Time-Dependent Pricing for Bandwidth Slicing under Information Asymmetry and Price Discrimination

Due to the bursty nature of Internet traffic, network service providers (NSPs) are forced to expand their network capacity in order to meet the ever-increasing peak-time traffic demand, which is however costly and inefficient. How to shift the traffic demand from peak time to off-peak time is a challenging task for NSPs. In this paper, we study the implementation of time-dependent pricing (TDP) for bandwidth slicing in software-defined cellular networks under information asymmetry and price discrimination. Congestion prices indicating real-time congestion levels of different links are used as a signal to motivate delay-tolerant users to defer their traffic demands. We formulate the joint pricing and bandwidth demand optimization problem as a two-stage Stackelberg leader-follower game. Then, we investigate how to derive the optimal solutions under the scenarios of both complete and incomplete information. We also extend the results from the simplified case of a single congested link to the more complicated case of multiple congested links, where price discrimination is employed to dynamically adjust the price of each congested link in accordance with its real-time congestion level. Simulation results demonstrate that the proposed pricing scheme achieves superior performance in increasing the NSP's revenue and reducing the peak-to-average traffic ratio (PATR).This work was supported in part by the National Natural Science Foundation of China under Grant Number 61971189, the Science and Technology Project of State Grid Corporation of China under Grant Number SGSDDK00KJJS1900405, the Exploration Project of State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (North China Electric Power University) under Grant Number LAPS2019-12, the Fundamental Research Funds for the Central Universities under Grant Number 2020MS001, and the National Key R&D Program of China under Grant Number 2019YFB1704702. This article was presented in part at the International Wireless Communications and Mobile Computing Conference (IWCMC’18), Limassol, Cyprus, 2018. The associate editor coordinating the review of this article and approving it for publication was T. He. (Corresponding author: Bo Gu.) Zhenyu Zhou is with the State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources, School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 10220

Work from home connection: a cluster analysis based on the Internet service attributes towards subscribers profile

Purpose – The purpose of the study is to discover clusters or homogenous groups of work-from-home (WFH) Internet subscribers in the Philippines based on the attributes of speed, network quality, customer service, after-sales support, price, contract and value for money. Another objective is to determine if the formed clusters are related to the demographic profile. Design/methodology/approach – A total of 275 internet subscribers from Metro Manila were surveyed. K-means cluster analysis using the Hartigan-Wong algorithm was performed on the data to generate the clusters. Findings – Results generated four significant clusters, which were named service value expecters, average expecters, low expecters and high expecters. Most of the subscribers are under the high expecters, followed by the service value expecters. The age and income of the subscribers are the profile that can affect the formulation of clusters in the Internet service industry. Those people in the younger age groups can be seen as more demanding, while older people tend to be content with the Internet service. Counter-intuitively, people with higher income seem to be more easily satisfied with Internet service features, while those people with lower income seem to be more demanding. Educational attainment and the number of household members do not have a direct effect on the formulation of clusters of Internet subscribers. Originality/value – This is the first study to use cluster analysis in identifying possible segments of WFH Internet subscribers based on Internet service attributes. Its value is the provision of market segments based on which can be the basis for formulating marketing strategies and policies for the telecommunications industry

The Role of Data Cap in Optimal Two-part Network Pricing

Internet services are traditionally priced at flat rates; however, many Internet service providers (ISPs) have recently shifted to-wards two-part tariffs where a data cap is imposed to restrain data demand from heavy users and usage over the data cap is charged based on a per-unit fee. Although the two-part tariff could gener-ally increase the revenue for ISPs and has been supported by the FCC chairman, the role of data cap and its revenue-optimal and welfare-optimal pricing structures are not well understood. In this paper, we study the impact of data cap on the optimal two-part pricing schemes for congestion-prone service markets, e.g., broadband or cloud services. We model users ’ demand and prefer-ences over pricing and congestion alternatives and derive the mar-ket share and congestion of service providers under a market equi-librium. Based on the equilibrium model, we characterize the two-part structures of the revenue-optimal and welfare-optimal pricing schemes. Our results reveal that 1) the data cap provides a mecha-nism for ISPs to transition from flat-rate to pay-as-you-go type of schemes, 2) with growing data demand and network capacity, the revenue-optimal pricing moves towards usage-based schemes with diminishing data caps, and 3) the structure of the welfare-optimal tariff comprises lower fees and data cap than those of the revenue-optimal counterpart, suggesting that regulators might want to pro-mote usage-based pricing but regulate the per-unit fees. Our results could help providers design revenue-optimal pricing schemes and guide regulatory authorities to legislate desirable regulations

The Role of Data Cap in Optimal Two-Part Network Pricing

10.1109/TNET.2017.2750173IEEE-ACM TRANSACTIONS ON NETWORKING2563602-361