Mobile Internet Quality Estimation using Self-Tuning Kernel Regression

Modeling and estimation for spatial data are ubiquitous in real life, frequently appearing in weather forecasting, pollution detection, and agriculture. Spatial data analysis often involves processing datasets of enormous scale. In this work, we focus on large-scale internet-quality open datasets from Ookla. We look into estimating mobile (cellular) internet quality at the scale of a state in the United States. In particular, we aim to conduct estimation based on highly {\it imbalanced} data: Most of the samples are concentrated in limited areas, while very few are available in the rest, posing significant challenges to modeling efforts. We propose a new adaptive kernel regression approach that employs self-tuning kernels to alleviate the adverse effects of data imbalance in this problem. Through comparative experimentation on two distinct mobile network measurement datasets, we demonstrate that the proposed self-tuning kernel regression method produces more accurate predictions, with the potential to be applied in other applications

Using data science as a community advocacy tool to promote equity in urban renewal programs: An analysis of Atlanta's Anti-Displacement Tax Fund

Cities across the United States are undergoing great transformation and urban growth. Data and data analysis has become an essential element of urban planning as cities use data to plan land use and development. One great challenge is to use the tools of data science to promote equity along with growth. The city of Atlanta is an example site of large-scale urban renewal that aims to engage in development without displacement. On the Westside of downtown Atlanta, the construction of the new Mercedes-Benz Stadium and the conversion of an underutilized rail-line into a multi-use trail may result in increased property values. In response to community residents' concerns and a commitment to development without displacement, the city and philanthropic partners announced an Anti-Displacement Tax Fund to subsidize future property tax increases of owner occupants for the next twenty years. To achieve greater transparency, accountability, and impact, residents expressed a desire for a tool that would help them determine eligibility and quantify this commitment. In support of this goal, we use machine learning techniques to analyze historical tax assessment and predict future tax assessments. We then apply eligibility estimates to our predictions to estimate the total cost for the first seven years of the program. These forecasts are also incorporated into an interactive tool for community residents to determine their eligibility for the fund and the expected increase in their home value over the next seven years.Comment: Presented at the Data For Good Exchange 201

Design and Analysis of Schedules for Virtual Network Migration

The Internet faces well-known challenges in realizing modifications to the core architecture. To help overcome these limitations, virtual networks run over physical networks and use Internet paths and protocols as essentially a link layer in the virtual network. Effective use of the underlying network requires intelligent placement of virtual networks so that underlying resources do not incur over-subscription. Additionally, because virtual networks may come and go over time, and underlying networks may experience their own dynamic changes, virtual networks may need to be migrated— re-mapped to the physical network during active operation— to maintain good performance. In this paper we consider the problem of scheduling the sequence of node moves that take a virtual network from an original placement to a new placement. We build on prior work that achieves migration of a single node with minimal disruption to develop a model for the migration cost and latency for a given network migration schedule. We then develop algorithms for determining a single-node-at-a-time sequence of moves to minimize migration cost, and further consider multiple node moves in parallel to minimize migration time and cost. Our algorithms are the first we are aware of to systematically address the virtual network migration scheduling problem

TANGO: Performance and Fault Management in Cellular Networks through Cooperation between Devices and Edge Computing Nodes

Cellular networks have become an essential part of our lives. With increasing demands on its available bandwidth, we are seeing failures and performance degradations for data and voice traffic on the rise. In this paper, we propose the view that fog computing, integrated in the edge components of cellular networks, can partially alleviate this situation. In our vision, some data gathering and data analytics capability will be developed at the edge of the cellular network and client devices and the network using this edge capability will coordinate to reduce failures and performance degradations. We also envisage proactive management of disruptions including prediction of impending events of interest (such as, congestion or call drop) and deployment of appropriate mitigation actions. We show that a simple streaming media pre-caching service built using such device-fog cooperation significantly expands the number of streaming video users that can be supported in a nominal cellular network of today

Bootstrapping in Gnutella: A Preliminary Measurement Study

To join an unstructured peer-to-peer network like Gnutella, peers have to execute a bootstrapping function in which they discover other on-line peers and connect to them. Until this bootstrapping step is complete, a peer cannot participate in file sharing activities. Once bootstrapping is complete, a peer’s experience is strongly influenced by the choice of neighbor peers resulting from the bootstrapping step. Despite its importance, there has been very little attention devoted to understanding the behavior of this bootstrapping function. In this paper, we study the bootstrapping process of a peer in the Gnutella network. This is a preliminary investigation, consisting of 1) an analysis and performance comparison of bootstrapping algorithms of four Gnutella servent implementations, 2) a measurement-based characterization of the global Gnutella Web Caching System (GWebCaches), a primary component of the current bootstrapping functions, and 3) a study of the behavior and experience of a single GWebCache that was setup locally and made part of the global caching infrastructure. Our study highlights the importance of understanding the performance of the bootstrapping function as an integral part of a peer-to-peer system. We find that 1) there is considerable variation among various servent implementations that correlates to their bootstrapping performance, 2) even though the GWebCache system is designed to operate as a truly distributed system in keeping with the peer-to-peer system philosophy, it actually operates more like a centralized infrastructure function, and 3) the GWebCache system is subject to misreporting of peer and cache availability due to stale data and absence of validity checks