Structure-Aware Reliability Analysis of Large-Scale Linear Sensor Systems

A linear sensor system is a system in which the sensor measurements have a linear relationship to the source variables that cannot be measured directly. Linear sensor systems are widely deployed in advanced manufacturing processes, wireless transportation systems, electrical grid systems, and oil and gas pipeline systems to monitor and control various physical phenomena critical to the smooth function of such systems. The source variables capture these complex physical phenomena which are then estimated based on the sensor measurements. Two of the critical parameters to be considered while modeling any linear sensor system are the degree of redundancy and reliability. The degree of redundancy is the minimum number of sensor failures that a system can withstand without compromising the identifiability of any source variables. The reliability of a sensor system is a probabilistic evaluation of the ability of a system to tolerate sensor failures. Unfortunately, the existing approaches to compute the degree of redundancy and estimate the reliability are limited in scope due to their inability to solve problems in large-scale. In this research, we establish a new knowledge base for computing the degree of redundancy and estimating the reliability of large-scale linear sensor systems. We first introduce a heuristic convex optimization algorithm that uses techniques from compressed sensing to find highly reliable approximate values for the degree of redundancy. Due to the distributed nature of linear sensor systems often deployed in practical applications, many of these systems embed certain structures. In our second approach, we study these structural properties in detail utilizing matroid theory concepts of connectivity and duality and propose decomposition theorems to disintegrate the redundancy degree problem into subproblems over smaller subsystems. We solve these subproblems using mixed integer programming to obtain the degree of redundancy of the overall system. We further extend these decomposition theorems to help with dividing the reliability evaluation problem into smaller subproblems. Finally, we estimate the reliability of the linear sensor system by solving these subproblems employing mixed integer programming embedded within a recursive variance reduction framework, a technique commonly used in network reliability literature. We implement and test developed algorithms using a wide range of standard test instances that simulate real-life applications of linear sensor systems. Our computational studies prove that the proposed algorithms are significantly faster than the existing ones. Moreover, the variance of our reliability estimate is significantly lower than the previous estimates

Formal analysis techniques for gossiping protocols

We give a survey of formal verification techniques that can be used to corroborate existing experimental results for gossiping protocols in a rigorous manner. We present properties of interest for gossiping protocols and discuss how various formal evaluation techniques can be employed to predict them

Improving Performance of Iterative Methods by Lossy Checkponting

Iterative methods are commonly used approaches to solve large, sparse linear systems, which are fundamental operations for many modern scientific simulations. When the large-scale iterative methods are running with a large number of ranks in parallel, they have to checkpoint the dynamic variables periodically in case of unavoidable fail-stop errors, requiring fast I/O systems and large storage space. To this end, significantly reducing the checkpointing overhead is critical to improving the overall performance of iterative methods. Our contribution is fourfold. (1) We propose a novel lossy checkpointing scheme that can significantly improve the checkpointing performance of iterative methods by leveraging lossy compressors. (2) We formulate a lossy checkpointing performance model and derive theoretically an upper bound for the extra number of iterations caused by the distortion of data in lossy checkpoints, in order to guarantee the performance improvement under the lossy checkpointing scheme. (3) We analyze the impact of lossy checkpointing (i.e., extra number of iterations caused by lossy checkpointing files) for multiple types of iterative methods. (4)We evaluate the lossy checkpointing scheme with optimal checkpointing intervals on a high-performance computing environment with 2,048 cores, using a well-known scientific computation package PETSc and a state-of-the-art checkpoint/restart toolkit. Experiments show that our optimized lossy checkpointing scheme can significantly reduce the fault tolerance overhead for iterative methods by 23%~70% compared with traditional checkpointing and 20%~58% compared with lossless-compressed checkpointing, in the presence of system failures.Comment: 14 pages, 10 figures, HPDC'1

Extended Abstract: Analysis of 1000 Arbiter PUF based RFID Tags

In this extended abstract a large-scale analysis of 4- way Arbiter PUFs is performed with measurement results from 1000 RFID tags. Arbiter PUFs are one of the most important building blocks in PUF-based protocols and have been the subject of many papers. However, in the past often only software simulations or a limited number of test chips were available for analysis. Therefore, the goal of this work is to verify earlier findings in regard to the uniqueness and reliability of Arbiter PUFs by using a much larger measurement set. Furthermore, we used machine learning algorithms to approximate and compare the internal delay differences of the employed PUF. One of the main research questions in this paper is to examine if any “outliers” occurred, i.e., if some tags performed considerably different. This might for example happen due to some unusual manufacturing variations or faults. However, our findings are that for all of the analyzed tags the parameters fell within the range of a Gaussian distribution without significant outliers. Hence, our results are indeed in line with the results of prior work