Trajectory Aware Macro-cell Planning for Mobile Users

We design and evaluate algorithms for efficient user-mobility driven macro-cell planning in cellular networks. As cellular networks embrace heterogeneous technologies (including long range 3G/4G and short range WiFi, Femto-cells, etc.), most traffic generated by static users gets absorbed by the short-range technologies, thereby increasingly leaving mobile user traffic to macro-cells. To this end, we consider a novel approach that factors in the trajectories of mobile users as well as the impact of city geographies and their associated road networks for macro-cell planning. Given a budget k of base-stations that can be upgraded, our approach selects a deployment that impacts the most number of user trajectories. The generic formulation incorporates the notion of quality of service of a user trajectory as a parameter to allow different application-specific requirements, and operator choices.We show that the proposed trajectory utility maximization problem is NP-hard, and design multiple heuristics. We evaluate our algorithms with real and synthetic data sets emulating different city geographies to demonstrate their efficacy. For instance, with an upgrade budget k of 20%, our algorithms perform 3-8 times better in improving the user quality of service on trajectories in different city geographies when compared to greedy location-based base-station upgrades.Comment: Published in INFOCOM 201

Automating question generation from educational text

The use of question-based activities (QBAs) is wide-spread in education, traditionally forming an integral part of the learning and assessment process. In this paper, we design and evaluate an automated question generation tool for formative and summative assessment in schools. We present an expert survey of one hundred and four teachers, demonstrating the need for automated generation of QBAs, as a tool that can significantly reduce the workload of teachers and facilitate personalized learning experiences. Leveraging the recent advancements in generative AI, we then present a modular framework employing transformer based language models for automatic generation of multiple-choice questions (MCQs) from textual content. The presented solution, with distinct modules for question generation, correct answer prediction, and distractor formulation, enables us to evaluate different language models and generation techniques. Finally, we perform an extensive quantitative and qualitative evaluation, demonstrating trade-offs in the use of different techniques and models.Comment: Accepted to AI-2023 (Forty-third SGAI International Conference on Artificial Intelligence) as a long paper, link: http://www.bcs-sgai.org/ai202

Proceedings of the Sixth International Workshop on Web Caching and Content Distribution

OVERVIEW The International Web Content Caching and Distribution Workshop (WCW) is a premiere technical meeting for researchers and practitioners interested in all aspects of content caching, distribution and delivery on the Internet. The 2001 WCW meeting was held on the Boston University Campus. Building on the successes of the five previous WCW meetings, WCW01 featured a strong technical program and record participation from leading researchers and practitioners in the field. This report includes all the technical papers presented at WCW'01. Note: Proceedings of WCW'01 are published by Elsevier. Hardcopies of these proceedings can be purchased through the workshop organizers. As a service to the community, electronic copies of all WCW'01 papers are accessible through Technical Report BUCS‐TR‐2001‐017, available from the Boston University Computer Science Technical Report Archives at http://www.cs.bu.edu/techreps. [Ed.note: URL outdated. Use http://www.bu.edu/cs/research/technical-reports/ or http://hdl.handle.net/2144/1455 in this repository to access the reports.]Cisco Systems; InfoLibria; Measurement Factory Inc; Voler

TCP Nice: A Mechanism for Background Transfers

background transfers transfers of data that humans are not waiting for to improve availability, reliability, latency or consistency. However, given the rapid fluctuations of available network bandwidth and changing resource costs due to technology trends, hand tuning the aggressiveness of background transfers risks (1) complicating applications, (2) being too aggressive and interfering with other applications, and (3) being too timid and not gaining the benefits of background transfers. Our goal is for the operating system to manage network resources in order to provide a simple abstraction of near zero-cost background transfers. Our system, TCP Nice, can provably bound the interference inflicted by background flows on foreground flows in a restricted network model. And our microbenchmarks and case study applications suggest that in practice it interferes little with foreground flows, reaps a large fraction of spare network bandwidth, and simplifies application construction and deployment. For example, in our prefetching case study application, aggressive prefetching improves demand performance by a factor of three when Nice manages resources; but the same prefetching hurts demand performance by a factor of six under standard network congestion control

Abstract TCP Nice: A Mechanism for Background Transfers

Many distributed applications can make use of large background transfers £ transfers of data that humans are not waiting for £ to improve availability, reliability, latency or consistency. However, given the rapid fluctuations of available network bandwidth and changing resource costs due to technology trends, hand tuning the aggressiveness of background transfers risks (1) complicating applications, (2) being too aggressive and interfering with other applications, and (3) being too timid and not gaining the benefits of background transfers. Our goal is for the operating system to manage network resources in order to provide a simple abstraction of near zero-cost background transfers. Our system, TCP Nice, can provably bound the interference inflicted by background flows on foreground flows in a restricted network model. And our microbenchmarks and case study applications suggest that in practice it interferes little with foreground flows, reaps a large fraction of spare network bandwidth, and simplifies application construction and deployment. For example, in our prefetching case study application, aggressive prefetching improves demand performance by a factor of three when Nice manages resources; but the same prefetching hurts demand performance by a factor of six under standard network congestion control.

Accountable Resource Allocation in Broadband Wireless Networks

Varying channel quality between a user and a base station in cellular and broadband wireless access networks leads to varying channel resource usage per Kbps user throughput. In this paper, we present the position that channel variations that are induced by user activity should be explicitly separated from those induced by network deployment and other factors; the variations should be treated differently during resource allocation by MAC schedulers to be accountable to both users and network operators. For instance, while variations induced by user activity generally warrants proportional or slot-based fairness across users, networkinduced variations warrant throughput-based fairness. To enable such customization of fairness metrics on different groups of flows, we propose ARA, a novel accountable resource allocation framework that builds on wireless network virtualization technology. We demonstrate the efficacy of ARA through prototype evaluation on a WiMAX testbed, and present preliminary measurements on categorizing the variations into user-induced and network-induced variations