Tootfinder

Dflow, a Python framework for constructing cloud-native AI-for-Science workflows
Xinzijian Liu, Yanbo Han, Zhuoyuan Li, Jiahao Fan, Chengqian Zhang, Jinzhe Zeng, Yifan Shan, Yannan Yuan, Wei-Hong Xu, Yun-Pei Liu, Yuzhi Zhang, Tongqi Wen, Darrin M. York, Zhicheng Zhong, Hang Zheng, Jun Cheng, Linfeng Zhang, Han Wang
https://arxiv…

Dflow, a Python framework for constructing cloud-native AI-for-Science workflows
In the AI-for-science era, scientific computing scenarios such as concurrent learning and high-throughput computing demand a new generation of infrastructure that supports scalable computing resources and automated workflow management on both cloud and high-performance supercomputers. Here we introduce Dflow, an open-source Python toolkit designed for scientists to construct workflows with simple programming interfaces. It enables complex process control and task scheduling across a distributed…

Mitigating Data Sharing in Public Cloud using Blockchain
Pratik Patil, Prerna Tulsiani, Dr. Sunil Mane
https://arxiv.org/abs/2404.16872 https://

Mitigating Data Sharing in Public Cloud using Blockchain
Public Cloud Computing has become a fundamental part of modern IT infrastructure as its adoption has transformed the way businesses operate. However, cloud security concerns introduce new risks and challenges related to data protection, sharing, and access control. A synergistic integration of blockchain with the cloud holds immense potential. Blockchain's distributed ledger ensures transparency, immutability, and efficiency as it reduces the reliance on centralized authorities. Motivated by th…

Proximal Byzantine Consensus
Roy Shadmon, Daniel Spencer, Owen Arden
https://arxiv.org/abs/2402.12577 https://arxiv.org/pdf/2402.1257…

Proximal Byzantine Consensus
Distributed control systems require high reliability and availability guarantees despite often being deployed at the edge of network infrastructure. Edge computing resources are less secure and less reliable than centralized resources in data centers. Replication and consensus protocols improve robustness to network faults and crashed or corrupted nodes, but these volatile environments can cause non-faulty nodes to temporarily diverge, increasing the time needed for replicas to converge on a co…