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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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  1. Networking
  2. Index
  3. CS 294 (Distributed System)
  4. Week 1 - Global State and Clocks

Time, Clocks, and the Ordering of Events in a Distributed System

PreviousDistributed Snapshots: Determining Global States of Distributed SystemsNextWeak 5 - Weak Consistency

Last updated 2 years ago

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Concepts

  • Distributed system

    • A collection of distinct processes which are spatially separated, and which communicate with each other by exchanging messages

    • Each process contains a sequence of events

  • Three important concepts

    • Time

    • Clock

    • Ordering of events

      • "Happen before": partial ordering of the events in the system, without using physical clocks

  • Though: problem of failures is a difficult one

    • The entire concept of failure is only meaningful in the context of physical time

    • Without it, there is no way of distinguish a failed process from one which is just puasing between events

  • The partial ordering

    • -->: "happen before" relation.

      • If a and b are events in the same process, and a comes before b, then a --> b

      • If a is the sending of a message by one process and b is the receipt of the same message by another process, then a --> b

      • If a --> b and b --> c then a --> c

    • Two distinct events a and b are said to be concurrent if and

    • Irreflexive partial ordering on the set of all events in the system

  • Logical clocks

    • Clocks: a way to assign number to the event, where the number is thought of as the time at which the event occurred

    • Clock CiC_iCi​ for each process PiP_iPi​ is a function which assigns a number Ci<a>C_i<a>Ci​<a>to any event aaa in that process (logical clock)

    • C<b>=Cj<b>C<b>=C_j<b>C<b>=Cj​<b> if b is an event in process PjP_jPj​

      • But the converse condition is not true!

  • Total ordering of the events

    • Define relation => as follows

      • If a is an event in process PiP_iPi​ and b is an event in process PjP_jPj​, then a => b if and only if either

      • This relation is a way of completing the "happened before" partial ordering to a total ordering

      • Clock condition implies that if a-->b then a ==> b

    • This ordering depends upon the system of clock CiC_iCi​, and is not unique

      • Different choice of clocks which satisfy the clock condition yields different relation =>

      • It is only the partial ordering --> which is uniquely determined by the system of events

    • Why do we need total ordering?

      • The reason for implementing a correct system of logical clock is to obtain such total ordering

      • 逻辑时钟设计的最终目的就是为了得到一种事件全局排序的机制

    • Scenarios

      • System composed with a fixed collection of processes which share a single resource

      • Only one process can use the resource at a time, so the processes must synchronize themselves to avoid conflict

      • Find an algorithm for granting the resource to a process which satisfies the following condition

        • A process which has been granted the resource must release it before it can be granted to another process

        • Different requests for the resource must be granted in the order in which they are made

        • If every process which is granted the resource eventually releases it, then every request is eventually granted

  • Anomalous Behavior ()

    • Person issues a request A on a computer A, and then telephones a friend in another city to have him issue a request B on a different computer B. It is possible for request B to receive a lower timestamp and be ordered before request A.

    • This can happen because the system has no way of knowing that A actually preceded B, since that precedence information is based on messages external to the system.

    • Two possible ways

      • Explicit introduce into the system about necessary information about the ordering

        • I.e. friend could specify that B be given a timestamp latter than T_A

        • This gives users the responsibility to avoid anomalous behavior

      • Construct a system of clocks which satisfies (i.e. properly synchronized physical clocks)

        • Stronger than ordinary clock condition because --> is a stronger relation than ->

        • The discussion on the physical clock references the paper (the following materials are from the ) --> need to understand and revisit this part

        • Goal: 通过运行一个时钟同步算法,保证时钟对于任何事件打上的时间戳都不产生前面的异常行为(anomalous behavior)。也就是说,对于任意两个有偏序关系的事件(或者说可能在因果性上产生影响的两个事件),我们的物理时钟要保证总是会为后一个事件打上一个更大的时间戳。要实现这个目标,我们面临的障碍主要来源于物理时钟的两种误差:

          • 时钟的运行速率跟真实时间的流逝速率可能有差异;

          • 任意两个时钟的运行速率有差异,它们的读数会漂移得越来越远。

        • Lamport在论文中提出的物理时钟同步算法 将这两种时钟误差考虑在内,不断地对各个进程本地的物理时钟进行微调,把误差控制在能够满足Strong Clock Condition的范围内。

        • 分别考虑进程内的事件和跨进程的事件:

          • 对于进程内发生的不同事件,必须保证后发生的事件比先发生的事件时间戳要大。这实际上是要求我们保证每个物理时钟实例的读数总是单调递增的。这是比较容易实现的,我们只需要在微调时钟读数的时候,只把读数调大而不把读数调小。

          • 对于发生在两个不同进程上的事件

            • 为了保证不出现异常行为,我们就要求,不管A向B传递信息这个过程发生的速度有多快(最快可以达到光速,但在实际系统中,由于网络延迟和缓存等原因会慢很多),请求B发生时的时钟读数都必须大于请求A发生时的时钟读数。

            • 这个要求可能没法被满足的原因在于两边的物理时钟可能有误差。

            • 因此,就需要在不同的物理时钟之间交换信息,并借助这些信息同步时钟读数。

              • Expect 可以把时钟之间的误差控制在一定范围内 (只要我们在时钟之间交换信息足够频繁)。这是两种机制的赛跑:

                • 一方面,A通过系统外的方式向B传递信息,只要这个过程足够快,他们就有可能“看到”时钟误差造成的时钟读数减退(也就是出现了异常行为)

                • 另一方面,物理时钟同步算法通过在时钟之间不断交换信息并按照一定规则调整时钟读数,将时钟误差控制在一定范围内。只要算法的各个参数设置得当,就能保证:即使A向B传递信息的速度达到物理极限——光速,他们也无法“看到”时钟读数的减退现象。于是,Strong Clock Condition就被满足了。

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