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Collaborative Research: CIF: Small: Maximizing Coding Gain in Coded Computing

协作研究：CIF：小型：最大化编码计算中的编码增益

基本信息

批准号：
2327509
负责人：
Emina Soljanin
金额：
$ 37.5万
依托单位：
Rutgers University New Brunswick
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2023
资助国家：
美国
起止时间：
2023-10-01 至 2026-09-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2327509&HistoricalAwards=false
关键词：
Collaborative Research CIF Small Maximizing

项目摘要

Artificial intelligence and machine learning algorithms rely on parallel, distributed computing systems to efficiently carry out intricate, data-heavy tasks. A significant challenge in designing large-scale distributed computing systems is addressing the unpredictable variations in service times across multiple servers. Computing redundancy, such as task replication, is a promising powerful tool to curtail the overall variability in service time. This project focuses on the intelligent management of redundancy in distributed computing that will affect the execution efficiency of data-intensive algorithms in large-scale systems. The project will quantify redundancy benefits, pivotal to developing and ultimately deploying efficient redundancy schemes for executing artificial intelligence and machine learning workloads. The educational goal of the project includes stimulating students' interest in applied probability and mathematical modeling and developing hands-on labs on cloud computing infrastructure. The project will contribute to the Research Experiences for Undergraduate and High School students and will recruit and mentor women and members of underrepresented groups.This project considers distributed computing systems that use replication and erasure coding to reduce job execution times. The project aims to maximize the gain of using computing redundancy (coding gain) in practical scenarios. It complements recent work on redundancy in distributed systems, focusing primarily on designing redundancy schemes using erasure codes. The project will use statistical analysis and queueing and coding theories to make the following contributions: (i) characterization of the crucial effects of using redundancy in distributed computing, including analysis of the benefits and costs of redundancy; (ii) new mathematical models that capture the performance of distributed computing systems with stragglers; (iii) new analysis tools for computing coding gain in coded computing systems; (iv) development of redundancy management algorithms; (v) characterization of the diversity vs. parallelism trade-off; and (vi) addressing other critical issues in coded computing that do not exist in the better-understood replication solutions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

人工智能和机器学习算法依赖于并行、分布式计算系统来有效地执行复杂的、数据量大的任务。设计大规模分布式计算系统的一个重大挑战是解决跨多个服务器的服务时间的不可预测的变化。计算冗余，如任务复制，是一个有前途的强大的工具，以减少服务时间的整体变化。该项目的重点是分布式计算中冗余的智能管理，这将影响大规模系统中数据密集型算法的执行效率。该项目将量化冗余效益，这对于开发并最终部署用于执行人工智能和机器学习工作负载的高效冗余方案至关重要。该项目的教育目标包括激发学生对应用概率和数学建模的兴趣，并在云计算基础设施上开发动手实验室。该项目将为本科生和高中生的研究经验做出贡献，并将招募和指导妇女和代表性不足的群体成员。该项目考虑使用复制和擦除编码来减少作业执行时间的分布式计算系统。该项目旨在最大限度地提高在实际场景中使用计算冗余（编码增益）的增益。它补充了分布式系统中的冗余最近的工作，主要集中在使用纠删码设计冗余方案。该项目将利用统计分析以及编码和编码理论作出以下贡献：（一）描述在分布式计算中使用冗余的关键影响，包括分析冗余的好处和成本;（二）建立新的数学模型，以反映具有落后者的分布式计算系统的性能;（三）建立新的分析工具，以计算编码计算系统中的编码增益;（iv）冗余管理算法的开发;（v）多样性与并行性权衡的表征;以及（vi）解决编码计算中不存在于更好理解的复制解决方案中的其他关键问题。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。