Jianwei Liu

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.

Using MPTCP subflow association control for heterogeneous wireless network optimization

Multipath TCP (MPTCP) was designed to increase the throughput and reliability of TCP, with specific motivation coming from scenarios including data center and cloud computing. The use of MPTCP has been recently explored to support heterogeneous wireless networks (HetNets) involving hosts that have multiple network interfaces. However, current solutions generally involve many simplifying assumptions. In this paper, we propose a new framework to collect scheduling information from various scheduling network entities and conduct optimization from a global view. The framework uses the existing or readily accessible MPTCP parameters. Under this framework, we introduce a centralized optimization algorithm to realize general proportional fairness of user throughput. Based on results from NS3 simulations, we provide evidence that the approach provides a low cost solution for improved performance from the perspectives of both applications and network operators.

ThinGs In a Fog: System Illustration with Connected Vehicles

This paper presents ThinGs In a Fog (TGIF)- a system designed to support interdisciplinary research that fall under the broad context of the Internet of Things. The framework is based on an Edge Computing system design that distributes application processing to system compute nodes leveraging geographic compute location diversity of a Cloud-to-the-edge to support machine-to- machine interactions that potentially have real- time constraints. To provide further insight, we focus on Connected Vehicle as an exemplar application domain. This paper provides a summary of work-to-date, including results from a small prototype of the system deployed at Clemson University. We illustrate the system be presenting work-to-date on the design, implementation and evaluation of a Queue Warning which is an application that has been studied thoroughly by the transportation community. This particular application is nicely suited for illustrating the additional benefits and complexities associated with implementing well understood applications in emerging distributed computing environments expected to be supported by the IoT.

Using cloud computing to support higher education in networks and systems

Virtual machine (VM) technology is widely used in higher education to support pedagogy and research. However, without a standard platform for managing VM technology, VM-based infrastructure is likely to be acquired and deployed in an ad-hoc manner. The virtual computing lab (VCL) system is an open source cloud computing platform developed specifically to support higher education. The main goal of VCL is to make available dedicated, custom compute environments to users. We built a small VCL cloud and trialed its use to support several networking courses offered in the School of Computing at Clemson University during the 2013/2014 academic year. In this paper, we summarise the trial and provide results and conclusions. We found that well provisioned PCs might support up to ten VMs however when subject to peak loads, the system is not able to support accurate and reproducible network experiments.