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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. Group
  2. WISR Group
  3. Cache
  4. Index

Hyperbolic Caching: Flexible Caching for Web Applications

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

PreviousReinforcement Learning for Caching with Space-Time Popularity DynamicsNextLearning Cache Replacement with CACHEUS

Last updated 4 years ago

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Presentation

  • Multi-tier data center

  • Cache tier: e.x. redis or memcache

    • Properties: web-like request pattern, various item cost, item expiration and so on

  • Number of accesses: Frequency captures independent draws property of workloads

  • Time since i entered cache: Addresses problem of LFU by measuring relative popularity

Item requests: A A B C C

  • t = 1: pr(A) = 1

  • t = 2: pr(A) = 1

  • t = 3: pr(A) = 2/3, pr(B) = 1

  • t = 4, pr(A) = 2/4, pr(B) = 1/2, pr(C) = 1

  • t = 5, pr(A) = 2/5, pr(B) = 1/3, pr(C) = 2/2 = 1

  • Static workload

  • caching as a service company

  • 16 applications with cache sizes chosen by the application developers

    • Only a few which Hyperbolic performs worse

  • item costs

    • CPU / DB load: experience a miss, cause the item is the result of the join rather than simple look up

  • GreedyDual

    • Incorporating cost into LRU

  • Stress one of the backend and measure tail latency

  • Tracking costs per item and per class

    • Load one of the backend, takes time to adjust to the knowledge --> spike in latency that is not experienced by per-class