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Xin Liu, Illinois Institute of Technology, Dong Jin, Illinois Institute of Technology, Cheol Won Lee, National Research Institute, South Korea, Jong Cheol Moon, National Research Institute, South Korea.  2016.  ConVenus: Congestion Verification of Network Updates in Software-defined Networks. Winter Simulation Conference (WSC).

We present ConVenus, a system that performs rapid congestion verification of network updates in softwaredefined networks. ConVenus is a lightweight middleware between the SDN controller and network devices, and is capable to intercept flow updates from the controller and verify whether the amount of traffic in any links and switches exceeds the desired capacity. To enable online verification, ConVenus dynamically identifies the minimum set of flows and switches that are affected by each flow update, and creates a compact network model. ConVenus uses a four-phase simulation algorithm to quickly compute the throughput of every flow in the network model and report network congestion. The experimental results demonstrate that ConVenus manages to verify 90% of the updates in a network consisting of over 500 hosts and 80 switches within 5 milliseconds.

Yangfend Qu, Illinois Institute of Technology, Xin Liu, Illinois Institute of Technology, Dong Jin, Illinois Institute of Technology, Yuan Hong, Illinois Institute of Technology, Chen Chen, Argonne National Laboratory.  2018.  Enabling a Resilient and Self-healing PMU Infrastructure Using Centralized Network Control. 2018 ACM International Workshop on Security in Software Defined Networks & Network Function Virtualization.

Many of the emerging wide-area monitoring protection and control (WAMPAC) applications in modern electrical grids rely heavily on the availability and integrity of widespread phasor measurement unit (PMU) data. Therefore, it is critical to protect PMU networks against growing cyber-attacks and system faults. In this paper, we present a self-healing PMU network design that considers both power system observability and communication network characteristics. Our design utilizes centralized network control, such as the emerging software-defined networking (SDN) technology, to design resilient network self-healing algorithms against cyber-attacks. Upon detection of a cyber-attack, the PMU network can reconfigure itself to isolate compromised devices and re-route measurement
data with the goal of preserving the power system observability. We have developed a proof-of-concept system in a container-based network testbed using integer linear programming to solve a graphbased PMU system model.We also evaluate the system performance regarding the self-healing plan generation and installation using the IEEE 30-bus system.
 

Jiaqi Yan, Illinois Institute of Technology, Xin Liu, Illinois Institute of Technology, Dong Jin, Illinois Institute of Technology.  2017.  Simulation of a Software-Defined Network as One Big Switch. ACM SIGSIM Conference on Principles of Advanced Discrete Simulation (ACM SIGSIM PADS).

Software-defined networking (SDN) technology promises centralized and rapid network provisioning, holistic management, low operational cost, and improved network visibility. Researchers have developed multiple SDN simulation and emulation platforms to expedite the adoption of many emerging SDN-based applications to production systems. However, the scalability of those platforms is often limited by the underlying physical hardware resources, which inevitably affects the simulation delity in large-scale network settings. In this paper, we present a model abstraction technique that e ectively transforms the network devices in an SDN-based network to one virtualized switch model. While signi cantly reducing the model execution time and enabling the real-time simulation capability, our abstracted model also preserves the end-to-end forwarding behavior of the original network. To achieve this, we first classify packets with the same forwarding behavior into smaller and disjoint Equivalence Classes (ECes) by analyzing the OpenFlow rules installed on the SDN devices. We then create a graph model representing the forwarding behavior of each EC. By traversing those graphs, we nally construct the rules of the big-switch model to e ectively preserve the original network's end-to-end forwarding behavior. Experimental results demonstrate that the network forwarding logic equivalence is well preserved between the abstracted model and the original SDN network. The model abstraction process is fast, e.g., 3.15 seconds to transform a medium-scale tree network consisting of 53,260 rules. The big-switch model is able to speed up the simulation by 4.3 times in average and up to 6.69 times among our evaluation experiments.