Toward Synthesis of Network Updates

Updates to network configurations are notoriously difficult to implement correctly. Even if the old and new configurations are correct, the update process can introduce transient errors such as forwarding loops, dropped packets, and access control violations. The key factor that makes updates difficult to implement is that networks are distributed systems with hundreds or even thousands of nodes, but updates must be rolled out one node at a time. In networks today, the task of determining a correct sequence of updates is usually done manually -- a tedious and error-prone process for network operators. This paper presents a new tool for synthesizing network updates automatically. The tool generates efficient updates that are guaranteed to respect invariants specified by the operator. It works by navigating through the (restricted) space of possible solutions, learning from counterexamples to improve scalability and optimize performance. We have implemented our tool in OCaml, and conducted experiments showing that it scales to networks with a thousand switches and tens of switches updating.Comment: In Proceedings SYNT 2013, arXiv:1403.726

Improving performance of pedestrian positioning by using vehicular communication signals

Pedestrian-to-vehicle communications, where pedestrian devices transmit their position information to nearby vehicles to indicate their presence, help to reduce pedestrian accidents. Satellite-based systems are widely used for pedestrian positioning, but have much degraded performance in urban canyon, where satellite signals are often obstructed by roadside buildings. In this paper, we propose a pedestrian positioning method, which leverages vehicular communication signals and uses vehicles as anchors. The performance of pedestrian positioning is improved from three aspects: (i) Channel state information instead of RSSI is used to estimate pedestrian-vehicle distance with higher precision. (ii) Only signals with line-of-sight path are used, and the property of distance error is considered. (iii) Fast mobility of vehicles is used to get diverse measurements, and Kalman filter is applied to smooth positioning results. Extensive evaluations, via trace-based simulation, confirm that (i) Fixing rate of positions can be much improved. (ii) Horizontal positioning error can be greatly reduced, nearly by one order compared with off-the-shelf receivers, by almost half compared with RSSI-based method, and can be reduced further to about 80cm when vehicle transmission period is 100ms and Kalman filter is applied. Generally, positioning performance increases with the number of available vehicles and their transmission frequency

Embedded System Optimization of Radar Post-processing in an ARM CPU Core

Algorithms executed on the radar processor system contributes to a significant performance bottleneck of the overall radar system. One key performance concern is the latency in target detection when dealing with hard deadline systems. Research has shown software optimization as one major contributor to radar system performance improvements. This thesis aims at software optimizations using a manual and automatic approach and analyzing the results to make informed future decisions while working with an ARM processor system. In order to ascertain an optimized implementation, a question put forward was whether the algorithms on the ARM processor could work with a 6-antenna implementation without a decline in the performance. However, an answer would also help project how many additional algorithms can still be added without performance decline. The manual optimization was done based on the quantitative analysis of the software execution time. The manual optimization approach looked at the vectorization strategy using the NEON vector register on the ARM CPU to reimplement the initial Constant False Alarm Rate(CFAR) Detection algorithm. An additional optimization approach was eliminating redundant loops while going through the Range Gates and Doppler filters. In order to determine the best compiler for automatic code optimization for the radar algorithms on the ARM processor, the GCC and Clang compilers were used to compile the initial algorithms and the optimized implementation on the radar post-processing stage. Analysis of the optimization results showed that it is possible to run the radar post-processing algorithms on the ARM processor at the 6-antenna implementation without system load stress. In addition, the results show an excellent headroom margin based on the defined scenario. The result analysis further revealed that the effect of dynamic memory allocation could not be underrated in situations where performance is a significant concern. Additional statements from the result demonstrated that the GCC and Clang compiler has their strength and weaknesses when used in the compilation. One limiting factor to note on the optimization using the NEON register is the sample size’s effect on the optimization implementation. Although it fits into the test samples used based on the defined scenario, there might be varying results in varying window cell size situations that might not necessarily improve the time constraints