🐣
Reading List
  • Starting point
  • Reference list
  • PhD application guidelines
  • Big Data System
    • Index
      • Architecture
        • Storage
          • Sun's Network File System (NFS)
      • Execution Engine, Resource Negotiator, Schedulers
        • Execution Engines
        • Resource Negotiator
        • Schedulers
      • Machine Learning
      • SQL Framework
      • Stream Processing
      • Graph Processing
      • Potpourri: Hardware, Serverless and Approximation
  • Operating System
    • Index
      • OSTEP
        • Virtualization
          • CPU Abstraction: the Process
          • Interlude: Process API
          • Mechanism: Limited Direct Execution
        • Intro
  • Networking
    • Index
      • CS 294 (Distributed System)
        • Week 1 - Global State and Clocks
          • Distributed Snapshots: Determining Global States of Distributed Systems
          • Time, Clocks, and the Ordering of Events in a Distributed System
        • Weak 5 - Weak Consistency
          • Dynamo: Amazon's Highly Available Key-value Store
          • Replicating Data Consistency Explained Through Baseball
          • Managing update conflicts in Bayou, a weakly connected replicated storage system
      • CS 268 (Adv Network)
        • Intro
        • Internet Architecture
          • Towards an Active Network Architecture
          • The Design Philosophy of the DARPA Internet Protocols
        • Beyond best-effort/Unicast
          • Core Based Trees (CBT)
          • Multicast Routing in Internetworks and Extended LANs
        • Congestion Control
        • SDN
          • ONIX: A Distributed Control Platform for Large-scale Production Networks
          • B4: Experience with a Globally-Deployed Software Defined WAN
          • How SDN will shape networking
          • The Future of Networking, and the Past of Protocols
        • Datacenter Networking
          • Fat tree
          • Jellyfish
        • BGP
          • The Case for Separating Routing from Routers
        • Programmable Network
          • NetCache
          • RMT
        • Datacenter Congestion Control
          • Swift
          • pFabric
        • WAN CC
          • Starvation (Sigcomm 22)
        • P2P
          • Design and Evaluation of IPFS: A Storage Layer for the Decentralized Web
          • The Impact of DHT Routing Geometry on Resilience and Proximity
        • Net SW
          • mTCP
          • The Click modular router
        • NFV
          • Performance Interfaces for Network Functions
          • Making Middleboxes Someone Else's Problem: Network Processing as a Cloud Service
        • Ethics
          • On the morals of network research and beyond
          • The collateral damage of internet censorship by DNS injection
          • Encore: Lightweight Measurement of Web Censorship with Cross-Origin Requests
        • Low Latency
          • Aquila: A unified, low-latency fabric for datacenter networks
          • cISP: A Speed-of-Light Internet Service Provider
        • Disaggregation
          • Network Requirements for Resource Disaggregation
        • Tenant Networking
          • Invisinets
          • NetHint: While-Box Networking for Multi-Tenant Data Centers
        • Verification
          • A General Approach to Network Configuration Verification
          • Header Space Analysis: Static Checking for Networks
        • ML
          • SwitchML
          • Fast Distributed Deep Learning over RDMA
      • Computer Networking: A Top-Down Approach
        • Chapter 1. Computer Network and the Internet
          • 1.1 What Is the Internet?
          • 1.2 The Network Edge
          • 1.3 The Network Core
        • Stanford CS144
          • Chapter 1
            • 1.1 A Day in the Life of an Application
            • 1.2 The 4-Layer Internet Model
            • 1.3 The IP Service Model
            • 1.4 A Day in the Life of a Packet
            • 1.6 Layering Principle
            • 1.7 Encapsulation Principle
            • 1.8 Memory layout and Endianness
            • 1.9 IPv4 Addresses
            • 1.10 Longest Prefix Match
            • 1.11 Address Resolution Protocol (ARP)
            • 1.12 The Internet and IP Recap
      • Reading list
        • Elastic hyperparameter tuning on the cloud
        • Rethinking Networking Abstractions for Cloud Tenants
        • Democratizing Cellular Access with AnyCell
        • Dagger: Efficient and Fast RPCs in Cloud Microservices in Near-Memory Reconfigurable NICs
        • Sage: Practical & Scalable ML-Driven Performance Debugging in Microservices
        • Faster and Cheaper Serverless Computing on Harvested Resources
        • Network-accelerated Distributed Machine Learning for Multi-Tenant Settings
        • User-Defined Cloud
        • LegoOS: A Disseminated Distributed OS for Hardware Resource Disaggregation
        • Beyond Jain's Fairness Index: Setting the Bar For The Deployment of Congestion Control Algorithms
        • IncBricks: Toward In-Network Computation with an In-Network Cache
  • Persistence
    • Index
      • Hardware
        • Enhancing Lifetime and Security of PCM-Based Main Memory with Start-Gap Wear Leveling
        • An Empirical Guide to the Behavior and Use of Scalable Persistent Memory
  • Database
    • Index
  • Group
    • WISR Group
      • Group
        • Offloading distributed applications onto smartNICs using iPipe
        • Semeru: A memory-disaggregated managed runtime
      • Cache
        • Index
          • TACK: Improving Wireless Transport Performance by Taming Acknowledgements
          • LHD: Improving Cache Hit Rate by Maximizing Hit Density
          • AdaptSize: Orchestrating the Hot Object Memory Cache in a Content Delivery Network
          • Clustered Bandits
          • Important Sampling
          • Contexual Bandits and Reinforcement Learning
          • Reinforcement Learning for Caching with Space-Time Popularity Dynamics
          • Hyperbolic Caching: Flexible Caching for Web Applications
          • Learning Cache Replacement with CACHEUS
          • Footprint Descriptors: Theory and Practice of Cache Provisioning in a Global CDN
      • Hyperparam Exploration
        • Bayesian optimization in cloud machine learning engine
    • Shivaram's Group
      • Tools
      • Group papers
        • PushdownDB: Accelerating a DBMS using S3 Computation
        • Declarative Machine Learning Systems
        • P3: Distributed Deep Graph Learning at Scale
        • Accelerating Graph Sampling for Graph Machine Learning using GPUs
        • Unicorn: A System for Searching the Social Graph
        • Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless
        • Garaph: Efficient GPU-accelerated GraphProcessing on a Single Machine with Balanced Replication
        • MOSAIC: Processing a Trillion-Edge Graph on a Single Machine
        • Fluid: Resource-aware Hyperparameter Tuning Engine
        • Lists
          • Wavelet: Efficient DNN Training with Tick-Tock Scheduling
          • GPU Lifetimes on Titan Supercomputer: Survival Analysis and Reliability
          • ZeRO-Infinity and DeepSpeed: Unlocking unprecedented model scale for deep learning training
          • ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning
          • KungFu: Making Training inDistributed Machine Learning Adaptive
        • Disk ANN
      • Queries Processing
        • Building An Elastic Query Engine on Disaggregated Storage
        • GRIP: Multi-Store Capacity-Optimized High-Performance NN Search
        • Milvus: A Purpose-Built Vector Data Management System
        • Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings
        • Billion-scale Approximate Nearest Neighbor Search
        • DiskANN: Fast accurate billion-point nearest neighbor search on a single node
        • KGvec2go - Knowledge Graph Embeddings as a Service
    • Seminar & Talk
      • Berkeley System Seminar
        • RR: Engineering Record and Replay for Deployability
        • Immortal Threads: Multithreaded Event-driven Intermittent Computing on Ultra-Low-Power Microcontroll
      • Berkeley DB Seminar
        • TAOBench: An End-to-End Benchmark for Social Network Workloads
      • PS2
      • Sky Seminar Series
        • Spring 23
          • Next-Generation Optical Networks for Emerging ML Workloads
      • Reading List
        • Confluo: Distributed Monitoring and Diagnosis Stack for High-speed Networks
        • Rearchitecting Linux Storage Stack for µs Latency and High Throughput
        • eBPF: rethinking the linux kernel
        • BPF for Storage: An Exokernel-Inspired Approach
        • High Velocity Kernel File Systems with Bento
        • Incremental Path Towards a Safe OS Kernel
        • Toward Reconfigurable Kernel Datapaths with Learned Optimizations
        • A Vision for Runtime Programmable Networks
        • The Demikernel and the future of kernal-bypass systems
        • Floem: A programming system for NIC-accelerated network applications
        • High Performance Data Center Operating Systems
        • Leveraging Service Meshes as a New Network Layer
        • Automatically Discovering Machine Learning Optimizations
        • Beyond Data and Model Parallelism for Deep Neural Networks
        • IOS: Inter-Operator Scheduler for CNN Acceleration
        • Building An Elastic Query Engine on Disaggregated Storage
        • Sundial: Fault-tolerant Clock Synchronization for Datacenters
        • MIND: In-Network Memory Management for Disaggregated Data Centers
        • Understanding host network stack overheads
        • From Laptop to Lambda: Outsourcing Everyday Jobs to Thousands of Transient Functional Containers
        • Redesigning Storage Systems for Future Workloads Hardware and Performance Requirements
        • Are Machine Learning Cloud APIs Used Correctly?
        • Fault-tolerant and transactional stateful serverless workflows
      • Reading Groups
        • Network reading group
          • Recap
          • ML & Networking
            • Video Streaming
              • Overview
              • Reducto: On-Camera Filtering for Resource Efficient Real-Time Video Analytics
              • Learning in situ: a randomized experiment in video streaming
              • SENSEI: Aligning Video Streaming Quality with Dynamic User Sensitivity
              • Neural Adaptive Video Streaming with Pensieve
              • Server-Driven Video Streaming for Deep Learning Inference
            • Congestion Control
              • ABC: A Simple Explicit Congestion Controller for Wireless Networks
              • TCP Congestion Control: A Systems Approach
                • Chapter 1: Introduction
              • A Deep Reinforcement Learning Perspective on Internet Congestion Control
              • Pantheon: the training ground for Internet congestion-control research
            • Other
              • On the Use of ML for Blackbox System Performance Prediction
              • Marauder: Synergized Caching and Prefetching for Low-Risk Mobile App Acceleration
              • Horcrux: Automatic JavaScript Parallelism for Resource-Efficient Web Computation
              • Snicket: Query-Driven Distributed Tracing
            • Workshop
          • Homa: A Receiver-Driven Low-Latency Transport Protocol Using Network Priorities
        • DB reading group
          • CliqueMap: Productionizing an RMA-Based Distributed Caching System
          • Hash maps overview
          • Dark Silicon and the End of Multicore Scaling
        • WISR
          • pFabric: Minimal Near-Optimal Datacenter Transport
          • Scaling Distributed Machine Learning within-Network Aggregation
          • WCMP: Weighted Cost Multipathing for Improved Fairness in Data Centers
          • Data center TCP (DCTCP)
      • Wisconsin Seminar
        • Enabling Hyperscale Web Services
        • The Lottery Ticket Hypothesis
        • External Merge Sort for Top-K Queries: Eager input filtering guided by histograms
      • Stanford MLSys Seminar
        • Episode 17
        • Episode 18
  • Cloud Computing
    • Index
      • Cloud Reading Group
        • Owl: Scale and Flexibility in Distribution of Hot Contents
        • RubberBand: cloud-based hyperparameter tuning
  • Distributed System
    • Distributed Systems Lecture Series
      • 1.1 Introduction
  • Conference
    • Index
      • Stanford Graph Learning Workshop
        • Overview of Graph Representation Learning
      • NSDI 2022
      • OSDI 21
        • Graph Embeddings and Neural Networks
        • Data Management
        • Storage
        • Preview
        • Optimizations and Scheduling for ML
          • Oort: Efficient Federated Learning via Guided Participant Selection
          • PET: Optimizing Tensor Programs with Partially Equivalent Transformations and Automated Corrections
      • HotOS 21
        • FlexOS: Making OS Isolation Flexible
      • NSDI 21
        • Distributed System
          • Fault-Tolerant Replication with Pull-Based Consensus in MongoDB
          • Ownership: A Distributed Futures System for Fine-Grained Tasks
          • Caerus: NIMBLE Task Scheduling for Serverless Analytics
          • Ship Computer or Data? Why not both?
          • EPaxos Revisited
          • MilliSort and MilliQuery: Large-Scale Data-Intensive Computing in Milliseconds
        • TEGRA: Efficient Ad-Hoc Analytics on Evolving Graphs
        • GAIA: A System for Interactive Analysis on Distributed Graphs Using a High-Level Language
      • CIDR 21
        • Cerebro: A Layered Data Platform for Scalable Deep Learning
        • Magpie: Python at Speed and Scale using Cloud Backends
        • Lightweight Inspection of Data Preprocessingin Native Machine Learning Pipelines
        • Lakehouse: A New Generation of Open Platforms that UnifyData Warehousing and Advanced Analytics
      • MLSys 21
        • Chips and Compilers Symposium
        • Support sparse computations in ML
      • SOSP 21
        • SmartNic
          • LineFS: Efficient SmartNIC offload of a distributed file system with pipeline parallelism
          • Xenic: SmartNIC-accelerated distributed transacitions
        • Graphs
          • Mycelium: Large-Scale Distributed Graph Queries with Differential Privacy
          • dSpace: Composable Abstractions for Smart Spaces
        • Consistency
          • Efficient and Scalable Thread-Safety Violation Detection
          • Understanding and Detecting Software Upgrade Failures in Distributed Systems
        • NVM
          • HeMem: Scalable Tiered Memory Management for Big Data Applications and Real NVM
        • Learning
          • Bladerunner: Stream Processing at Scale for a Live View of Backend Data Mutations at the Edge
          • Faster and Cheaper Serverless Computing on Harvested Resources
  • Random
    • Reading List
      • Random Thoughts
      • Hesse
      • Anxiety
  • Grad School
    • Index
      • Resources for undergraduate students
Powered by GitBook
On this page
  • Presentation
  • Presentation: Practical Record & Replay Debugging with rr

Was this helpful?

  1. Group
  2. Seminar & Talk
  3. Berkeley System Seminar

RR: Engineering Record and Replay for Deployability

https://www.usenix.org/conference/atc17/technical-sessions/presentation/ocallahan

PreviousBerkeley System SeminarNextImmortal Threads: Multithreaded Event-driven Intermittent Computing on Ultra-Low-Power Microcontroll

Last updated 2 years ago

Was this helpful?

Presentation

  • Topic: partial record and replay debugging with rr

  • Debugging nondeterminisim

    • Bugs, confuses the output of the system

    • Difficult to debug

  • Deterministic hardware

  • Sources of nondeterminism

    • Record inputs

    • Reply execution

  • Old idea

    • Nirvana, PinPlay, ReVirt, Jockey, ReSpec, Chronomancer, PANDA, Echo, FlashBack, ...

RR goals

  1. Easy to deploy: stock hardware (i.e. not customized), commodity OS, no kernel changes

  2. Low overhead

  3. Works on Firefox

  4. Small investment

RR design

Idea: user-space processes running in the linux, record all the input (system call results, signals) to those processes, reply those inputs, get the same process execution, would be able to replay and debug

  • No code instrumentation

  • Use modern HW/OS features

      • Linux API: ptrace

    • Data races: multiple CPU running the same time, one read and one write, can lead to non-deterministic result

      • Shared memory data access: limit to single core , manage context switches

    • Asynchronous event timing: HW performance counters

      • Signal runs at the right program states during replay

      • Idea: count the number of signals, deliver the signal after the signals

      • Doing this in HW, no code instrumentation

    • Trap on a subset of system calls: seccomp-bpf

      • Two traps, four context switches, system calls expensive

      • shim library: loaded into the process that we're tracing; part of the recording and replay; wrap the common system calls; after system calls, record the results into the buffer (periodically flush by the supervisor process)

        • Inject bpf predicate to the kernel

      • What happens if the system call blocks?

        • Schedule another thread to run

        • DESCHED perf event

          • Everytime a thread is put out of the core and put into the idle queue

          • Get the performance of it

    • Other issues

      • RDTSC

      • RDRAND

      • XGEBIN/XEND

      • CPUID

Performance

  • CP: recursive

  • Octane: javascript

  • HTMLTEST: firefox on html unit test

  • Sambatest

Also: reverse-execution debugging

Lessons

  • Replay performance matters

  • Session-cloning performance matters (checkpoint of the current system states)

    • Clonning processes via fork() seems cheaper than e.g. cloning VM state

  • In-process system-call interception is fragile

    • Applications make syscalls in strage states (bad TLS, insufficient stack, etc)

    • In-process interception code could be accidentally or maliciously subverted

    • Move this part into kernel?

  • OS design implications

    • Recording boundary should

      • Be stable, simple, documented API boundary

      • Also be a boundary for hardware performance counter measurement

  • ARM

    • Need hardware support to detect / compensate

    • Or binary rewriting

  • Related work

    • VM-level reply: heavyweight

    • Kernel-supported replay: hard to maintain

    • Pure user-space replay: instrumentation, higher overhead

    • Higher-level replay: more limited scope

    • Parallel replay: more limited scope, higher overhead

    • Hardware-supported parallel replay: nonexistent hardware

Conclusions

  • rr's apporach delivers a lot of vlaue

  • more research needed for multicore apporaches

  • lots of unexplored applications of record+replay

Questions

  • 1-thread-one-time: disappearance?

  • virtual system calls?

    • patch this to normal system call

  • application: undefined behavior?

    • fine, re-produce the exact execution during replay

  • deal with msi free

    • recording: deterministic behavior

    • recording locations of memory maps, use map fix to make sure that ...

  • applications that have randomizations?

    • exponential back-off?

    • random numbers are from some source, record and replay from the random number generations

  • move traces in between different machines is difficult?

    • trace format: pack

    • cpu ID

Presentation: Practical Record & Replay Debugging with rr

  • Non-determinism (debugging)

    • Test randomly failed, don't know why / how often

    • Diff tests running (linux opt/pgo/debug/...)

    • orange/red test has nothing to do with the change

  • Deterministic hardware

    • External sources of non-determinism

  • Building in the middle: record input

    • Non-determinisitc conditions

  • Replay execution

  • Old idea

    • ODR, PinPlay, ...

  • RR goals

    • Easy to deploy

    • Low overhead

    • Works on FF

    • Small investment (other work: binary instrumentation, OS kernel changes, hard to maintain and distribute)

  • Modern HW/OS features

    • Ptrace: one process monitors what is happening on the other one (system tracing)

      • Tracer / tracee

      • Single sys call: context switches (overhead)

        • I.e. get PID, read (cheap sys call)

    • Shared memory data races --> limit to single core

    • Async event timing --> HW performance counters (retired conditional branch)

      • When the tracee gets to the point, software interrupt

      • Instruction stream, runtime instrumentation

      • JIT

    • Trap on a subset of system calls

      • seccomp-bpf

        • Do the filtering when staying in the user-space

      • Conditions to be checked before context-switches

      • Recording in user-space

    • Sys call block

      • Look at the scheduled event and record them

    • Record all the memories

      • Same memory map in the same locations

    • Other issues

      • instructions that generate randomness in CPU

      • RDTSC: tell the .. to interrupt

      • Back them: same CPU

      • Now: ptrace to tell what the CPU is

  • Replay: can be fast (no context switches)

Another apporach: cloning the whole VM state

  • Capture the evolution of the memory?

    • See what's changing

    • Doing it as the process level

    • No need to re-record, but keep track of the changes

  • GDB: go backward

    • In forward execution, at diff points in time, fork() of replay

    • Backward: breakpoint, and then go into one of the fork()

  • Move this part into kernel

    • Painful: because they do all of these in the process

    • Some of the recording phase can maybe done in kernel

      • Security

      • Faster

  • could create snapshot but it's not what they're describing

Distributed system

  • Can we apply this kind of technique

  • Common bugs but hard to find

    • FlyMS (eurosys 19), samc (osdi 14)

    • Make the traces more interpretable?

    • Oathkeeper (OSDI 22)

    • etcd still gets constant flow of bug reports

  • Bottom line: many bugs reproducible by recreating external conditions (e.g., not OS thread timing dependent)

    • No memory race conditions, etc.

  • Now

    • Debugging distributed systems now

      • Collect per-machine logs

      • Virtually unify them

      • Guess root causes

    • A system that emits machine readable logs?

      • Logs --> reproduce the bugs

      • Reverse-execute the system from the bug location

        • Like in rr

  • Root cause analysis in distributed systems: prior work

    • Demi (NSDI 16) [minimize faulty executions of distributed systems)

    • FlyMC, SAMC

      • Collect per-node partial event orders

      • Use DPOR to recreate total order

    • I.e. RR needs to have exact recording, but not this one

    • STOA debugging tools for DS? limited to the types of systems, but not in general distributed system way (Define a general model for every distributed system that you have)

      • Ray: log file of outputs, per process

  • Message contents

    • Annotate each of the log? figure out from the message contents / payload?

    • Merging files? (where do you merge [problem])

    • Common goal of DS: common state machine ? to do this you need an exact ordering of the log ? network parition? [data inconsistency --> data corruption]