Optimal services for content delivery based on business priority

Abstract

In a content-delivery system, connections are viewed as resources for sending files. However, the growing business needs of large-scale networks require an effective content-delivery service for transferring files. Since connections are sparse resources, prioritizing connections is essential for efficiently delivering urgent files and regular files based on various business priorities. This study presents a loss function as a performance index for a content-delivery service. The proposed loss function was applied to a sample content-delivery system to derive the average number of regular files in the retry group, the probability of failure to transfer a regular file in the first attempt, and the probability of failure to transmit urgent files. Additionally, the loss was associated with the decreased number of reserve connections under regular hours and peak hours. The experimental results show that the proposed model finds the optimal number of reserve connections for sending high-and low-priority files, and a manager can increase the service rate to ensure that losses are tolerable when delivering urgent files. Finally, the relative probabilities of blocked urgent files and blocked regular files are used as an indicator of efficiency in reserving connections.

Retrial Queues with Unreliable Servers and Delayed Feedback

“…Note. The average number of busy (NBS av ) and failed servers (NFS av ) (see Formulas (24) and ( 25)) does not depend on the parameters η and σ. These facts are explained by the accepted assumption that η << λ, i.e., the impact of the indicated parameters on two performance measures is not essential.…”

Solving two-dimensional Markov chain model for call centers

“…Numerous approximation methods have been proposed to solve the computational problem associated with a large number of channels (exceeding 1,000) with heavy traffic in the telecommunication research literature (Abdrabou and Zhuang, 2011;Allon et al, 2013;Bandi and Bertsimas, 2012;Bicen et al, 2012;Halfin and Whitt, 1981;Nelson, 2012;Sriram and Whitt, 1986;Whitt, 1983 One such method is known as phase merging algorithm and was introduced by Melikov and Babayev (2006) and adopted by Choi, Melikov and Velibekov (Choi et al, 2008;Liang and Luh, 2013;Ponomarenko et al, 2010) to compute stationary probability. This method can be implemented in call centers.…”

“…Motivated by a research project involving call centers at a selected company in Taiwan (Kim et al, 2012;Liang et al, 2005Liang et al, , 2009, this study discusses an approximation method suitable for use when an exact solution is not attainable to calculation of the management cost of a call center that involves blocking probability and waiting time (Huang, 2010;Liang and Luh, 2013;Melikov and Babayev, 2006;Xu et al, 2002;Bright and Taylor,1995). A large-scale service sector regards uninterrupted customer service as a key operational target (Kim et al, 2012;Liang et al, 2005Liang et al, , 2009.…”

RQwDFB: analysis of retrial queues with delayed feedback

“…Recently hierarchical SMA to study 3D MC is developed in [13]. In last decade many researchers develop similar SMAs to study 2D MC in various areas of applications [7,9,[14][15][16]24]. In all papers, authors note high efficiency of SMA (in both sense of accuracy and complexity) in comparison with other numerical methods.…”