For many military and civilian large-scale, real-world systems of interest, data are first acquired asynchronously, i.e., at irregular intervals of time, at geographically-dispersed sites, processed utilizing decision...
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For many military and civilian large-scale, real-world systems of interest, data are first acquired asynchronously, i.e., at irregular intervals of time, at geographically-dispersed sites, processed utilizing decision-making algorithms, and the processed data then disseminated to other appropriate sites. The term real-world refers to systems under computer control that relate to everyday life and are beneficial to the society in the large. The traditional approach to such problems consists of designing a central entity which collects all data, executes a decision-making algorithm sequentially to yield the decisions, and propagates the decisions to the respective sites. Centralized derision-making algorithms are slow and highly vulnerable to natural and artificial catastrophes. Recent literature includes successful asynchronous, distributed, decision-making algorithm designs wherein the local decision making at every site replaces the centralized decision making to achieve faster response, higher reliability, and greater accuracy of the decisions. Two key issues include 1) the lack of an approach to synthesize asynchronous, distributed, decision-making algorithms, for any "globally" optimal. In truth, however, as the frequency of the sensor data given problem, and 2) the absence of a comparative analysis of the quality of their decisions. This paper proposes MFAD, a Mathematical Framework for asynchronousdistributed Systems, that permits the description of centralized decision-making algorithms and facilities the synthesis of distributed decision-making algorithms. MFAD is based on the Kohn-Nerode distributed hybrid control paradigm. It has been a belief that since the centralized control gathers every necessary data from all entities in the system and utilizes them to compute the decisions, the decisions may be increases and the environment gets larger, dynamic, and more complex, the decisions are called into question. In the distributed decision-making system, the
We study the communication complexity of asynchronous distributed algorithms. Such algorithms can generate excessively many messages in the worst case. Nevertheless, we show that, under certain probablistic assumption...
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We study the communication complexity of asynchronous distributed algorithms. Such algorithms can generate excessively many messages in the worst case. Nevertheless, we show that, under certain probablistic assumptions, the expected number of messages generated per time unit is bounded by a polynomial function of the number of processors under a very general model of distributed computation. Furthermore, for constant-degree processor graphs, the expected number of generated messages is only O(nT), where n is the number of processors and T is the running time. We conclude that (under our model) any asynchronous algorithm with good time complexity will also have good communication complexity, on the average.
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