Decentralized computing

Abstract: We consider a network of devices, such as generators, fixed loads, deferrable loads, and storage devices, each with its owndynamic constraints and objective, connected by AC and DC lines. The problem is to minimize the total network objective subject tothe device and line constraints, over a given time horizon. This is a large optimization problem, with variables for consumptionor generation for each device, power flow for each line, and voltage phase angles at AC buses, in each time period. In this paperwe develop a decentralized method for solving this problem called proximal message passing. The method is iterative: At each step,each device exchanges simple messages with its neighbors in the network and then solves its own optimization problem, minimizing itsown objective function, augmented by a term determined by the messages it has received. We show that this message passing methodconverges to a solution when the device objective and constraints are convex. The method is completely decentralized, and needs noglobal coordination other than synchronizing iterations; the problems to be solved by each device can typically be solved extremelyefficiently and in parallel. The method is fast enough that even a serial implementation can solve substantial problems inreasonable time frames. We report results for several numerical experiments, demonstrating the method's speed and scaling,including the solution of a problem instance with over 10 million variables in under 50 minutes for a serial implementation;with decentralized computing, the solve time would be less than one second.

Abstract: This study empirically develops a taxonomy that has implications for matching information technology (IT) and organizational structures. The taxonomy of IT structure is based on the degree of centralization of computer processing, capability to support communications, and the ability to share resources. By using a multistep cluster analysis, both the membership and number of groups are derived from the responses of 313 firms. Four IT structures are identified: centralized (centralized processing, low communication, low sharing), decentralized (decentralized processing, low communication, low sharing), centralized cooperative (centralized processing, high communication, high sharing), and distributed cooperative computing (decentralized processing, high communication, high sharing). Centralized computing is related to functional organizational forms with low integration and centralized decision making. Decentralized computing is related to product organizational forms with decentralized decision making. Centralized cooperative computing is related to functional organizational forms with high integration. Distributed cooperative computing is related to both matrix and product organizational forms with high integration. The ability to identify and understand the implications of IT structure is of critical importance to both academic and management practitioners.

Abstract: Structural health monitoring (SHM) systems are implemented for structures (e.g., bridges, buildings) to monitor their operations and health status. Wireless sensor networks (WSNs) are becoming an enabling technology for SHM applications that are more prevalent and more easily deployable than traditional wired networks. However, SHM brings new challenges to WSNs: engineering-driven optimal deployment, a large volume of data, sophisticated computing, and so forth. In this paper, we address two important challenges: sensor deployment and decentralized computing. We propose a solution, to deploy wireless sensors at strategic locations to achieve the best estimates of structural health (e.g., damage) by following the widely used wired sensor system deployment approach from civil/structural engineering. We found that faults (caused by communication errors, unstable connectivity, sensor faults, etc.) in such a deployed WSN greatly affect the performance of SHM. To make the WSN resilient to the faults, we present an approach, called ${\tt FTSHM}$ (fault-tolerance in SHM), to repair the WSN and guarantee a specified degree of fault tolerance. ${\tt FTSHM}$ searches the repairing points in clusters in a distributed manner, and places a set of backup sensors at those points in such a way that still satisfies the engineering requirements. ${\tt FTSHM}$ also includes an SHM algorithm suitable for decentralized computing in the energy-constrained WSN, with the objective of guaranteeing that the WSN for SHM remains connected in the event of a fault, thus prolonging the WSN lifetime under connectivity and data delivery constraints. We demonstrate the advantages of ${\tt FTSHM}$ through extensive simulations and real experimental settings on a physical structure.