arXiv subjects

Xiaole Wu

Publications and source records attributed to Xiaole Wu.

4 recordsLinked to original sources

arXiv2022

A Closed Queueing Maintenance Network with Two Batch Policies

This paper discusses a maintenance network with failed items that can be removed, repaired, redistributed, and reused under two batch policies: one for removing the failed items from each base to a maintenance shop and the other for redistributing the repaired items from the maintenance shop to bases. This maintenance network can be considered a virtual closed queueing network, and the Markov system of each node is described as an elegant block-structured Markov process whose stationary probabilities can be computed by the RG-factorizations. The structure of this maintenance network is novel and interesting. To compute the closed queueing network, we set up a new nonlinear matrix equation to determine the relative arrival rates, in which the nonlinearity comes from two different groups of processes: the failure and removal processes and the repair and redistribution processes. This paper also extends a simple queueing system of a node to a more general block-structured Markov process which can be computed by the RG-factorizations. Based on this, the paper establishes a more general product-form solution for the closed queueing network and provides performance analysis of the maintenance network. Our method will open a new avenue for quantitative evaluation of more general maintenance networks.

cs.PF↗

arXiv2021

A New Theoretical Framework of Pyramid Markov Processes for Blockchain Selfish Mining

In this paper, we provide a new theoretical framework of pyramid Markov processes to solve some open and fundamental problems of blockchain selfish mining under a rigorous mathematical setting. We first describe a more general model of blockchain selfish mining with both a two-block leading competitive criterion and a new economic incentive mechanism. Then we establish a pyramid Markov process and show that it is irreducible and positive recurrent, and its stationary probability vector is matrix-geometric with an explicitly representable rate matrix. Also, we use the stationary probability vector to study the influence of many orphan blocks on the waste of computing resource. Next, we set up a pyramid Markov reward process to investigate the long-run average profits of the honest and dishonest mining pools, respectively. As a by-product, we build three approximative Markov processes and provide some new interesting interpretation on the Markov chain and the revenue analysis reported in the seminal work by Eyal and Sirer (2014). Note that the pyramid Markov (reward) processes can open up a new avenue in the study of blockchain selfish mining. Thus we hope that the methodology and results developed in this paper shed light on the blockchain selfish mining such that a series of promising research can be developed potentially.

cs.CR↗

arXiv2021

Two Basic Queueing Models of Service Platforms in Digital Sharing Economy

This paper describes two basic queueing models of service platforms in digital sharing economy by means of two different policies of platform matching information. We show that the two queueing models of service platforms can be expressed as the level-independent quasi birth-and-death (QBD) processes. Using the proposed QBD processes, we provide a detailed analysis for the two queueing models of service platforms, including the system stability, the average stationary numbers of seekers and of idle owners, the expected sojourn time of an arriving seeker, and the expected profits for both the service platform and each owner. Finally, numerical examples are employed to verify our theoretical results, and demonstrate how the performance measures of service platforms are influenced by some key system parameters. We believe that the methodology and results developed in this paper not only can be applied to develop a broad class of queuing models of service platforms, but also will open a series of promising innovative research on performance evaluation, optimal control and queueing-game of service platforms and digital sharing economy.

cs.PF↗

arXiv2021

Infections Forecasting and Intervention Effect Evaluation for COVID-19 via a Data-Driven Markov Process and Heterogeneous Simulation

The Coronavirus Disease 2019 (COVID-19) pandemic has caused tremendous amount of deaths and a devastating impact on the economic development all over the world. Thus, it is paramount to control its further transmission, for which purpose it is necessary to find the mechanism of its transmission process and evaluate the effect of different control strategies. To deal with these issues, we describe the transmission of COVID-19 as an explosive Markov process with four parameters. The state transitions of the proposed Markov process can clearly disclose the terrible explosion and complex heterogeneity of COVID-19. Based on this, we further propose a simulation approach with heterogeneous infections. Experimentations show that our approach can closely track the real transmission process of COVID-19, disclose its transmission mechanism, and forecast the transmission under different non-drug intervention strategies. More importantly, our approach can helpfully develop effective strategies for controlling COVID-19 and appropriately compare their control effect in different countries/cities.

stat.AP↗