BIGDATA: Collaborative Research: F: RDMA-Based Datacenter Networks for Online Big Data Applications

BIGDATA:协作研究:F:用于在线大数据应用的基于 RDMA 的数据中心网络

基本信息

  • 批准号:
    1633412
  • 负责人:
  • 金额:
    $ 57万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2016
  • 资助国家:
    美国
  • 起止时间:
    2016-09-01 至 2021-08-31
  • 项目状态:
    已结题

项目摘要

This project addresses the challenge of achieving extreme low latency in datacenter networks for Online Big Data (OLBD) applications which are critical workloads in datacenter computing. Remote Direct Memory Access (RDMA), which is a promising alternative to traditional TCP, significantly reduces datacenter network latencies by about an order-of-magnitude. However, RDMA adoption poses two major challenges as RDMA suffers from performance fragility under congestion, and RDMA incurs either wasted memory or significant programmer burden for typical OLBD traffic. This project develops two novel networking technologies -- Blitz and RIMA -- which enable scalable datacenter networks that achieve the low latency benefits of RDMA while avoiding its drawbacks (performance fragility, programmer burden, and wasted memory).Blitz addresses performance fragility by decoupling edge-congestion and in-network congestion. Blitz handles edge-congestion using receiver-directed congestion control (RDCC) unlike prior approaches where senders have to infer sending rates indirectly from round-trip-times and/or dropped packets. RDCC enables accurate and fast (within-one-round-trip-time) convergence, which leads to lower latency and higher throughput. Blitz handles transient in-network congestion by deflecting packets along longer yet less-congested paths.Remote Indirect Memory Access (RIMA) addresses the second challenge by enabling reactive, on-demand memory allocation as opposed to RDMAs proactive memory allocation for the worst case, which minimizes the memory footprint without programmer effort. Together, Blitz and RIMA enable extreme low datacenter network latency for OLBD applications. The project will extensively involve Ph.D, Masters, and undergraduate students in cross-layer research activities. Results from the projects will be broadly disseminated via publication in scientific conferences.
该项目解决了在线大数据(OLBD)应用程序在数据中心网络中实现极低延迟的挑战,这些应用程序是数据中心计算中的关键工作负载。远程直接内存访问(RDMA)是传统TCP的一种很有前途的替代方案,它显著减少了数据中心网络延迟约一个数量级。然而,RDMA的采用带来了两大挑战,因为RDMA在拥塞情况下存在性能脆弱性,并且RDMA会对典型的OLBD流量造成内存浪费或严重的程序员负担。该项目开发了两种新的网络技术--Blitz和RIMA--它们实现了可扩展的数据中心网络,实现了RDMA的低延迟优势,同时避免了其缺点(性能脆弱性、程序员负担和内存浪费)。Blitz通过分离边缘拥塞和网络内拥塞来解决性能脆弱性。闪电战使用接收方指导的拥塞控制(RDCC)来处理边缘拥塞,而不像以前的方法,发送方必须从往返时间和/或丢弃的分组间接推断发送速率。RDCC能够实现准确快速(一次往返时间内)的融合,从而实现更低的延迟和更高的吞吐量。闪电战通过将数据包转移到更长但不那么拥塞的路径来处理网络内的暂时性拥塞。远程间接内存访问(RIMA)通过实现反应性的按需内存分配来解决第二个挑战,而不是RDMA在最坏情况下的主动内存分配,这可以在不需要程序员努力的情况下最大限度地减少内存占用。Blitz和RIMA共同为OLBD应用程序实现了极低的数据中心网络延迟。该项目将广泛地让博士、硕士和本科生参与跨层研究活动。这些项目的成果将通过科学会议上的出版物广泛传播。

项目成果

期刊论文数量(6)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Slytherin: Dynamic, Network-Assisted Prioritization of Tail Packets in Datacenter Networks
斯莱特林:数据中心网络中尾部数据包的动态网络辅助优先级排序
Millipede: Die-Stacked Memory Optimizations for Big Data Machine Learning Analytics
Millipede:用于大数据机器学习分析的芯片堆叠内存优化
FastZ: accelerating gapped whole genome alignment on GPUs
FastZ:在 GPU 上加速有缺口的全基因组比对
  • DOI:
    10.1145/3458817.3476202
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Gundabolu, Sree Charan;Vijaykumar, T. N.;Thottethodi, Mithuna
  • 通讯作者:
    Thottethodi, Mithuna
Network Interface Architecture for Remote Indirect Memory Access (RIMA) in Datacenters
Dart: Divide and Specialize for Fast Response to Congestion in RDMA-Based Datacenter Networks
  • DOI:
    10.1109/tnet.2019.2961671
  • 发表时间:
    2018-05
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Jiachen Xue;M. Chaudhry;Balajee Vamanan;T. N. Vijaykumar;Mithuna Thottethodi
  • 通讯作者:
    Jiachen Xue;M. Chaudhry;Balajee Vamanan;T. N. Vijaykumar;Mithuna Thottethodi
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Mithuna Thottethodi其他文献

Power-efficient clustering via incomplete bypassing
通过不完全旁路实现节能集群
BLAM: a high-performance routing algorithm for virtual cut-through networks
BLAM:虚拟直通网络的高性能路由算法
Stratified Online Sampling for Sound Approximation in MapReduce
MapReduce 中声音近似的分层在线采样
  • DOI:
  • 发表时间:
    2015
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Mithuna Thottethodi;T. N. Vijaykumar;Milind Kulkarni;Nitin
  • 通讯作者:
    Nitin
Achieving Causal Consistency under Partial Replication for Geo-distributed Cloud Storage
地理分布式云存储部分复制下实现因果一致性
  • DOI:
  • 发表时间:
    2016
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Tariq Mahmood;S. Narayanan;Sanjay G. Rao;T. N. Vijaykumar;Mithuna Thottethodi
  • 通讯作者:
    Mithuna Thottethodi
Efficient Collaborative Approximation in MapReduce without Missing Rare Keys
MapReduce 中的高效协作逼近而不丢失稀有密钥

Mithuna Thottethodi的其他文献

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{{ truncateString('Mithuna Thottethodi', 18)}}的其他基金

CSR: Small: SmartEdge for Low Latency and Consistent Mobile Web Applications
CSR:小型:用于低延迟和一致的移动 Web 应用程序的 SmartEdge
  • 批准号:
    1618921
  • 财政年份:
    2016
  • 资助金额:
    $ 57万
  • 项目类别:
    Standard Grant
CAREER: Cross-Layer Schemes For Flexible Resource Sharing in Multicore Systems
职业:多核系统中灵活资源共享的跨层方案
  • 批准号:
    0644183
  • 财政年份:
    2007
  • 资助金额:
    $ 57万
  • 项目类别:
    Continuing Grant
Performance Models and Systems Optimization for Disk-Bound Applications
磁盘密集型应用程序的性能模型和系统优化
  • 批准号:
    0621457
  • 财政年份:
    2006
  • 资助金额:
    $ 57万
  • 项目类别:
    Standard Grant
CPA: Reconfigurable On-chip Network Design Framework for Fault-Tolerance and Performance
CPA:可重构片上网络设计框架,实现容错和性能
  • 批准号:
    0541385
  • 财政年份:
    2006
  • 资助金额:
    $ 57万
  • 项目类别:
    Standard Grant

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