tcp pacing in data center networks
play

TCP Pacing in Data Center Networks Monia Ghobadi, Yashar Ganjali - PowerPoint PPT Presentation

Sep 08, 2022 •473 likes •1.52k views

TCP Pacing in Data Center Networks Monia Ghobadi, Yashar Ganjali Department of Computer Science, University of Toronto {monia, yganjali}@cs.toronto.edu 1 TCP , Oh TCP! 2 TCP , Oh TCP! TCP congestion control 2 TCP , Oh TCP! TCP

  1. Base-Case Experiment: One flow vs Two flows, 64KB of buffering, Utilization/Drop/FCT No congestion Congestion 300 paced Bottleneck Link Utilization (Mbps) non − paced 250 200 38% 150 100 50 0 0 50 100 150 200 250 300 Time (sec) 1 1 paced paced 0.9 0.9 non − paced non − paced 0.8 0.8 0.7 0.7 1RTT 0.6 0.6 CDF CDF 0.5 0.5 2RTTs 2RTTs 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0 0 0.039 0.063 0.1 0.2 0.3 0.4 0.06 0.2 0.5 1 2 3 5 7 9 Flow Completion Time (sec) Flow Completion Time (sec)

  2. Multiple flows: Link Utilization/Drop/Latency Buffer size 1.7% of BDP , varying number of flows 10

  3. Multiple flows: Link Utilization/Drop/Latency Buffer size 1.7% of BDP , varying number of flows N * PoI Bottleneck Link Utilization (Mbps) 1000 800 600 400 paced 200 non − paced 0 0 10 20 30 40 50 60 70 80 90 100 Number of Flows 10

  4. Multiple flows: Link Utilization/Drop/Latency Buffer size 1.7% of BDP , varying number of flows N * PoI Bottleneck Link Utilization (Mbps) 1000 800 600 400 paced 200 non − paced 0 0 10 20 30 40 50 60 70 80 90 100 Number of Flows N * PoI 1 paced Bottleneck Link Drop (%) non − paced 0.8 0.6 0.4 0.2 0 0 10 20 30 40 50 60 70 80 90 100 10 Number of Flows

  5. Multiple flows: Link Utilization/Drop/Latency Buffer size 1.7% of BDP , varying number of flows N * N * PoI PoI Bottleneck Link Utilization (Mbps) 1000 1 paced non − paced 0.8 800 Average FCT (sec) 600 0.6 400 0.4 paced 200 0.2 non − paced 0 0 0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100 Number of Flows Number of Flows N * PoI 1 paced Bottleneck Link Drop (%) non − paced 0.8 0.6 0.4 0.2 0 0 10 20 30 40 50 60 70 80 90 100 10 Number of Flows

  6. Multiple flows: Link Utilization/Drop/Latency Buffer size 1.7% of BDP , varying number of flows N * N * PoI PoI Bottleneck Link Utilization (Mbps) 1000 1 paced non − paced 0.8 800 Average FCT (sec) 600 0.6 400 0.4 paced 200 0.2 non − paced 0 0 0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100 Number of Flows Number of Flows N * N * PoI PoI 1 2 paced paced 99 th Percentile FCT (sec) 1.8 Bottleneck Link Drop (%) non − paced non − paced 0.8 1.6 1.4 0.6 1.2 1 0.4 0.8 0.6 0.2 0.4 0.2 0 0 0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100 10 Number of Flows Number of Flows

  7. Multiple flows: Link Utilization/Drop/Latency Buffer size 1.7% of BDP , varying number of flows N * N * PoI PoI Bottleneck Link Utilization (Mbps) 1000 1 paced non − paced 0.8 800 Average FCT (sec) 600 0.6 400 0.4 paced 200 0.2 non − paced 0 0 0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100 • Number of concurrent connections increase beyond a Number of Flows Number of Flows N * N * certain point the benefits of pacing diminish. PoI PoI 1 2 paced paced 99 th Percentile FCT (sec) 1.8 Bottleneck Link Drop (%) non − paced non − paced 0.8 1.6 1.4 0.6 1.2 1 0.4 0.8 0.6 0.2 0.4 0.2 0 0 0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100 10 Number of Flows Number of Flows

  8. Multiple flows: Link Utilization/Drop/Latency Buffer size 3.4% of BDP , varying number of flows 11

  9. Multiple flows: Link Utilization/Drop/Latency Buffer size 3.4% of BDP , varying number of flows N * N * PoI PoI Bottleneck Link Utilization (Mbps) 1 1000 800 0.8 Average FCT (sec) 600 0.6 400 0.4 200 paced paced 0.2 non − paced non − paced 0 0 10 20 30 40 50 60 70 80 90 100 0 Number of Flows 0 10 20 30 40 50 60 70 80 90 100 Number of Flows N * PoI 0.8 2 paced nonpaced 99 th Percentile FCT (sec) 1.8 0.7 1.6 0.6 Bottleneck link drop(%) 1.4 0.5 1.2 0.4 1 0.8 0.3 0.6 paced 0.2 0.4 non − paced 0.1 0.2 0 0 0 20 40 60 80 100 0 10 20 30 40 50 60 70 80 90 100 11 Number of flows sharing the bottleneck Number of Flows

  10. Multiple flows: Link Utilization/Drop/Latency Buffer size 3.4% of BDP , varying number of flows N * N * PoI PoI Bottleneck Link Utilization (Mbps) 1 1000 800 0.8 Average FCT (sec) 600 • Aagarwal et al.: Don’t pace! 0.6 400 • 50 flows, BDP 1250 packets and buffer size 0.4 200 paced 312 packets paced 0.2 non − paced non − paced 0 • N* = 8 flows. 0 10 20 30 40 50 60 70 80 90 100 0 Number of Flows 0 10 20 30 40 50 60 70 80 90 100 • Kulik et al.: Pace! Number of Flows N * PoI 0.8 2 • 1 flow, BDP 91 packets, buffer size 10 paced nonpaced 99 th Percentile FCT (sec) 1.8 0.7 1.6 packets. 0.6 Bottleneck link drop(%) 1.4 0.5 • N* = 9 flows. 1.2 0.4 1 0.8 0.3 0.6 paced 0.2 0.4 non − paced 0.1 0.2 0 0 0 20 40 60 80 100 0 10 20 30 40 50 60 70 80 90 100 11 Number of flows sharing the bottleneck Number of Flows

  11. N* vs. Bu fg er 12

  12. N* vs. Bu fg er Bottleneck Link Utilization (Mbps) 1000 800 600 400 200 paced non − paced 0 50 100 150 200 250 Buffer Size (KB) 12

  13. N* vs. Bu fg er Bottleneck Link Utilization (Mbps) 1000 800 600 400 200 paced non − paced 0 50 100 150 200 250 Buffer Size (KB) 0.8 paced nonpaced 0.7 0.6 Bottleneck link drop(%) 0.5 0.4 0.3 0.2 0.1 0 50 100 150 200 250 12 Buffer size(KB)

  14. N* vs. Bu fg er Bottleneck Link Utilization (Mbps) 1.4 1000 paced 1.2 non − paced 800 CT (sec) 1 600 0.8 Average � 0.6 400 0.4 200 paced 0.2 non − paced 0 0 50 100 150 200 250 50 100 150 200 250 Buffer Size (KB) Buffer Size (KB) 0.8 paced nonpaced 0.7 0.6 Bottleneck link drop(%) 0.5 0.4 0.3 0.2 0.1 0 50 100 150 200 250 12 Buffer size(KB)

  15. N* vs. Bu fg er Bottleneck Link Utilization (Mbps) 1.4 1000 paced 1.2 non − paced 800 CT (sec) 1 600 0.8 Average � 0.6 400 0.4 200 paced 0.2 non − paced 0 0 50 100 150 200 250 50 100 150 200 250 Buffer Size (KB) Buffer Size (KB) 2.8 0.8 paced paced CT (sec) nonpaced 2.4 0.7 non − paced 0.6 2 Bottleneck link drop(%) 99 th Percentile � 0.5 1.6 0.4 1.2 0.3 0.8 0.2 0.4 0.1 0 0 50 100 150 200 250 50 100 150 200 250 12 Buffer size(KB) Buffer Size (KB)

  16. N* vs. Bu fg er Bottleneck Link Utilization (Mbps) 1.4 1000 paced 1.2 non − paced 800 CT (sec) 1 600 0.8 Average � 0.6 400 0.4 200 paced 0.2 non − paced 0 0 50 100 150 200 250 50 100 150 200 250 Buffer Size (KB) Buffer Size (KB) 2.8 0.8 paced paced CT (sec) nonpaced 2.4 0.7 non − paced 0.6 2 Bottleneck link drop(%) 99 th Percentile � 0.5 1.6 0.4 1.2 0.3 0.8 0.2 0.4 0.1 0 0 50 100 150 200 250 50 100 150 200 250 12 Buffer size(KB) Buffer Size (KB)

  17. Clustering Effect: The probability of packets from a flow being followed by packets from other flows 13

  18. Clustering Effect: The probability of packets from a flow being followed by packets from other flows Non-paced: Packets of each flow are clustered together. 13

  19. Clustering Effect: The probability of packets from a flow being followed by packets from other flows Non-paced: Packets of each flow Paced: Packets of different flows are are clustered together. multiplexed. 13

  20. Drop Synchronization: Number of Flows Affected by Drop Event 14

  21. Drop Synchronization: Number of Flows Affected by Drop Event NetFPGA router to count the number of flows affected by drop events. 14

  22. Drop Synchronization: Number of Flows Affected by Drop Event NetFPGA router to count the number of flows affected by drop events. 1 1 1 paced 0.9 0.9 paced paced 0.9 non − paced 0.8 0.8 0.8 non − paced non − paced 0.7 0.7 0.7 0.6 0.6 0.6 CDF CDF CDF 0.5 0.5 0.5 0.4 0.4 0.4 0.3 0.3 0.3 0.2 0.2 0.2 0.1 0.1 0.1 0 0 0 0 20 40 60 80 0 50 100 150 200 250 300 350 400 0 10 20 30 40 Number of Flows Affected by Drop Event Number of Flows Affected by Drop Event Number of Flows Affected by Drop Event N: 48 N: 96 N: 384 14

  23. Drop Synchronization: Number of Flows Affected by Drop Event NetFPGA router to count the number of flows affected by drop events. 1 1 1 paced 0.9 0.9 paced paced 0.9 non − paced 0.8 0.8 0.8 non − paced non − paced 0.7 0.7 0.7 0.6 0.6 0.6 CDF CDF CDF 0.5 0.5 0.5 0.4 0.4 0.4 0.3 0.3 0.3 0.2 0.2 0.2 0.1 0.1 0.1 0 0 0 0 20 40 60 80 0 50 100 150 200 250 300 350 400 0 10 20 30 40 Number of Flows Affected by Drop Event Number of Flows Affected by Drop Event Number of Flows Affected by Drop Event N: 48 N: 96 N: 384 14

  24. Future Trends for Pacing: per-egress pacing. 15

  25. Future Trends for Pacing: per-egress pacing. N * N * PoI PoI Bottleneck Link Utilization (Mbps) 1 1000 per − flow paced non − paced Average RCT (sec) 0.8 800 per − host + per − flow paced 0.6 600 0.4 400 per − flow paced 200 0.2 non − paced per − host + per − flow paced 0 0 6 12 24 48 96 192 6 12 24 48 96 192 Number of Flows Number of Flows N * PoI 20 per − flow paced 2.2 per − flow paced nonpaced 99 th Percentile RCT (sec) 2 per − host + per − flow paced non − paced Bottleneck Link Drop (%) 1.8 15 per − host + per − flow paced 1.6 1.4 1.2 10 1 0.8 0.6 5 0.4 0.2 0 0 6 12 24 48 96 192 6 12 24 48 96 192 Number of flows Number of Flows 15

  26. Future Trends for Pacing: per-egress pacing. N * N * PoI PoI Bottleneck Link Utilization (Mbps) 1 1000 per − flow paced non − paced Average RCT (sec) 0.8 800 per − host + per − flow paced 0.6 600 0.4 400 per − flow paced 200 0.2 non − paced per − host + per − flow paced 0 0 6 12 24 48 96 192 6 12 24 48 96 192 Number of Flows Number of Flows N * PoI 20 per − flow paced 2.2 per − flow paced nonpaced 99 th Percentile RCT (sec) 2 per − host + per − flow paced non − paced Bottleneck Link Drop (%) 1.8 15 per − host + per − flow paced 1.6 1.4 1.2 10 1 0.8 0.6 5 0.4 0.2 0 0 6 12 24 48 96 192 6 12 24 48 96 192 Number of flows Number of Flows 15

  27. Future Trends for Pacing: per-egress pacing. N * N * PoI PoI Bottleneck Link Utilization (Mbps) 1 1000 per − flow paced non − paced Average RCT (sec) 0.8 800 per − host + per − flow paced 0.6 600 0.4 400 per − flow paced 200 0.2 non − paced per − host + per − flow paced 0 0 6 12 24 48 96 192 6 12 24 48 96 192 Number of Flows Number of Flows N * PoI 20 per − flow paced 2.2 per − flow paced nonpaced 99 th Percentile RCT (sec) 2 per − host + per − flow paced non − paced Bottleneck Link Drop (%) 1.8 15 per − host + per − flow paced 1.6 1.4 1.2 10 1 0.8 0.6 5 0.4 0.2 0 0 6 12 24 48 96 192 6 12 24 48 96 192 Number of flows Number of Flows 15

  28. Future Trends for Pacing: per-egress pacing. N * N * PoI PoI Bottleneck Link Utilization (Mbps) 1 1000 per − flow paced non − paced Average RCT (sec) 0.8 800 per − host + per − flow paced 0.6 600 0.4 400 per − flow paced 200 0.2 non − paced per − host + per − flow paced 0 0 6 12 24 48 96 192 6 12 24 48 96 192 Number of Flows Number of Flows N * PoI 20 per − flow paced 2.2 per − flow paced nonpaced 99 th Percentile RCT (sec) 2 per − host + per − flow paced non − paced Bottleneck Link Drop (%) 1.8 15 per − host + per − flow paced 1.6 1.4 1.2 10 1 0.8 0.6 5 0.4 0.2 0 0 6 12 24 48 96 192 6 12 24 48 96 192 Number of flows Number of Flows 15

  29. Future Trends for Pacing: per-egress pacing. N * N * PoI PoI Bottleneck Link Utilization (Mbps) 1 1000 per − flow paced non − paced Average RCT (sec) 0.8 800 per − host + per − flow paced 0.6 600 0.4 400 per − flow paced 200 0.2 non − paced per − host + per − flow paced 0 0 6 12 24 48 96 192 6 12 24 48 96 192 Number of Flows Number of Flows N * PoI 20 per − flow paced 2.2 per − flow paced nonpaced 99 th Percentile RCT (sec) 2 per − host + per − flow paced non − paced Bottleneck Link Drop (%) 1.8 15 per − host + per − flow paced 1.6 1.4 1.2 10 1 0.8 0.6 5 0.4 0.2 0 0 6 12 24 48 96 192 6 12 24 48 96 192 Number of flows Number of Flows 15

  30. Conclusions and Future work ๏ Re-examine TCP pacing’s effectiveness: ๏ Demonstrate when TCP pacing brings benefits in such environments. ๏ Inter-flow burstiness ๏ Burst-pacing vs. packet-pacing. ๏ Per-egress pacing. 16

  31. Renewed Interest 17

  32. Tra ffj c Burstiness Survey ๏ ‘Bursty’ is a word with no agreed meaning. How do you define a bursty traffic? ๏ If you are involved with a data center, is your data center traffic bursty? ๏ If yes, do you think that it will be useful to supress the burstiness in your traffic? ๏ If no, are you already supressing the burstiness? How? Would you anticipate the traffic becoming burstier in the future? monia@cs.toronto.edu 18

  33. 19

  34. Base-Case Experiment: One RPC vs Two RPCs, 64KB of bu fg ering, Latency 20

  35. Multiple flows: Link Utilization/Drop/ Latency Bu fg er size: 6% of BDP, varying number of 21

  36. Base-Case Experiment: One RPC vs Two RPCs, 64KB of bu fg ering, Latency / Queue Occupancy 22

  37. Base-Case Experiment: One RPC vs Two RPCs, 64KB of bu fg ering, Latency / Queue Occupancy 22

  38. Base-Case Experiment: One RPC vs Two RPCs, 64KB of bu fg ering, Latency / Queue Occupancy 22

  39. Base-Case Experiment: One RPC vs Two RPCs, 64KB of bu fg ering, Latency / Queue Occupancy 22

  40. Functional test 23

  41. Functional test 23

  42. Functional test 23

  43. Functional test 23

  44. RPC vs. Streaming Paced by ack clocking Bursty 10GE 1GE 10GE RTT = 10ms 24

  45. Zooming in more on the paced flow 25

  46. Multiple flows: Link Utilization/Drop/Latency Buffer size 6.8% of BDP , varying number of flows 26

Recommend

Temporary Cardiac Pacing 1 Objectives Outline various types of temporary pacing Identify

Temporary Cardiac Pacing 1 Objectives Outline various types of temporary pacing Identify

Temporary Cardiac Pacing 1 Objectives Outline various types of temporary pacing Identify how pacing method determined Outline insertion / application procedure for each type of pacing Identify initial nursing care required 2

1.27k views • 86 slides
Right Ventricular Pacing Revisited Unavoidable or to be Avoided Alireza Ghorbani Sharif, MD

Right Ventricular Pacing Revisited Unavoidable or to be Avoided Alireza Ghorbani Sharif, MD

Right Ventricular Pacing Revisited Unavoidable or to be Avoided Alireza Ghorbani Sharif, MD Electrophysiologist Tehran Arrhythmia Center March 2016 Deleterious Effects of RV Pacing? Altered left ventricular electrical and mechanical

494 views • 36 slides
Pacing in Race Walking Dr Brian Hanley b.hanley@leedsbeckett.ac.uk Introduction Pacing an

Pacing in Race Walking Dr Brian Hanley b.hanley@leedsbeckett.ac.uk Introduction Pacing an

Pacing in Race Walking Dr Brian Hanley b.hanley@leedsbeckett.ac.uk Introduction Pacing an endurance event properly is one of the most important aspects to get right for success, whether that means winning a medal, achieving a PB, beating

619 views • 29 slides
Sheriff: A Regional Pre-Alert Management Scheme in Data Center Networks Xiaofeng Gao, Wen Xu,

Sheriff: A Regional Pre-Alert Management Scheme in Data Center Networks Xiaofeng Gao, Wen Xu,

Sheriff: A Regional Pre-Alert Management Scheme in Data Center Networks Xiaofeng Gao, Wen Xu, Fan Wu, Guihai Chen and Ding-Zhu Du Shanghai Jiao Tong University Background Data Center Networks Background Goal of a data center network

359 views • 25 slides
LA Marathon-SRLA Presentation Marathon Pacing Strategies Pacing Strategies Getting Started

LA Marathon-SRLA Presentation Marathon Pacing Strategies Pacing Strategies Getting Started

LA Marathon-SRLA Presentation Marathon Pacing Strategies Pacing Strategies Getting Started Choose your marathon goal Consider training technique Factor in race Conditions Getting Started-Marathon Goal Goal #1 Just finish

145 views • 10 slides
Cardiovascular Hospitalizations and Permanent Atrial Fibrillation Compared to Conventional Dual

Cardiovascular Hospitalizations and Permanent Atrial Fibrillation Compared to Conventional Dual

Atrial Antitachycardia Pacing and Managed Ventricular Pacing Reduce the Endpoint Composed by Death, Cardiovascular Hospitalizations and Permanent Atrial Fibrillation Compared to Conventional Dual Chamber Pacing in Bradycardia Patients: Results

192 views • 15 slides
Friends, not Foes Synthesizing Existing Transport Strategies for Data Center Networks Ali

Friends, not Foes Synthesizing Existing Transport Strategies for Data Center Networks Ali

Friends, not Foes Synthesizing Existing Transport Strategies for Data Center Networks Ali Munir Michigan State University Michigan State University Ghufran Baig, Syed M. Irteza, Ihsan A. Qazi, Alex X. Liu, Fahad R. Dogar Data Center (DC)

1.34k views • 66 slides
AI and Predictive Analytics in Data-Center Environments Neural Networks and Deep Learning Josep

AI and Predictive Analytics in Data-Center Environments Neural Networks and Deep Learning Josep

AI and Predictive Analytics in Data-Center Environments Neural Networks and Deep Learning Josep Ll. Berral @BSC Intel Academic Education Mindshare Initiative for AI Introduction Neural networks attempt to imitate the brain neuron

339 views • 21 slides
Smart Pacing for Effective Online Ad Campaign Optimization Jian Xu, Kuang-chih Lee, Wentong Li,

Smart Pacing for Effective Online Ad Campaign Optimization Jian Xu, Kuang-chih Lee, Wentong Li,

Smart Pacing for Effective Online Ad Campaign Optimization Jian Xu, Kuang-chih Lee, Wentong Li, Hang Qi, and Quan Lu Yahoo Inc. August 26, 2015 Jian Xu et al. (Yahoo Inc.) Smart Pacing August 26, 2015 1 / 17 Overview Background 1 Pacing

303 views • 17 slides
1 9/14/2019 Traditional Pacemakers What Are We Really Comparing? HIS-Bundle Pacing has been

1 9/14/2019 Traditional Pacemakers What Are We Really Comparing? HIS-Bundle Pacing has been

9/14/2019 Disclosures CON: HIS-Bundle Pacing Should be First Line Therapy for AV block with Preserved Research LV Function Medtronic Zoll California HRS 2019 Boston Scientific Consulting Biotelemetry Byron K. Lee MD

499 views • 7 slides
Lecture 18: Congestion Control in Data Center Networks 1 Overview Why is the problem

Lecture 18: Congestion Control in Data Center Networks 1 Overview Why is the problem

Lecture 18: Congestion Control in Data Center Networks 1 Overview Why is the problem different from that in the Internet? What are possible solutions? 2 DC Traffic Patterns In-cast applications Client send queries to servers

992 views • 39 slides
Elas%cTree: Saving Energy in Data Center Networks Brandon

Elas%cTree: Saving Energy in Data Center Networks Brandon

Elas%cTree: Saving Energy in Data Center Networks Brandon Heller (Stanford) Srini Seetharaman (Deutsche Telekom R&D, Los Altos) Priya Mahadevan

869 views • 66 slides
ElasticTree: Saving Energy in Data Center Networks Brandon Heller, Srini Seetharaman, Priya

ElasticTree: Saving Energy in Data Center Networks Brandon Heller, Srini Seetharaman, Priya

ElasticTree: Saving Energy in Data Center Networks Brandon Heller, Srini Seetharaman, Priya Mahadevan, Yiannis Yiakoumis, Puneet Sharma, Sujata Banerjee, Nick McKeown Presentation by Micha Dereziski Problem Data centers consume huge

255 views • 21 slides
ServerSwitch: A Programmable and High Performance Platform for Data Center Networks Guohan Lu,

ServerSwitch: A Programmable and High Performance Platform for Data Center Networks Guohan Lu,

ServerSwitch: A Programmable and High Performance Platform for Data Center Networks Guohan Lu, Chuanxiong Guo, Yulong Li, Zhiqiang Zhou , Tong Yuan, Haitao Wu, Yongqiang Xiong, Rui Gao, Yongguang Zhang Microsoft Research Asia Tsinghua

326 views • 19 slides
Electrophysiology and Pacing in Portugal: current situation JOO PRIMO Saturday 20th February

Electrophysiology and Pacing in Portugal: current situation JOO PRIMO Saturday 20th February

Electrophysiology and Pacing in Portugal: current situation JOO PRIMO Saturday 20th February 2010 Thessaloniki Portugal 10.566.212 (July 2005) people 2 million in Lisbon area 1,3 million in Oporto area 43 pacing

888 views • 34 slides
Ch 6b: Data-center networking Holger Karl Future Internet Computer Networks Group Universitt

Ch 6b: Data-center networking Holger Karl Future Internet Computer Networks Group Universitt

Ch 6b: Data-center networking Holger Karl Future Internet Computer Networks Group Universitt Paderborn Outline Evolution of data centres Topologies Networking issues Case study: Jupiter rising SS 19, v 0.9 FI - Ch 6b:

430 views • 25 slides
Data Center Challenges Building Networks for Agility Sreenivas Addagatla, Albert Greenberg,

Data Center Challenges Building Networks for Agility Sreenivas Addagatla, Albert Greenberg,

Data Center Challenges Building Networks for Agility Sreenivas Addagatla, Albert Greenberg, James Hamilton, Navendu Jain, Srikanth Kandula, Changhoon Kim, Parantap Lahiri, David A. Maltz, Parveen Patel Sudipta Sengupta 1 Agenda Brief

961 views • 46 slides
Data Center Challenges Building Networks for Agility Sreenivas Addagatla, Albert Greenberg,

Data Center Challenges Building Networks for Agility Sreenivas Addagatla, Albert Greenberg,

Data Center Challenges Building Networks for Agility Sreenivas Addagatla, Albert Greenberg, James Hamilton, Navendu Jain, Srikanth Kandula, Changhoon Kim, Parantap Lahiri, David A. Maltz, Parveen Patel Sudipta Sengupta 1 Capacity Issues

499 views • 48 slides
1 9/14/2019 Selective His Bundle Pacing Histological 90 90 40 40 S V H V HBP Correa de

1 9/14/2019 Selective His Bundle Pacing Histological 90 90 40 40 S V H V HBP Correa de

9/14/2019 Disclosures When and How to Perform Consultant - Abbott His-Purkinje Conduction System Pacing Advisory board - Boston Scientific Advisory board - Eaglepoint LLC Speaker, Consultant, Research - Medtronic Fellowship support

577 views • 16 slides
Tagger: Practical PFC Deadlock Prevention in Data Center Networks Shuihai Hu*(HKUST) , Yibo Zhu,

Tagger: Practical PFC Deadlock Prevention in Data Center Networks Shuihai Hu*(HKUST) , Yibo Zhu,

Tagger: Practical PFC Deadlock Prevention in Data Center Networks Shuihai Hu*(HKUST) , Yibo Zhu, Peng Cheng, Chuanxiong Guo* (Toutiao), Kun Tan*(Huawei), Jitendra Padhye, Kai Chen (HKUST) Microsoft CoNEXT 2017, Incheon, South Korea * Work done

549 views • 36 slides
D ATA center networks use multi-rooted Clos topologies to balances the number of flowcells. It

D ATA center networks use multi-rooted Clos topologies to balances the number of flowcells. It

IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL. 30, NO. 1, JANUARY 2019 133 Luopan: Sampling-Based Load Balancing in Data Center Networks Peng Wang , Member, IEEE , George Trimponias, Hong Xu , Member, IEEE , and Yanhui Geng

309 views • 13 slides
On Maximum Elastic Scheduling of Virtual Machines for Cloud-based Data Center Networks Jie Wu b ,

On Maximum Elastic Scheduling of Virtual Machines for Cloud-based Data Center Networks Jie Wu b ,

On Maximum Elastic Scheduling of Virtual Machines for Cloud-based Data Center Networks Jie Wu b , Shuaibing Lu a,b , and Huangyang Zheng a a College of Computer Science and Tech., Jilin University b Dept. of Computer and Info. Sciences, Temple

646 views • 18 slides
Minimal Rewiring: Efficient Live Expansion for Clos Data Center Networks Shizhen Zhao 1 2 ,

Minimal Rewiring: Efficient Live Expansion for Clos Data Center Networks Shizhen Zhao 1 2 ,

Minimal Rewiring: Efficient Live Expansion for Clos Data Center Networks Shizhen Zhao 1 2 , Rui Wang 1 , Junlan Zhou 1 , Joon Ong 1 ,Jeffrey C. Mogul 1 , Amin Vahdat 1 1. Google NetInfra; 2. Shanghai Jiao Tong University 1 Clos Topology for

536 views • 26 slides
AOPs, biological networks, and data analysis Ed Perkins, Ph.D., US Army Senior Scientist (ST)

AOPs, biological networks, and data analysis Ed Perkins, Ph.D., US Army Senior Scientist (ST)

AOPs, biological networks, and data analysis Ed Perkins, Ph.D., US Army Senior Scientist (ST) Environmental networks and toxicology US Army Engineer Research and Development Center Vicksburg, MS 39180 Bridging biological networks and AOPs

516 views • 33 slides

More recommend