Implementation

Recommendations

Info

Data-Driven Systems Encoding, Fast and Slow: Low-Latency Video Processing Using Thousands of Tiny Threads. ........... 363 Sadjad Fouladi, Riad S. Wahby, and Brennan Shacklett, Stanford University; Karthikeyan Vasuki Balasubramaniam, University of California, San Diego; William Zeng, Stanford University; Rahul Bhalerao, University of California, San Diego; Anirudh Sivaraman, Massachusetts Institute of Technology; George Porter, University of California, San Diego; Keith Winstein, Stanford University Live Video Analytics at Scale with Approximation and Delay-Tolerance. ............................. 377 Haoyu Zhang, Microsoft and Princeton University; Ganesh Ananthanarayanan, Peter Bodik, Matthai Philipose, and Paramvir Bahl, Microsoft; Michael J. Freedman, Princeton University Pytheas: Enabling Data-Driven Quality of Experience Optimization Using Group-Based Exploration-Exploitation. .................................................................... 393 Junchen Jiang, Carnegie Mellon University; Shijie Sun, Tsinghua University; Vyas Sekar, Carnegie Mellon University; Hui Zhang, Carnegie Mellon University and Conviva Inc. Datacenter Networking Let It Flow: Resilient Asymmetric Load Balancing with Flowlet Switching. .......................... 407 Erico Vanini and Rong Pan, Cisco Systems; Mohammad Alizadeh, Massachusetts Institute of Technology; Parvin Taheri and Tom Edsall, Cisco Systems Flowtune: Flowlet Control for Datacenter Networks .............................................. 421 Jonathan Perry, Hari Balakrishnan, and Devavrat Shah, Massachusetts Institute of Technology Flexplane: An Experimentation Platform for Resource Management in Datacenters. .................. 437 Amy Ousterhout, Jonathan Perry, and Hari Balakrishnan, MIT CSAIL; Petr Lapukhov, Facebook Cloud and Distributed Systems I Can’t Believe It’s Not Causal! Scalable Causal Consistency with No Slowdown Cascades .............. 453 Syed Akbar Mehdi, Cody Littley, and Natacha Crooks, The University of Texas at Austin; Lorenzo Alvisi, The University of Texas at Austin and Cornell University; Nathan Bronson, Facebook; Wyatt Lloyd, University of Southern California CherryPick: Adaptively Unearthing the Best Cloud Configurations for Big Data Analytics. ............. 469 Omid Alipourfard, Yale University; Hongqiang Harry Liu and Jianshu Chen, Microsoft Research; Shivaram Venkataraman, University of California, Berkeley; Minlan Yu, Yale University; Ming Zhang, Alibaba Group AdaptSize: Orchestrating the Hot Object Memory Cache in a Content Delivery Network. .............. 483 Daniel S. Berger, University of Kaiserslautern; Ramesh K. Sitaraman, University of Massachusetts Amherst and Akamai Technologies; Mor Harchol-Balter, Carnegie Mellon University Wednesday, March 29 Mobile Systems and IoT Bringing IoT to Sports Analytics. .............................................................. 499 Mahanth Gowda, Ashutosh Dhekne, Sheng Shen, and Romit Roy Choudhury, University of Illinois at Urbana– Champaign; Xue Yang, Lei Yang, Suresh Golwalkar, and Alexander Essanian, Intel FarmBeats: An IoT Platform for Data-Driven Agriculture. ........................................ 515 Deepak Vasisht, Microsoft and Massachusetts Institute of Technology; Zerina Kapetanovic, Microsoft and University of Washington; Jongho Won, Microsoft and Purdue University; Xinxin Jin, Microsoft and University of California, San Diego; Ranveer Chandra, Ashish Kapoor, Sudipta N. Sinha, and Madhusudhan Sudarshan, Microsoft; Sean Stratman, Dancing Crow Farm (Continues on next page)
Enabling High-Quality Untethered Virtual Reality. .............................................. 531 Omid Abari, Dinesh Bharadia, Austin Duffield, and Dina Katabi, Massachusetts Institute of Technology Improving User Perceived Page Load Times Using Gaze........................................... 545 Conor Kelton, Jihoon Ryoo, Aruna Balasubramanian, and Samir R. Das, Stony Brook University Networking in the Datacenter RAIL: A Case for Redundant Arrays of Inexpensive Links in Data Center Networks. .................. 561 Danyang Zhuo, University of Washington; Monia Ghobadi, Ratul Mahajan, Amar Phanishayee, and Xuan Kelvin Zou, Microsoft Research; Hang Guan, Columbia University; Arvind Krishnamurthy and Thomas Anderson, University of Washington Enabling Wide-Spread Communications on Optical Fabric with MegaSwitch. ........................ 577 Li Chen and Kai Chen, The Hong Kong University of Science and Technology; Zhonghua Zhu, Omnisensing Photonics; Minlan Yu, Yale University; George Porter, University of California, San Diego; Chunming Qiao, University at Buffalo; Shan Zhong, CoAdna Passive Realtime Datacenter Fault Detection and Localization . .................................... 595 Arjun Roy, University of California, San Diego; Hongyi Zeng and Jasmeet Bagga, Facebook; Alex C. Snoeren, University of California, San Diego Big Data Systems Clipper: A Low-Latency Online Prediction Serving System. ....................................... 613 Daniel Crankshaw, Xin Wang, and Guilio Zhou, University of California, Berkeley; Michael J. Franklin, University of California, Berkeley, and The University of Chicago; Joseph E. Gonzalez and Ion Stoica, University of California, Berkeley Gaia: Geo-Distributed Machine Learning Approaching LAN Speeds. ............................... 629 Kevin Hsieh, Aaron Harlap, Nandita Vijaykumar, Dimitris Konomis, Gregory R. Ganger, and Phillip B. Gibbons, Carnegie Mellon University; Onur Mutlu, ETH Zurich and Carnegie Mellon University Efficient Memory Disaggregation with Infiniswap.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 649 Juncheng Gu, Youngmoon Lee, Yiwen Zhang, Mosharaf Chowdhury, and Kang G. Shin, University of Michigan TuX 2 : Distributed Graph Computation for Machine Learning...................................... 669 Wencong Xiao, Beihang University and Microsoft Research; Jilong Xue, Peking University and Microsoft Research; Youshan Miao, Microsoft Research; Zhen Li, Beihang University and Microsoft Research; Cheng Chen and Ming Wu, Microsoft Research; Wei Li, Beihang University; Lidong Zhou, Microsoft Research Network Verification and Debugging Correct by Construction Networks Using Stepwise Refinement. .................................... 683 Leonid Ryzhyk, VMware Research; Nikolaj Bjørner, Microsoft Research; Marco Canini, King Abdullah University of Science and Technology (KAUST ); Jean-Baptiste Jeannin, Samsung Research America; Cole Schlesinger, Barefoot Networks; Douglas B. Terry, Amazon; George Varghese, University of California, Los Angeles Verifying Reachability in Networks with Mutable Datapaths. ...................................... 699 Aurojit Panda, University of California, Berkeley; Ori Lahav, Max Planck Institute for Software Systems (MPI- SWS); Katerina Argyraki, École Polytechnique Fédérale de Lausanne (EPFL); Mooly Sagiv, Tel Aviv University; Scott Shenker, University of California, Berkeley, and International Computer Science Institute Automated Bug Removal for Software-Defined Networks.......................................... 719 Yang Wu, Ang Chen, and Andreas Haeberlen, University of Pennsylvania; Wenchao Zhou, Georgetown University; Boon Thau Loo, University of Pennsylvania Delta-net: Real-time Network Verification Using Atoms. .......................................... 735 Alex Horn, Fujitsu Labs of America; Ali Kheradmand, University of Illinois at Urbana–Champaign; Mukul Prasad, Fujitsu Labs of America
Page 1 and 2: conference proceedings ISBN 978-1-9
Page 4 and 5: Proceedings of NSDI ’17: 14th USE
Page 6 and 7: NSDI ’17: 14th USENIX Symposium o
Page 10: Message from the NSDI ’17 Program
Page 13 and 14: • Security. Before reading input
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Page 40 and 41: not at the cost of the throughput o
Page 42 and 43: ceedings of the 9th USENIX Conferen
Page 44: [57] SCHROEDER, B., LAGISETTY, R.,
Page 47 and 48: materialized streams. Unlike stream
Page 49 and 50: "sequencers": 10.0.0.1, "segments":
Page 51 and 52: class User { String name; String pa
Page 53 and 54: class CSMRMap implements Map { fina
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[20] Robert Escriva, Bernard Wong,
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Curator: Self-Managing Storage for
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Figure 1: Cluster architecture and
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finds all the extent groups that ha
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Fraction of Clusters 1 0.8 0.6 0.4
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decisions on where to put a piece o
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use negative latencies, 18 i.e., th
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References [1] Q-learning with Neur
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D ML-driven Policies Workloads We u
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Abstract Evaluating the Power of Fl
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cannot guarantee that forwarded pac
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Estimate Error (%) 100 80 60 40 20
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since the last update as t ∆ . Be
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CDF 1 0.8 0.6 RCP 0.4 RCP-Approx XC
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perform RCP for long flows and perf
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References [1] AL-FARES, M., RADHAK
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A Building Blocks and their Use in
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APUNet: Revitalizing GPU as Packet
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Excavator CPU L1 Cache L2 Cache GCN
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CPU GPU RAM CPU GPU RAM OS CPU / Di
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W RQ CPU Master M Data Queue Worker
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Packet latency (us) 18 15 12 9 6 3
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Throughput (Gbps) 20 15 10 5 0 CPU
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References [1] Amazon. https://www.
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Stateless Network Functions: Breaki
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2 Motivation Simply running virtual
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Network Function State Key Value Ac
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network-wide capabilities, and sing
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create or destroy network functions
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cate with RAMCloud is higher than I
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References [1] Algo-logic systems.
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[43] R. Potharaju and N. Jain. Demy
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mOS: A Reusable Networking Stack fo
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eassembling bi-directional bytestre
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To detect the free-riding attack, w
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(a) (b) (c) seq x Receive buffer si
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ever enough flow data is reassemble
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Throughput (Gbps) 60 50 40 30 20 10
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8 Acknowledgments We would like to
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[56] S. Peter, J. Li, I. Zhang, D.
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Appendix B 1 // count # of packets
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132 14th USENIX Symposium on Networ
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microarchitectures. We ask ourselve
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ing, generating certificates, and c
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User process (Tor application) Tor-
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are SGX-enabled. However, we also s
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Data structure Tor Network-level ad
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Bandwidth (KB/s) 600 Probability (%
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[6] Intel Software Guard Extensions
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[61] F. Schuster, M. Costa, C. Four
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ViewMap: Sharing Private In-Vehicle
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samples. Time-series analysis on lo
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time t 1 time t 60 A B VP v B VP x
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Platform Blur time I/O time Frame r
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!""#$%"&'()*' :%,-'45.()*;' 100% 20
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Vehicle 1 Vehicle 2 Vehicle 1 Vehic
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References [1] Top Rider. 2015. Res
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A System to Verify Network Behavior
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OpenSSL and BoringSSL. Verification
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PQ PT PR ! "#$ 1-:$'HF7$5 PS Figure
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void Client ( int x, int y, int iv)
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●●●●●●●●●●●
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●●●●●●● ● ●●●
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References [1] B. Anderson, S. Paul
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scheduler thread and one or more wo
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DEFINE_MODEL (void , AES_encrypt ,
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int main () { unsigned int x, y; MA
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processing load of a large MIMO sys
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Find the path with the minimum c(s
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15 th 12 th 13th 16th 8 th 6 th 10
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§3), the corresponding overhead ca
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Througput (Mbit/s) (bars) Geosphere
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throughput gains for the same numbe
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Units. European Wireless Conference
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Facilitating Robust 60 GHz Network
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AoD Tx array Reflecting point AoA R
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Algorithm 1 Virtual Beamforming 1:
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Reflector Reflector Rx’’’ Rx
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True AoA/AoD (deg.) 360 300 240 180
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360 300 LOS 0 -5 240 -10 180 -15 12
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12. References [1] B. Wire, “FCC
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Skip-Correlation for Multi-Power Wi
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Figure 4: Example showing need for
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on a basic property of correlation-
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Figure 9: Running sum block in Schm
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A B B can sense A +3dB a) Experimen
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Probability 10 0 10 -1 η + 10 -2
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References [1] Aironet 1570 Series
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µ(C) = { LE 2 N /2 (LE S ) 2 /4 +
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FM Backscatter: Enabling Connected
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ter techniques described above and
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signal shown in Fig. 3. If we norma
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four FM stations. These plots confi
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Bit-error rate 0.35 0.3 0.25 0.2 0.
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PESQ score -20 dBm -30 dBm -40 dBm
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(a) (b) Figure 15: Talking Poster a
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[16] Silicon labs si4713-b30-gmr. h
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Prio: Private, Robust, and Scalable
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Privacy. Prio provides f -privacy,
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4.2 Constructing SNIPs To run the S
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the servers can execute the verific
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Workstation Phone Field size: 87-bi
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Client time (s) Lower is better. 10
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(a) RAPPOR [57] provides differenti
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[27] Brickell, J., and Shmatikov, V
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[78] Janosi, A., Steinbrunn, W., Pf
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there exists an efficient simulator
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arithmetic confirm that σ i is a s
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where y i is the true value associa
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Opaque: An Oblivious and Encrypted
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other process on the same processor
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Note that Opaque protects most cons
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all records that have the same grou
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SQL Query Opaque Logical Plan SELEC
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Patient SELECT FROM WHERE ⨝ ⨝ D
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Normalized runtime 10 2 10 1 10 0 1
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References [1] AMPlab, University o
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Splinter: Practical Private Queries
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answer = r1 + r2 ItemId Price ItemI
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true on an interval of the range of
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putation time is O(nlogn) and the c
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Dataset Query Desc. FSS Scheme Inpu
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Figure 12: Per-core throughput of v
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Acknowledgements We thank our anony
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[55] R. J. Rosen. Study: Consumers
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or desirable. Instead, we created a
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IGMP Snooping [3], that we don’t
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On the inbound direction, the layer
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updated to take as input a FlowID a
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VFP from the stack, uninstall VFP (
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however, that our controllers neede
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[22] Overview of Single Root I/O Vi
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switch sends a notification to the
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messages in bounded time - this is
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Control Application Controller Prox
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Network Event Controller 1 Aware Aw
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1 0.8 SCL Consensus 1 0.8 SCL Conse
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CDF 1 0.8 0.6 0.4 SCL Consensus Tra
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References [1] A. Akella. Experimen
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a single path to ensure safety poli
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Robust validation of network design
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not discuss this extensively, our f
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(F)max v,λ,x ∑ t,i≠t d it (v i
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The robust optimization literature
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Maximum Link Utilization (MLU) 1.50
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Maximum Link Utilization (MLU) 3.00
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A Appendix Proof of Proposition 1:
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Experience with a globally-deployed
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Encoding, Fast and Slow: Low-Latenc
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At the time of writing, AWS Lambda
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computational power (TFLOPS) 10 9 8
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initializes the decoder’s state:
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threads start 25 s 50 s 75 s 100 s
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average bitrate (Mbit/s) 20 25 30 3
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er 9, 2015). http://techblog.netfli
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Live Video Analytics at Scale with
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1 " name ": " LicensePlate ", 2 " t
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Deep Neural Network (DNN) Classifie
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6 U Q 3 query 1 query 2 0 0 Q M 1 0
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We first distribute the query resou
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0.95 0.90 0.85 0.80 Quality 1.00 La
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Scheduling Time (s) 6 5 4 3 2 Numbe
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[38] G. Cormode, M. Garofalakis, P.
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Pytheas: Enabling Data-Driven Quali
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JoinTime (ms) 4000 3500 3000 2500 2
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Frontend Backend Frontend (a)$Logic
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6.1 Requirements The Pytheas system
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Control*decision* [Features,Decisio
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Throughput (Thousand RPS) 180 160 1
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[6] How a/b testing works. http://g
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Let it Flow: Resilient Asymmetric L
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Scheme Granularity Information need
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3 Design and Hardware Implementatio
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Probability 0.5 0.45 0.4 0.35 0.3 0
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FCT (ms) 15 10 ECMP CONGA Let
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(e.g. packet loss, increased delay)
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References [1] M. Al-Fares, A. Louk
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Flowtune: Flowlet Control for Datac
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utility 2.0 1.5 1.0 0.5 0.0 0 10 20
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lb lb lb lb fb fb fb fb lb src fb f
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Throughput (Gbps) 10.0 7.5 5.0 2.5
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DCTCP Flowtune pFabric sfqCoDel XCP
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over−allocation (Gbps) 200 150 10
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gathered statistics, and at any giv
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[18] GREENBERG, A., HAMILTON, J. R.
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programmed scheme (Figure 1). The r
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For simplicity of emulation, all ab
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3.5 Flexplane APIs Flexplane expose
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y two. We run DropTail both in Flex
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6 DCTCP 20 ● 8 Normalized FCT 5 4
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Throughput (Gbits/s) 125 100 75 50
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[14] W. Bai, L. Chen, K. Chen, D. H
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I Can’t Believe It’s Not Causal
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Buffered Replicated Writes 1200 100
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# c l i t s i s t h e c l i e n t
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T 1 : s(1) r(x) w(y=10) c(2) T 3 :
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dinator recovery [32, 64]. Upon det
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Goodput (million ops/s) 1.4 1.2 1 0
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[5] ADYA, A. Weak Consistency: A Ge
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[65] ZHANG, Y., POWER, R., ZHOU, S.
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CherryPick: Adaptively Unearthing t
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Cost model: The cloud charges users
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leaving the interval between ⃗x 1
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Figure 6: Architecture of CherryPic
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(a) Running cost (b) S
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Figure 13: CherryPick learns dimini
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References [1] Apache Spark the fas
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AdaptSize: Orchestrating the Hot Ob
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Varnish Nginx AdaptSize SIZE-OPT +9
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average and 75% higher in some case
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HK trace US trace Total Requests 45
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Throughput (Gbps) 20 10 0 AdaptSize
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Byte Hit Ratio 1.00 0.75 0.50 0.25
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Appendix A Proof of Theorem 1 The r
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[37] JELENKOVIĆ, P. R., AND RADOVA
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Bringing IoT to Sports Analytics Ma
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Figure 2: Ball instrumentation: (a)
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Angle (degrees) 50 0 -50 Groundtrut
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jectory (derived from ViCon). The p
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(For example, if the integer ambigu
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Cumulative Angle (degrees) 4000 300
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[2] adidas micoach smart ball. http
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FarmBeats: An IoT Platform for Data
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as shown in Table 1), farms do not
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γ Minimum Data-Gap Parameters O T
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Front side (a) Omni-directional dro
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4 7.5 60 3.5 7 59 3 6.5 58 2.5 6 57
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3 4 5 Video Length (min) (a) (b) (c
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REFERENCES [1] IEEE 802.11af: https
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sist in UAV path planning. On a hig
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hand can block the signal. Said dif
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User rotated her head AP SNR (in dB
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Leakage TX to RX(in dB) -50 -60 -70
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MoVR estimates φ AP . To estimate
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CDF 1 0.8 0.6 0.4 0.2 0 0 200 400 6
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10 No Mirror 30 8 Concluding Remark
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[44] SUR, S., VENKATESWARAN, V., ZH
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that exhibit high degree of collect
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time (s) 35 30 25 20 15 10 5 Lab us
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Figure 6: A heatmap of the collecti
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Figure 9: WebGaze architecture. 5.2
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Alternative # WebGaze # WebGaze # W
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8 Discussions There are several tec
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[27] GAZEPOINTER. Control mouse cur
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RAIL: A Case for Redundant Arrays o
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(a) 10Gbps multi-mode (b) 10Gbps si
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Figure 7: Price and reach of standa
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The speed of the above algorithm de
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Figure 10: Our 10G-SR testbed. We s
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(a) 10Gbps (b) 40Gbps Figure 14: To
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Acknowledgments We thank Keren Berg
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Today, data center operators chose
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Enabling Wide-spread Communications
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copies of data to be stored in diff
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40dB 30dB 20dB 0 5 10 15 # nodes on
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Figure 8: The OWS box we implemente
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Mbps Mbps 4×10 5 2×10 5 (a) 0 0s
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1.0 0.5 0 1.0 0.5 0 (a) % Full Bise
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References [1] AL-FARES, M., LOUKIS
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internet applications. ACM SIGCOMM
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when the capacity of basemesh is in
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the topological regularity of Faceb
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Figure 1: High-level system overvie
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metrics can be used to find under-p
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computes a lightweight statistic fo
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0.80 0.60 0.40 0.20 0.05 10 40 60 1
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the majority of traffic is routed a
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8. REFERENCES [1] ab - Apache HTTP
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Syscall latency (nsec) 10 8 10 7 10
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Syscall latency (nsec) 10 7 10 6 10
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Consequently, models can be modifie
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over, the low and bounded latency d
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Latency (¹s) Latency (¹s) 35000 3
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Throughput (10K qps) Latency (ms) 1
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Cumulative Average Error 0.5 0.4 0.
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duced by Clipper’s layered archit
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References [1] M. Abadi, A. Agarwal
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Gaia: Geo-Distributed Machine Learn
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Network Bandwidth (Mb/s) 1000 900 8
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ization factorizes X into factor ma
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Normliaed Cost 2.5 2 1.5 1 0.5 0 Ma
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Acknowledgments We thank our shephe
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[55] C. Olston, J. Jiang, and J. Wi
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〈 x˜ t − x ∗ , g˜ t 〉 = 1
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inaccurate, values in order to get
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Memcached ETC SYS 100% 95.83 93.87
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Container 1 … Container N Infinis
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Each RDMA dispatch queue has a limi
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We have implemented INFINISWAP as a
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workloads with different rates of S
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Completion time (s) 5000 4000 3000
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[3] Amazon EC2 Pricing. https://aws
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[59] F. Mietke, R. Rex, R. Baumgart
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Send QUERY_MEM messages Receive FRE
Page 680 and 681:
TUX 2 : Distributed Graph Computati
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Model: User 0 rƸ ui = q T i p u Us
Page 684 and 685:
dates. This is because they are sca
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ExchangeStage:: Exchange(v_doc, v_w
Page 688 and 689:
Time per iteration (s) 140 120 100
Page 690 and 691:
Time per iteration (s) 1000 100 10
Page 692 and 693:
[3] Petuum v0.93. https://github.co
Page 694 and 695:
Correct by Construction Networks us
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zone 1 security policy core zone 3
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RouterZoneIn RouterZoneOut zone cor
Page 700 and 701:
possible outputs of ^R on this pack
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vm1 vm2 vm3 vnet 1 vrouter vm4 vm5
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Scale Hosts Switches NetKAT Policy
Page 706 and 707:
References [1] M. Al-Fares, A. Louk
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given in terms of: • a packet p
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Verifying Reachability in Networks
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function with a set of packets to f
Page 714 and 715:
Intrusion Prevention Systems We con
Page 716 and 717:
parallel and origin-agnostic middle
Page 718 and 719:
Time (seconds) 500 450 400 350 300
Page 720 and 721:
1000 1000 Subnet 1 100 100 SB Time
Page 722 and 723:
References [1] AMAZON. Amazon EC2 S
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[56] VELNER, Y., ALPERNAS, K., PAND
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uration parameters that control, e.
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C Formal Definition of Slices Given
Page 730 and 731:
Automated Bug Removal for Software-
Page 732 and 733:
1 FlowTable(@Swi,Hdr,Prt) :- Packet
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h1 Tuple(@C,Tab,Val1,Val2) :- Base(
Page 736 and 737:
function GENERATEREPAIRCANDIDATES(P
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7(v1) FlowTable(@Swi,Hdr,Prt) :- Pa
Page 740 and 741:
Query description Result Q1 H20 is
Page 742 and 743:
Q1 Q2 Q3 Q4 Q5 Trema (Ruby) 7/2 10/
Page 744:
[33] D. Logothetis, S. De, and K. Y
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To address this problem, this paper
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s 2 s 3 r 1 r 2 r 3 s 1 r 4 s 4 (a)
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in the graph on which link(r) is in
Page 753 and 754:
Algorithm 3 Compute all-pairs reach
Page 755 and 756:
CDF 1 0.8 0.6 0.4 0.2 0 Berkley INE
Page 757 and 758:
48, 26, 35, 17, 33] and online [27,
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[41] NUNES, B. A. A., MENDONCA, M.,
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