The effect of forgetting on the performance of a synchronizer

AbstractWe study variants of the α-synchronizer by Awerbuch (1985) within a distributed message passing system with probabilistic message loss. The purpose of a synchronizer is to maintain a virtual (lock-step) round structure, which simplifies the design of higher-level distributed algorithms. The underlying idea of an α-synchronizer is to let processes continuously exchange round numbers and to allow a process to proceed to the next round only after it has witnessed that all processes have already started the current round.In this work, we study the performance of several synchronizers in an environment with probabilistic message loss. In particular, we analyze how different strategies of forgetting affect the round durations. The synchronizer variants considered differ in the times when processes discard part of their accumulated knowledge during the execution. Possible applications can be found, e.g., in sensor fusion, where sensor data become outdated and thus invalid after a certain amount of time.For all synchronizer variants considered, we develop corresponding Markov chain models and quantify the performance degradation using both analytic approaches and Monte-Carlo simulations. Our results allow to explicitly calculate the asymptotic behavior of the round durations: While in systems with very reliable communication the effect of forgetting is negligible, the effect is more profound in systems with less reliable communication. Our study thus provides computationally efficient bounds on the performance of the (non-forgetting) α-synchronizer and allows to quantitatively assess the effect accumulated knowledge has on the performance

The Effect of Forgetting on the Performance of a Synchronizer

International audienceWe study variants of the α -synchronizer by Awerbuch (J. ACM, 1985) within a distributed message passing system with probabilistic message loss. The purpose of synchronizers is to maintain a virtual (discrete) round structure. Their idea essentially is to let processes continuously exchange round numbers and to allow a process to proceed to the next round only after it has witnessed that all processes have already started its own current round. In this work, we study how four different, naturally chosen, strategies of forgetting affect the performance of these synchronizers. The variants differ in the times when processes discard part of their accumulated knowledge during execution. Such actively forgetting synchronizers have applications, e.g., in sensor fusion where sensor data becomes outdated and thus invalid after a certain amount of time. We give analytical formulas to quantify the degradation of the synchronizers' performance in an environment with probabilistic message loss. In particular, the formulas allow to explicitly calculate the performance's asymptotic behavior. Interestingly, all considered synchronizer variants behave similarly in systems with low message loss, while one variant shows fundamentally different behavior from the remaining three in systems with high message loss. The theoretical results are backed up by Monte-Carlo simulations

Brief Announcement: The Degrading Effect of Forgetting on a Synchronizer

International audienceA strategy to increase an algorithm's robustness against internal memory corruption is to let processes actively discard part of their accumulated knowledge during execution. We study how different strategies of forgetting affect the performance of a synchronizer in an environment with probabilistic message loss

Synchronization for impulse radio ultra-wideband system

Master'sMASTER OF ENGINEERIN

Theory, Design, and Implementation of Landmark Promotion Cooperative Simultaneous Localization and Mapping

Simultaneous Localization and Mapping (SLAM) is a challenging problem in practice, the use of multiple robots and inexpensive sensors poses even more demands on the designer. Cooperative SLAM poses specific challenges in the areas of computational efficiency, software/network performance, and robustness to errors. New methods in image processing, recursive filtering, and SLAM have been developed to implement practical algorithms for cooperative SLAM on a set of inexpensive robots. The Consolidated Unscented Mixed Recursive Filter (CUMRF) is designed to handle non-linear systems with non-Gaussian noise. This is accomplished using the Unscented Transform combined with Gaussian Mixture Models. The Robust Kalman Filter is an extension of the Kalman Filter algorithm that improves the ability to remove erroneous observations using Principal Component Analysis (PCA) and the X84 outlier rejection rule. Forgetful SLAM is a local SLAM technique that runs in nearly constant time relative to the number of visible landmarks and improves poor performing sensors through sensor fusion and outlier rejection. Forgetful SLAM correlates all measured observations, but stops the state from growing over time. Hierarchical Active Ripple SLAM (HAR-SLAM) is a new SLAM architecture that breaks the traditional state space of SLAM into a chain of smaller state spaces, allowing multiple robots, multiple sensors, and multiple updates to occur in linear time with linear storage with respect to the number of robots, landmarks, and robots poses. This dissertation presents explicit methods for closing-the-loop, joining multiple robots, and active updates. Landmark Promotion SLAM is a hierarchy of new SLAM methods, using the Robust Kalman Filter, Forgetful SLAM, and HAR-SLAM. Practical aspects of SLAM are a focus of this dissertation. LK-SURF is a new image processing technique that combines Lucas-Kanade feature tracking with Speeded-Up Robust Features to perform spatial and temporal tracking. Typical stereo correspondence techniques fail at providing descriptors for features, or fail at temporal tracking. Several calibration and modeling techniques are also covered, including calibrating stereo cameras, aligning stereo cameras to an inertial system, and making neural net system models. These methods are important to improve the quality of the data and images acquired for the SLAM process

Design and Optimization of Mobile Cloud Computing Systems with Networked Virtual Platforms

A Mobile Cloud Computing (MCC) system is a cloud-based system that is accessed by the users through their own mobile devices. MCC systems are emerging as the product of two technology trends: 1) the migration of personal computing from desktop to mobile devices and 2) the growing integration of large-scale computing environments into cloud systems. Designers are developing a variety of new mobile cloud computing systems. Each of these systems is developed with different goals and under the influence of different design constraints, such as high network latency or limited energy supply. The current MCC systems rely heavily on Computation Offloading, which however incurs new problems such as scalability of the cloud, privacy concerns due to storing personal information on the cloud, and high energy consumption on the cloud data centers. In this dissertation, I address these problems by exploring different options in the distribution of computation across different computing nodes in MCC systems. My thesis is that "the use of design and simulation tools optimized for design space exploration of the MCC systems is the key to optimize the distribution of computation in MCC." For a quantitative analysis of mobile cloud computing systems through design space exploration, I have developed netShip, the first generation of an innovative design and simulation tool, that offers large scalability and heterogeneity support. With this tool system designers and software programmers can efficiently develop, optimize, and validate large-scale, heterogeneous MCC systems. I have enhanced netShip to support the development of ever-evolving MCC applications with a variety of emerging needs including the fast simulation of new devices, e.g., Internet-of-Things devices, and accelerators, e.g., mobile GPUs. Leveraging netShip, I developed three new MCC systems where I applied three variations of a new computation distributing technique, called Reverse Offloading. By more actively leveraging the computational power on mobile devices, the MCC systems can reduce the total execution times, the burden of concentrated computations on the cloud, and the privacy concerns about storing personal information available in the cloud. This approach also creates opportunities for new services by utilizing the information available on the mobile device instead of accessing the cloud. Throughout my research I have enabled the design optimization of mobile applications and cloud-computing platforms. In particular, my design tool for MCC systems becomes a vehicle to optimize not only the performance but also the energy dissipation, an aspect of critical importance for any computing system

Public Relations Practices of the Communications Services Department of Dallas Power & Light Company

This study presents detailed analyses of public relations practices of the Communications Services Department, Dallas (Texas) Power & Light Company. Information sources included interviews with company personnel, company publications, and other publications. Four chapters deal with unique problems with which the electric utility industry in the United States is confronted; history and development of the electric power industry in Dallas; history and development of Dallas Power & Light Company, and organizations, functions, and operations of Communications Services Department of Dallas Power & Light Company. The study finds much strength in the department, but recommends several minor writing and clerical changes in the department's practices. It recommends further scholarly examination of public relations activities in other electric utilities