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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
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  • 6.1 Basic Technique: Limited Direct Execution
  • 6.2 Problem #1: Restricted Operations
  • 6.3 Problem #2: Switching Between Processes
  • 6.3 Concurrency?

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  1. Operating System
  2. Index
  3. OSTEP
  4. Virtualization

Mechanism: Limited Direct Execution

PreviousInterlude: Process APINextIntro

Last updated 4 years ago

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  • By time sharing the CPU, virtualization is achieved

    • Challenges

      • Performance: how can we implement virtualization without adding excessive overhead to the system?

      • Control: how can we run processes efficiently while retaining control over the CPU?

6.1 Basic Technique: Limited Direct Execution

Problem: program might do something harmful, and how is OS able to stop the program from running and switch to another process?

6.2 Problem #1: Restricted Operations

  • Restricted operations: I/O request to disk, or gaining access to more system resources like CPU or memory

    • system call is a procedure call, but hidden inside that procedure call is the trap instruction

  • Approach

    • User mode: code that runs in user mode is restricted in what it can do

    • Kernel mode: OS (or kernel) runs in, code can do what it likes, including privileged operations

    • System call: allows the kernel to carefully expose certain key pieces of functionality to user programs

      • Trap + return from trap (trap table)

  • system call number

    • A form of protection

  • LDE protocol

    • At boot time, kernel initializes the trap table, and the CPU remembers its location for subsequent use

    • When running a process, the kernel sets up a few things (e.g. allocating a node on the process list, allocating memory) before using a return-from-trap instruction to start the execution of the process; this switches the CPU to user mode and begins running the process

6.3 Problem #2: Switching Between Processes

Key: How can the OS regain control of the CPU so that it can switch between processes?

A Cooperative Approach: Wait for System Calls

  • yield system call

  • Or, when the applications do some illegal operations

A Non-Cooperative Approach: The OS Takes Control

  • How can the OS gain control of the CPU even if processes are not being cooperative? What can the OS do to ensure a rogue process does not take over the machine?

  • A timer interrupt (hardware feature)

    • Pre-configured interrupt handler in the OS runs, regain control of the CPU. Can stop and resume another one.

  • Saving and restoring context

    • Whether to continue running or switch to another one. Decision made by scheduler

      • If switch: executes a low-level piece of code which is context switch

        • save the general purpose registers, PC, and the kernel stack pointer of the currently-running process, and then restore said registers, PC, and switch to the kernel stack for the soon-to-be-executing process

6.3 Concurrency?

  • what happens when, during a system call, a timer interrupt occurs?

  • What happens when you’re handling one interrupt and another one happens? Doesn’t that get hard to handle in the kernel?

  • Methods

    • Disable interrupts when handling (but need to be careful that disabling interrupts for too long could lead to lost interrupts

    • Locking mechanism

Note:

  • Note: many OS operations are memory intensive, and memory bandwidth has not improved as dramatically as processor speed over time. Depending on the workload, buying the latest and greatest processor may not speed up OS as much as you might hope!

Context switch / system call time measuring tool:

http://www.bitmover.com/lmbench/lat_ctx.8.html