Tootfinder

Benchmarking Distributed Coordination Systems: A Survey and Analysis
Bekir Turkkan, Tevfik Kosar, Aleksey Charapko, Ailidani Ailijiang, Murat Demirbas
https://arxiv.org/abs/2403.09445

Benchmarking Distributed Coordination Systems: A Survey and Analysis
Coordination services and protocols are critical components of distributed systems and are essential for providing consistency, fault tolerance, and scalability. However, due to lack of a standard benchmarking tool for distributed coordination services, coordination service developers/researchers either use a NoSQL standard benchmark and omit evaluating consistency, distribution, and fault-tolerance; or create their own ad-hoc microbenchmarks and skip comparability with other services. In this …

Distributed Secret Securing in Discrete-Event Systems
Shoma Matsui, Kai Cai, Karen Rudie
https://arxiv.org/abs/2402.08878 https://arx…

Distributed Secret Securing in Discrete-Event Systems
In this paper, we study a security problem of protecting secrets in distributed systems. Specifically, we employ discrete-event systems to describe the structure and behaviour of distributed systems, in which global secret information is separated into pieces and stored in local component agents. The goal is to prevent such secrets from being exposed to intruders by imposing appropriate protection measures. This problem is formulated as to ensure that at least one piece of every distributed glo…

Distributed Optimization with Consensus Constraint for Multi-Robot Semantic Octree Mapping
Arash Asgharivaskasi, Nikolay Atanasov
https://arxiv.org/abs/2402.08867

Distributed Optimization with Consensus Constraint for Multi-Robot Semantic Octree Mapping
This work develops a distributed optimization algorithm for multi-robot 3-D semantic mapping using streaming range and visual observations and single-hop communication. Our approach relies on gradient-based optimization of the observation log-likelihood of each robot subject to a map consensus constraint to build a common multi-class map of the environment. This formulation leads to closed-form updates which resemble Bayes rule with one-hop prior averaging. To reduce the amount of information e…

A-DisETrac Advanced Analytic Dashboard for Distributed Eye Tracking
Yasasi Abeysinghe, Bhanuka Mahanama, Gavindya Jayawardena, Yasith Jayawardana, Mohan Sunkara, Andrew T. Duchowski, Vikas Ashok, Sampath Jayarathna
https://arxiv.org/abs/2404.08143

A-DisETrac Advanced Analytic Dashboard for Distributed Eye Tracking
Understanding how individuals focus and perform visual searches during collaborative tasks can help improve user engagement. Eye tracking measures provide informative cues for such understanding. This article presents A-DisETrac, an advanced analytic dashboard for distributed eye tracking. It uses off-the-shelf eye trackers to monitor multiple users in parallel, compute both traditional and advanced gaze measures in real-time, and display them on an interactive dashboard. Using two pilot studie…

This https://arxiv.org/abs/2309.12483 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

PrivAgE: A Toolchain for Privacy-Preserving Distributed Aggregation on Edge-Devices
Valuable insights, such as frequently visited environments in the wake of the COVID-19 pandemic, can oftentimes only be gained by analyzing sensitive data spread across edge-devices like smartphones. To facilitate such an analysis, we present a toolchain called PrivAgE for a distributed, privacy-preserving aggregation of local data by taking the limited resources of edge-devices into account. The distributed aggregation is based on secure summation and simultaneously satisfies the notion of dif…

Constrained Reinforcement Learning for Adaptive Controller Synchronization in Distributed SDN
Ioannis Panitsas, Akrit Mudvari, Leandros Tassiulas
https://arxiv.org/abs/2403.08775 …

Constrained Reinforcement Learning for Adaptive Controller Synchronization in Distributed SDN
In software-defined networking (SDN), the implementation of distributed SDN controllers, with each controller responsible for managing a specific sub-network or domain, plays a critical role in achieving a balance between centralized control, scalability, reliability, and network efficiency. These controllers must be synchronized to maintain a logically centralized view of the entire network. While there are various approaches for synchronizing distributed SDN controllers, most tend to prioriti…

Byzantine fault-tolerant distributed set intersection with redundancy
Shuo Liu, Nitin H. Vaidya
https://arxiv.org/abs/2402.08809 https://

Byzantine fault-tolerant distributed set intersection with redundancy
In this report, we study the problem of Byzantine fault-tolerant distributed set intersection and the importance of redundancy in solving this problem. Specifically, consider a distributed system with $n$ agents, each of which has a local set. There are up to $f$ agents that are Byzantine faulty. The goal is to find the intersection of the sets of the non-faulty agents. We derive the Byzantine set intersection problem from the Byzantine optimization problem. We present the definition of $2f$-…

BurstAttention: An Efficient Distributed Attention Framework for Extremely Long Sequences
Sun Ao, Weilin Zhao, Xu Han, Cheng Yang, Zhiyuan Liu, Chuan Shi, Maosong Sun, Shengnan Wang, Teng Su
https://arxiv.org/abs/2403.09347

BurstAttention: An Efficient Distributed Attention Framework for Extremely Long Sequences
Effective attention modules have played a crucial role in the success of Transformer-based large language models (LLMs), but the quadratic time and memory complexities of these attention modules also pose a challenge when processing long sequences. One potential solution for the long sequence problem is to utilize distributed clusters to parallelize the computation of attention modules across multiple devices (e.g., GPUs). However, adopting a distributed approach inevitably introduces extra mem…

Application of Distributed Arithmetic to Adaptive Filtering Algorithms: Trends, Challenges and Future
Mohd. Tasleem Khan
https://arxiv.org/abs/2403.08099 h…

Application of Distributed Arithmetic to Adaptive Filtering Algorithms: Trends, Challenges and Future
The utilization of distributed arithmetic (DA) in AF algorithms has gained significant attention in recent years due to its potential to enhance computational efficiency and reduce resource requirements. This paper presents an exploration of the application of DA to adaptive filtering (AF) algorithms, analyzing trends, discussing challenges, and outlining future prospects. It begins by providing an overview of both DA and AF algorithms, highlighting their individual merits and established appli…

Uncertainty Estimation in Multi-Agent Distributed Learning for AI-Enabled Edge Devices
Gleb Radchenko, Victoria Andrea Fill
https://arxiv.org/abs/2403.09141

Uncertainty Estimation in Multi-Agent Distributed Learning for AI-Enabled Edge Devices
Initially considered as low-power units with limited autonomous processing, Edge IoT devices have seen a paradigm shift with the introduction of FPGAs and AI accelerators. This advancement has vastly amplified their computational capabilities, emphasizing the practicality of edge AI. Such progress introduces new challenges of optimizing AI tasks for the limitations of energy and network resources typical in Edge computing environments. Our study explores methods that enable distributed data pro…