2 research outputs found
User Tolerance and Self-Regulation in Congestion Control
In response to poor quality of service (QoS), users self-regulate, i.e. they
immediately release bandwidth and abandon network. However, there are studies
that show users are willing to tolerate poor QoS for some time to evaluate if
network performance will improve before abandoning the network. In this paper,
we investigate how users willingness to wait for improved QoS may influence
network activities, such as network pricing, bandwidth allocation, network
revenue, and performance. We develop and employ a self-regulation model that
includes user evaluation of QoS before deciding to abandon or stay in the
network. This model considers these two factors: user tolerance of low QoS and
the price per unit a user is willing to pay. Our investigation uncovers a
double edged problem network may be populated with lower paying users, who are
also dissatisfied. These lower paying users drive the price higher than the
price produced by conventional solution for network congestion. This leads to
our proposal for a market informed congestion control scheme, where network
resolves congestion based on user profile that is defined by their ability to
pay and demand for bandwidth.Comment: 9 page
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How\u27s My Network - Incentives and Impediments of Home Network Measurements
Gathering meaningful information from Home Networking (HN) environments has presented researchers with measurement strategy challenges. A measurement platform is typically designed around the process of gathering data from a range of devices or usage statistics in a network that are specifically behind the HN firewall. HN studies require a fine balance between incentives and impediments to promote usage and minimize efforts for user participation with the focus on gathering robust datasets and results. In this dissertation we explore how to gather data from the HN Ecosystem (e.g. devices, apps, permissions, configurations) and feedback from HN users across a multitude of HN infrastructures, leveraging low impediment and low/high incentive methods to entice user participation. We look to understand the trade-offs of using a variety of approach types (e.g. Java Applet, Mobile app, survey) for data collections, user preferences, and how HN users react and make changes to the HN environment when presented with privacy/security concerns, norms of comparisons (e.g. comparisons to the local environment and to other HNs) and other HN results. We view that the HN Ecosystem is more than just “the network” as it also includes devices and apps within the HN. We have broken this dissertation down into the following three pillars of work to understand incentives and impediments of user participation and data collections. These pillars include: 1) preliminary work, as part of the How\u27s My Network (HMN) measurement platform, a deployed signed Java applet that provided a user-centered network measurement platform to minimize user impediments for data collection, 2) a HN user survey on preference, comfort, and usability of HNs to understand incentives, and 3) the creation and deployment of a multi-faceted How\u27s My Network Mobile app tool to gather and compare attributes and feedback with high incentives for user participation; as part of this flow we also include related approaches and background work. The HMN Java applet work demonstrated the viability of using a Web browser to obtain network performance data from HNs via a user-centric network measurement platform that minimizes impediments for user participation. The HMN HN survey work found that users prefer to leverage a Mobile app for HN data collections, and can be incentivized to participate in a HN study by providing attributes and characteristics of the HN Ecosystem. The HMN Mobile app was found to provide high incentives, with minimal impediments, for participation with focus on user Privacy and Security concerns. The HMN Mobile app work found that 84\% of users reported a change in perception of privacy and security, 32\% of users uninstalled apps, and 24\% revoked permissions in their HN. As a by-product of this work we found it was possible to gather sensitive information such as previously attached networks, installed apps and devices on the network. This information exposure to any installed app with minimal or no granted permissions is a potential privacy concern