2017 Spring Retreat Talks/retreat... · 2019-03-11 · §GFS + MapReduce → Big Data (large-scale...

2017 Spring Retreat

2017 Spring Retreat:Lab Overview and Update

John OusterhoutFaculty Director

June 8, 2017 Platform Lab Overview and Update Slide 3

Thank You, Sponsors!


Platform Lab Faculty

JohnOusterhout

(Fac. Director)

MendelRosenblum

KeithWinstein

GuruParulkar

(Exec. Director)

BillDally

PhilLevis

SachinKatti

ChristosKozyrakis

NickMcKeown

PeterBailis

MateiZaharia

BalajiPrabhakar


What is a Platform?● General-purpose hardware or software substrate

● Simplifies construction of a class of applications(or higher-level platforms)§ Solves common problems§ Usually introduces (simplifying) restrictions

● Example: MapReduce§ Applications: large-scale analytics§ Problems solved: hides latency, handles slow/crashed servers§ Simplifying restrictions: two-phase decomposition, large sequential

accesses


Platform Lab Vision● New platforms enable new applications:

§ Relational databases → Enterprise applications§ HTTP + HTML + JavaScript → Internet commerce§ GFS + MapReduce → Big Data (large-scale analytics)§ Smart phones + GPS → Google Maps, Uber, …

● Mission:§ Define the next generation of platforms§ Stimulate new classes of applications§ One or two flagship projects at any given time

● Current focus: platforms for large-scale control§ Self-Driving Networks and Datacenters§ Granular Computing


Platform Lab News● NSF Expedition proposal still under review

§ Notification about current hurdle in August§ Final hurdle: reverse site visit to D.C. in November

● Continuing evolution of platforms for Big Control§ Granular computing platform§ Big Control Platform → Self-driving networks

● Christos Kozyrakis:§ Elected ACM Fellow§ Promoted to Professor


Recent/Imminent Graduates

Dinesh Bharadia ??? Academia

Song Han Efficient Methods and Hardware for AcademiaDeep Learning

Omid Mashayekhi Fast, Distributed Computations in Googlethe Cloud


Big Control● How to manage enormous swarms of devices

● Devices may be mobile…§ 1M+ self-driving cars in the morning commute§ 10,000+ indoor drones in the distribution center§ 1,000+ drones and automated vehicles for disaster recovery§ Smart phones in a cellular network

● … or stationary§ In-road sensors, traffic lights for a metropolitan area§ Servers, switches, and routers in a large datacenter


Big Control, cont’d● Interesting properties:

§ Scale§ Collaboration§ Latency

● Control will become more centralized:§ Easier application development§ More powerful features

● Integrate large back-end datasets● Apply large-scale machine learning

§ More robust!

● Lab goal: define and enable the Big Control paradigm


At the February Review…Focus on two platforms:● Granular Computing:

§ Infrastructure for large-scale control applications§ Efficient execution of large numbers of very small tasks§ Generic (useful for more than just control)

● Big Control Platform:§ Framework for large-scale control applications§ Control-specific features such as

● Data ingestion & fusion● Declarative planning● Deep reinforcement learning

§ Thinking in terms of mobile devices such as drones, self-driving cars

● But, control of networks emerging as interesting challenge


Self-Driving Networks● Challenge: control of networks● Devices under control:

§ Networking infrastructure (switches, routers, mobile infrastructure, etc.)§ Servers in datacenters

● Must support§ Large scale (thousands of devices)§ Collaborative behaviors§ Low latency

● Interested faculty:§ Katti§ Prabhakar§ Rosenblum


Self-Driving Networks, cont’d● Control specified at a higher level

§ What you want (declarative)§ Not how to get it

● Network monitors, controls itself:§ Sense to collect data on behavior, needs§ Infer global state, bottlenecks, causes of

symptoms§ Learn best control behaviors (e.g. deep

reinforcement learning)§ Forecast future behaviors, impact of

changes§ Control network configuration, policies

● Heavy use of machine learning

Sense

Infer Learn Forecast

Control


Self-Driving Networks Activity● Accurate telemetry: reconstruct datacenter network state without

hardware sensors in switches§ Bottleneck switch delays§ Link utilizations

● Accurate clock synchronization at scale§ Within 10s of ns across a datacenter§ Software-only solution

● Visualization engine for performance data● ForeC: data-driven control in dense mobile networks


Granular Computing● Big Data: process data in large sequential chunks● Big Control: process data in very small chunks

§ Example: 10,000 devices, updates all-to-all every second§ Each device notification triggers internal events for fusion, inference

● Granular computing:§ Support tasks lasting 10ms → 1µs§ Efficient instantiation, communication§ Short duration => large numbers§ Highly elastic§ Must coexist with traditional large tasks

● Related trends: micro-services, lambdas


Granular Computing Platform

Cluster Scheduling

Low-LatencyRPC

ScalableNotifications

Thread/AppMgmt

Low-LatencyStorage

HardwareAccelerators

ApplicationsLevis

Kozyrakis

Ousterhout

Winstein

Ousterhout

Kozyrakis

Zaharia


Granular Computing Activity● Arachne thread library:

§ Cross-core thread creation in 200ns§ Core allocation in 10-20µs

● Homa: new transport protocol for datacenters§ Enables low latency for short messages even under high loads

● Canary: high throughput cluster scheduling using task recipes:§ Schedule >100,000 tasks/second with 10% of one core§ Scheduling latency < 10µs

● ExCamera: using AWS Lambda for video processing§ 14x reduction in latency for 4K video encoding

● Started work on gg: granular build accelerator


Other Recent Results

● Tock: OS for securing embedded devices§ Collaborations with U.C. Berkeley, Michigan, Chalmers, several companies

● Hardware accelerator for gene sequence assembly§ 1000x speedups§ Microsoft deploying on FPGA Azure cloud

● Launched xRAN:§ Non-profit consortium for specifying architecture of 5G mobile networks


Thursday Agenda1:00 Welcome, Introductions, Platform Lab Overview John Ousterhout1:45 Self-Driving Networks Balaji Prabhakar2:10 Network Clock Synchronization Yilong Geng2:35 Deep Reinforcement Learning for Content Delivery Sachin Katti3:00 Break3:30 Granular computing for low-latency compression Keith Winstein

and compilation3:55 Arachne: Core-Aware Thread Management Henry Qin4:20 A coarse-grain reconfigurable architecture for analytics Raghu Prabhakar4:45 Breakout Discussions5:45 Reception/posters7:00 Dinner9:00 Board games


Friday Agenda9:00 Reports from breakout discussions9:25 Efficient Methods for Deep Neural Networks Song Han9:50 Predictable Stream Processing Manu Bansal10:15 Can Great Programmers Be Taught? John Ousterhout10:40 Recreation and Informal Conversations12:00 Lunch1:00 BCP for Datacenters Matei Zaharia1:25 Multicore Optimizations for Large Scale Web Services Grant Ayers1:50 Homa: A Receiver-Driven Low-Latency Transport Protocol Behnam Montazeri

Using Network Priorities2:15 CGAR: Strong Consistency without Synchronous Replication Seo Jin Park2:40 Break3:00 Machine Learning Methods for Networking Zi Yin3:25 Accelerating Genomics Computations 1000x with Hardware Yatish Turakhia3:50 Industrial Feedback


Breakout Sessions● Smaller group discussions this afternoon● 5-minute reports tomorrow morning● Topics:

§ Self-driving networks (Balaji Prabhakar)§ Serverless computing (Keith Winstein)§ Clean slate platform for granular computing (John Ousterhout)

● Additional proposals welcome§ Talk to John or Guru

Questions/Discussion


2017 Spring Retreat Talks/retreat... · 2019-03-11 · §GFS + MapReduce → Big Data (large-scale...

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