Yingdong Lu, Jing-Sheng Song, et al.
IIE Transactions
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, Jing-Sheng Song, et al.
IIE Transactions
Arnab Bhattacharyya, Elena Grigorescu, et al.
SODA 2009
Yingdong Lu, Mayank Sharma, et al.
IBM J. Res. Dev
Mohsen Bayati, Balaji Prabhakar, et al.
INFOCOM 2007