the game. Through extensive simulation, the proposed network selection scheme was shown to balance system throughput and fairness with a built-in utility division rule of the framework. The algorithm efficiency showed eightfold enhancement over a conventional coalition formation algorithm. Such features validate the potential of implementation in practice.

An OpenFlow Testbed for the Evaluation of Vertical Handover Decision Algorithms in Heterogeneous Wireless Networks

This paper details a framework that leverages Software Defined Networking (SDN) features to provide a testbed for evaluating handovers for IPv4 heterogeneous wireless networks. The framework is intended to be an extension to the Global Environment for Network Innovations (GENI) testbed, but the essence of the framework can be applied on any OpenFlow (OF) enabled network. Our goal is to enable researchers to evaluate vertical handover decision algorithms using GENI resources, open source software, and low cost commodity hardware. The framework eliminates the triangle routing problem experienced by other previous IPv4-compatible IP mobility solutions. This paper provides an overview of the testbed framework, implementation details for our installation using GENI WiMAX resources, and a discussion of future work.

GENI Cinema: An SDN-Assisted Scalable Live Video Streaming Service

This paper introduces GENI Cinema (GC), a system that provides a scalable live video streaming service based on dynamic traffic steering with software defined networking (SDN) and demand driven instantiation of video relay servers in NSF GENIs distributed cloud environments. While the service can be used to relay a multitude of video content, its initial objective is to support live video streaming of educational content such as lectures and seminars among university campuses. Users on any campus would bootstrap video upload or download via a public Web portal and, for scalability, have the video delivered seamlessly across the network over one or multiple paths selected and dynamically controlled by GC. The architecture aims to provide a framework for addressing several well-known limitations of video streaming in todays Internet, where little control is available for controlling forwarding paths of on demand live video streams. GC utilizes GENIs distributed cloud servers to host on-demand video servers/relays and its Open Flow SDN to achieve seamless video upload/download and optimization of forwarding paths in the network core. This paper presents the architecture and an early prototype of the basic GC framework, together with some initial performance measurement results.

The Widening Gulf between Genomics Data Generation and Consumption: A Practical Guide to Big Data Transfer Technology

In the last decade, high-throughput DNA sequencing has become a disruptive technology and pushed the life sciences into a distributed ecosystem of sequence data producers and consumers. Given the power of genomics and declining sequencing costs, biology is an emerging “Big Data” discipline that will soon enter the exabyte data range when all subdisciplines are combined. These datasets must be transferred across commercial and research networks in creative ways since sending data without thought can have serious consequences on data processing time frames. Thus, it is imperative that biologists, bioinformaticians, and information technology engineers recalibrate data processing paradigms to fit this emerging reality. This review attempts to provide a snapshot of Big Data transfer across networks, which is often overlooked by many biologists. Specifically, we discuss four key areas: 1) data transfer networks, protocols, and applications; 2) data transfer security including encryption, access, firewalls, and the Science DMZ; 3) data flow control with software-defined networking; and 4) data storage, staging, archiving and access. A primary intention of this article is to orient the biologist in key aspects of the data transfer process in order to frame their genomics-oriented needs to enterprise IT professionals.

HetSDN: Exploiting SDN for intelligent network usage in heterogeneous wireless networks

Mobile devices nowadays can find multiple wireless networks, such as WiFi, 4G/LTE and relay through devices. These networks have different characteristics in terms of coverage, data rate, and price. Meanwhile, mobile applications (and even different TCP/UDP connections) often have diverse and time-variant network needs. Thus, to better use all wireless network resources, it would be ideal to enable a TCP/UDP connection to 1) select the most appropriate network dynamically and 2) migrate between networks transparently. However, existing methods fail to provide both functions in a systematic and efficient way at the TCP/UDP connection level. In this paper, we adopt Software-Defined Networking (SDN) to realize such a feature. We use the features of SDN to realize intelligent network selection that is adaptive to time-variant application needs, network availability, and scheduling commands. To support transparent migration, an intelligent home agent (HA) is designed with the SDN to anchor packets from the mobile device. It can intelligently determine which wireless network a TCP/UDP connection is running over. Finally, our implementation demonstrates the effectiveness and efficiency of the proposed system.

Steroid OpenFlow service: A scalable, cloud-based data transfer solution

With the recent rise in cloud computing, applications are routinely accessing and interacting with data on remote resources. As data sizes become increasingly large, often combined with their locations being far from the applications, the well known impact of lower TCP throughput over large delay-bandwidth product paths becomes more significant to these applications. While myriads of solutions exist to alleviate the problem, they require specialized software at both the application host and the remote data server, making it hard to scale up to a large range of applications and execution environments. A software defined networking based solution called Steroid OpenFlow Service (SOS) has been proposed as a network service that transparently increases the throughput of data transfers across large networks. In this paper, the SOS architecture is refined to support data transfer at scale. In an OpenFlow-based cloud environment such as GENI, SOS can leverage the use of multiple agents to provide increased network throughput for many applications simultaneously. A cloud-based approach is particularly beneficial to applications in environments without access to high performance networks. This paper introduces the scalable SOS architecture and demonstrates its viability and scalability in GENIs distributed testbed.

OpenFlow-based live video streaming with GENI Cinema

The use of computer networks to relay streaming multimedia content is on the rise and is expected to continue to increase in the coming years. This paper discusses the use of GENI Cinema as a means to distribute streaming content across different networks at scale. Through the use of OpenFlow, GENI Cinema is able to gain control over the flow and routing of video streams in the network to efficiently and scalably distribute video content from video producers to video consumers. GENI Cinema accomplishes video “channel” switching with minimal perceived delay and no disruption in content to the producers or consumers. A prototype implementation of GENI Cinema on the distributed GENI testbed is discussed.

HPC enabled real-time remote processing of laparoscopic surgery

Laparoscopic surgery is a minimally invasive surgical technique. The benefit of small incisions has a disadvantage of limited visualization of subsurface tissues. Image-guided surgery (IGS) uses pre-operative and intra-operative images to map subsurface structures. One particular laparoscopic system is the daVinci-si robotic surgical system. The video streams generate approximately 360 megabytes of data per second. Real-time processing this large stream of data on a bedside PC, single or dual node setup, has become challenging and a high-performance computing (HPC) environment may not always be available at the point of care. To process this data on remote HPC clusters at the typical 30 frames per second rate, it is required that each 11.9 MB video frame be processed by a server and returned within 1/30th of a second. We have implement and compared performance of compression, segmentation and registration algorithms on Clemsons Palmetto supercomputer using dual NVIDIA K40 GPUs per node. Our computing framework will also enable reliability using replication of computation. We will securely transfer the files to remote HPC clusters utilizing an OpenFlow-based network service, Steroid OpenFlow Service (SOS) that can increase performance of large data transfers over long-distance and high bandwidth networks. As a result, utilizing high-speed OpenFlow- based network to access computing clusters with GPUs will improve surgical procedures by providing real-time medical image processing and laparoscopic data.

GREE 2014 OpenFlow Tutorial and Demo

Summary form only given. This tutorial is designed to provide insight into how OpenFlow can be used to conduct network experiments. At present, Clemson University is developing a seamless vertical handover testbed for use by future GENI experimenters. The design of the testbed utilizes OpenFlow and consists of two key components that enable a seamless handover: (1) the client-level and (2) the network-level. Each will be described in detail at the introduction for this tutorial, and you will be given the opportunity to explore a version of the client-level implementation. For the purpose of this tutorial, the client-level component has been ported to a virtual machine (VM) so all can participate without regard to the unique hardware capabilities of each participant.