26 citations · 53 across the 18 of their papers we have counts for
18 papers
Pramana: A Composable, Domain-Specific Backend for Empirical Networking Research
Jaber Daneshamooz, Eugene Vuong, Alagappan Ramanathan +11
Networking research advances by turning hypotheses into empirical evidence, so accelerating it means reducing the lag between ideation (synthesizing a hypothesis) and generating th…
LBI: Parallel Scan Backpropagation via Latent Bounded Interfaces
Shaun Christopher Lee, Sangeetha Abdu Jyothi
Backpropagation is inherently sequential across depth, creating an -deep dependency chain that bottlenecks parallel training. While parallel-scan formulations theoretically r…
Airavat: An Agentic Framework for Internet Measurement
Alagappan Ramanathan, Eunju Kang, Dongsu Han +1
Internet measurement faces twin challenges: complex analyses require expert-level orchestration of tools, yet even syntactically correct implementations can have methodological fla…
FlexiCache: Leveraging Temporal Stability of Attention Heads for Efficient KV Cache Management
Nazmul Takbir, Hamidreza Alikhani, Nikil Dutt +1
Large Language Model (LLM) serving is increasingly constrained by the growing size of the key-value (KV) cache, which scales with both context length and generation length. Prior w…
AMAQ: Adaptive Mixed-bit Activation Quantization for Collaborative Parameter Efficient Fine-tuning
Yurun Song, Zhuoyi Yang, Ian G. Harris +1
Large Language Models (LLMs) are scaling rapidly, creating significant challenges for collaborative server client distributed training, particularly in terms of communication effic…
A Deep Dive into the Impact of Solar Storms on LEO Satellite Networks
Eunju Kang, Alagappan Ramanathan, Sangeetha Abdu Jyothi
Low Earth Orbit (LEO) satellite networks are an important part of the global communication infrastructure today. Despite ongoing efforts to improve their resilience, they remain vu…
CoopQ: Cooperative Game Inspired Layerwise Mixed Precision Quantization for LLMs
Junchen Zhao, Ali Derakhshan, Jayden Kana Hyman +3
Large Language Models (LLMs) promise impressive capabilities, yet their multi-billion-parameter scale makes on-device or low-resource deployment prohibitive. Mixed-precision quanti…
Leveraging Traceroute Inconsistencies to Improve IP Geolocation
Alagappan Ramanathan, Sangeetha Abdu Jyothi
Traceroutes and geolocation are two essential network measurement tools that aid applications such as network mapping, topology generation, censorship, and Internet path analysis.…
ShareLoRA: Parameter Efficient and Robust Large Language Model Fine-tuning via Shared Low-Rank Adaptation
Yurun Song, Junchen Zhao, Ian G. Harris +1
In this paper, we introduce \textbf{Share}d \textbf{Lo}w \textbf{R}ank \textbf{A}daptation (ShareLoRA), a Large Language Model (LLM) fine-tuning technique that balances parameter e…
An In-Depth Investigation of LEO Satellite Topology Design Parameters
Wenyi Zhang, Zihan Xu, Sangeetha Abdu Jyothi
Low Earth Orbit (LEO) satellite networks are rapidly gaining traction today. Although several real-world deployments exist, our preliminary analysis of LEO topology performance wit…
LinguaLinked: A Distributed Large Language Model Inference System for Mobile Devices
Junchen Zhao, Yurun Song, Simeng Liu +2
Deploying Large Language Models (LLMs) locally on mobile devices presents a significant challenge due to their extensive memory requirements. In this paper, we introduce LinguaLink…
Characterizing the Role of Power Grids in Internet Resilience
Sangeetha Abdu Jyothi
Among critical infrastructures, power grids and communication infrastructure are identified as uniquely critical since they enable the operation of all other sectors. Due to their…
CrystalBox: Future-Based Explanations for Input-Driven Deep RL Systems
Sagar Patel, Sangeetha Abdu Jyothi, Nina Narodytska
We present CrystalBox, a novel, model-agnostic, posthoc explainability framework for Deep Reinforcement Learning (DRL) controllers in the large family of input-driven environments…
Plume: A Framework for High Performance Deep RL Network Controllers via Prioritized Trace Sampling
Sagar Patel, Junyang Zhang, Sangeetha Abdu Jyothi +1
Deep Reinforcement Learning (DRL) has shown promise in various networking environments. However, these environments present several fundamental challenges for standard DRL techniqu…
Caramel: Accelerating Decentralized Distributed Deep Learning with Computation Scheduling
Sayed Hadi Hashemi, Sangeetha Abdu Jyothi, Brighten Godfrey +1
The method of choice for parameter aggregation in Deep Neural Network (DNN) training, a network-intensive task, is shifting from the Parameter Server model to decentralized aggrega…
cISP: A Speed-of-Light Internet Service Provider
Debopam Bhattacherjee, Sangeetha Abdu Jyothi, Ilker Nadi Bozkurt +8
Low latency is a requirement for a variety of interactive network applications. The Internet, however, is not optimized for latency. We thus explore the design of cost-effective wi…
TicTac: Accelerating Distributed Deep Learning with Communication Scheduling
Sayed Hadi Hashemi, Sangeetha Abdu Jyothi, Roy H. Campbell
State-of-the-art deep learning systems rely on iterative distributed training to tackle the increasing complexity of models and input data. The iteration time in these communicatio…
Measuring and Understanding Throughput of Network Topologies
Sangeetha Abdu Jyothi, Ankit Singla, P. Brighten Godfrey +1
High throughput is of particular interest in data center and HPC networks. Although myriad network topologies have been proposed, a broad head-to-head comparison across topologies…