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Distributed File Allocation with Consistency Constraints
 in Proceedings of the International Conference on Distributed Computing Systems
, 1992
"... We consider the resource allocation problem in distributed computing systems that have strict mutual consistency requirements. Our model incorporates the behavior of consistency control algorithms, which ensure that mutual consistency of replicated data is preserved even when communication links of ..."
Abstract

Cited by 12 (1 self)
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We consider the resource allocation problem in distributed computing systems that have strict mutual consistency requirements. Our model incorporates the behavior of consistency control algorithms, which ensure that mutual consistency of replicated data is preserved even when communication links of the computer network and/or computers on which the files reside fail. The problem of resource allocation in these networks is significant in terms of the efficiency of operations and the reliability of the network. The constrained resource allocation problem is formulated as a mixed nonlinear integer program. An efficient algorithm is proposed to solve this problem. The performance of the algorithm is evaluated in terms of the algorithm's accuracy, efficiency and execution times, using a representative problem set. 1 Introduction Consider a distributed computing system (DCS) that is made up of a set of sites (nodes) connected through communication links which transmit information from one s...
A shorter proof of Kanter’s Bessel function concentration bound. Preprint. Available at arXiv.math.PR/0603522 Nagaev, S.V
 Appl
, 2006
"... We give a shorter proof of Kanter’s (1976) sharp Bessel function bound for concentrations of sums of independent symmetric random vectors. We provide sharp upper bounds for the sum of modified Bessel functions I0(x)+I1(x), which might be of independent interest. Corollaries improve concentration or ..."
Abstract

Cited by 2 (0 self)
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We give a shorter proof of Kanter’s (1976) sharp Bessel function bound for concentrations of sums of independent symmetric random vectors. We provide sharp upper bounds for the sum of modified Bessel functions I0(x)+I1(x), which might be of independent interest. Corollaries improve concentration or smoothness bounds for sums of independent random variables due to Čekanavičius & Roos (2006), Roos (2005), Barbour & Xia (1999), and Le Cam (1986).