Yingdong Lu
Probab. Eng. Inf. Sci.
We consider fundamental properties of stochastic loss networks, seeking to improve on the so-called Erlang fixed-point approximation. We propose a family of mathematical approximations for estimating the stationary loss probabilities and show that they always converge exponentially fast, provide asymptotically exact results, and yield greater accuracy than the Erlang fixed-point approximation. We further derive structural properties of the inverse of the classical Erlang loss function that characterize the region of capacities that ensures a workload is served within a set of loss probabilities. We then exploit these results to efficiently solve a general class of stochastic optimization problems involving loss networks. Computational experiments investigate various issues of both theoretical and practical interest, and demonstrate the benefits of our approach.
Yingdong Lu
Probab. Eng. Inf. Sci.
David A. Goldberg, Dmitriy A. Katz, et al.
IFIP WG 7.3 Performance 2014
Yingdong Lu, Siva Theja Maguluri, et al.
IEEE TACON
Mohsen Bayati, Devavrat Shah, et al.
ISIT 2006