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      • How to Create Picture-in-Picture Effect / Video Overlay for a Presentation Video
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  • Machine Learning Systems
    • Machine Learning Systems - Index
      • MLSys Papers - Short Notes
      • [2011 NSDI] Dominant Resource Fairness: Fair Allocation of Multiple Resource Types
      • [2014 OSDI] Scaling Distributed Machine Learning with the Parameter Server
      • [2018 OSDI] Gandiva: Introspective Cluster Scheduling for Deep Learning
      • [2018 SIGCOMM] Chameleon: Scalable Adaptation of Video Analytics via Temporal and Cross-camera ...
      • [2018 NIPS] Dynamic Space-Time Scheduling for GPU Inference
      • [2019 ATC] Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads
      • [2019 NSDI] Tiresias: A GPU Cluster Manager for Distributed Deep Learning
      • [2019 SOSP] ByteScheduler: A Generic Communication Scheduler for Distributed DNN Training ...
      • [2019 SOSP] PipeDream: Generalized Pipeline Parallelism for DNN Training
      • [2019 SOSP] Parity Models: Erasure-Coded Resilience for Prediction Serving Systems
      • [2019 NIPS] GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
      • [2019 SC] ZeRO: memory optimizations toward training trillion parameter models
      • [2020 OSDI] Gavel: Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads
      • [2020 OSDI] AntMan: Dynamic Scaling on GPU Clusters for Deep Learning
      • [2020 OSDI] BytePS: A High Performance and Generic Framework for Distributed DNN Training
      • [2020 SIGCOMM] Reducto: On-Camera Filtering for Resource-Efficient Real-Time Video Analytics
        • [2020 MLSys] Salus: Fine-Grained GPU Sharing Primitives for Deep Learning Applications
      • [2020 EuroSys] AlloX: Compute Allocation in Hybrid Clusters
      • [2020 VLDB] PyTorch Distributed: Experiences on Accelerating Data Parallel Training
      • [2020 NetAI] Is Network the Bottleneck of Distributed Training?
      • [2020 NSDI] Themis: Fair and Efficient GPU Cluster Scheduling
      • [2021 MLSys] Accordion: Adaptive Gradient Communication via Critical Learning Regime Identification
      • [2021 VLDB] Analyzing and Mitigating Data Stalls in DNN Training
      • [2021 FAST] CheckFreq: Frequent, Fine-Grained DNN Checkpointing
      • [2021 EuroMLSys] Interference-Aware Scheduling for Inference Serving
      • [2021 OSDI] Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning
      • [2021 MLSys] Wavelet: Efficient DNN Training with Tick-Tock Scheduling
      • [2021 NSDI] SwitchML: Scaling Distributed Machine Learning with In-Network Aggregation
    • Big Data Systems - Index
      • Big Data Systems Papers - Short Notes
      • [2003 SOSP] The Google File System
      • [2004 OSDI] MapReduce: Simplified Data Processing on Large Clusters
      • [2010 SIGMOD] Pregel: A System for Large-Scale Graph Processing
      • [2011 NSDI] Mesos: A Platform for Fine-Grained Resource Sharing in the Data Center
      • [2012 NSDI] Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster ...
      • [2012 OSDI] PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs
      • [2019 FAST] DistCache: Provable Load Balancing for Large-Scale Storage Systems with Distributed...
      • [2021 HotOS] From Cloud Computing to Sky Computing
      • [2021 EuroSys] NextDoor: Accelerating graph sampling for graph machine learning using GPUs
  • Earlier Readings & Notes
    • High Performance Computing Course Notes
      • Lecture 1: Course Overview
      • Lecture 2: From Code to Instructions. The FDX Cycle. Instruction Level Parallelism.
      • Lecture 3: Superscalar architectures. Measuring Computer Performance. Memory Aspects.
      • Lecture 4: The memory hierarchy. Caches.
      • Lecture 5: Caches, wrap up. Virtual Memory.
      • Lecture 6: The Walls to Sequential Computing. Moore’s Law.
      • Lecture 7: Parallel Computing. Flynn's Taxonomy. Amdahl's Law.
      • Lecture 8: GPU Computing Intro. The CUDA Programming Model. CUDA Execution Configuration.
      • Lecture 9: GPU Memory Spaces
      • Lecture 10: GPU Scheduling Issues.
      • Lecture 11: Execution Divergence. Control Flow in CUDA. CUDA Shared Memory Issues.
      • Lecture 12: Global Memory Access Patterns and Implications.
      • Lecture 13: Atomic operations in CUDA. GPU ode optimization rules of thumb.
      • Lecture 14: CUDA Case Studies. (1) 1D Stencil Operation. (2) Vector Reduction in CUDA.
      • Lecture 15: CUDA Case Studies. (3) Parallel Prefix Scan on the GPU. Using Multiple Streams in CUDA.
      • Lecture 16: Streams, and overlapping data copy with execution.
      • Lecture 17: GPU Computing: Advanced Features.
      • Lecture 18: GPU Computing with thrust and cub.
      • Lecture 19: Hardware aspects relevant in multi-core, shared memory parallel computing.
      • Lecture 20: Multi-core Parallel Computing with OpenMP. Parallel Regions.
      • Lecture 21: OpenMP Work Sharing.
      • Lecture 22: OpenMP Work Sharing
      • Lecture 23: OpenMP NUMA Aspects. Caching and OpenMP.
      • Lecture 24: Critical Thinking. Code Optimization Aspects.
      • Lecture 25: Computing with Supercomputers.
      • Lecture 26: MPI Parallel Programming General Introduction. Point-to-Point Communication.
      • Lecture 27: MPI Parallel Programming Point-to-Point communication: Blocking vs. Non-blocking sends.
      • Lecture 28: MPI Parallel Programming: MPI Collectives. Overview of topics covered in the class.
    • Cloud Computing Course Notes
      • 1.1 Introduction to Clouds, MapReduce
      • 1.2 Gossip, Membership, and Grids
      • 1.3 P2P Systems
      • 1.4 Key-Value Stores, Time, and Ordering
      • 1.5 Classical Distributed Algorithms
      • 4.1 Spark, Hortonworks, HDFS, CAP
      • 4.2 Large Scale Data Storage
    • Operating Systems Papers - Index
      • CS 736 @ UW-Madison Fall 2020 Reading List
      • All File Systems Are Not Created Equal: On the Complexity of Crafting Crash-Consistent Applications
      • ARC: A Self-Tuning, Low Overhead Replacement Cache
      • A File is Not a File: Understanding the I/O Behavior of Apple Desktop Applications
      • Biscuit: The benefits and costs of writing a POSIX kernel in a high-level language
      • Data Domain: Avoiding the Disk Bottleneck in the Data Domain Deduplication File System
      • Disco: Running Commodity Operating Systems on Scalable Multiprocessors
      • FFS: A Fast File System for UNIX
      • From WiscKey to Bourbon: A Learned Index for Log-Structured Merge Trees
      • LegoOS: A Disseminated, Distributed OS for Hardware Resource Disaggregation
      • LFS: The Design and Implementation of a Log-Structured File System
      • Lottery Scheduling: Flexible Proportional-Share Resource Management
      • Memory Resource Management in VMware ESX Server
      • Monotasks: Architecting for Performance Clarity in Data Analytics Frameworks
      • NFS: Sun's Network File System
      • OptFS: Optimistic Crash Consistency
      • RAID: A Case for Redundant Arrays of Inexpensive Disks
      • RDP: Row-Diagonal Parity for Double Disk Failure Correction
      • Resource Containers: A New Facility for Resource Management in Server Systems
      • ReVirt: Enabling Intrusion Analysis through Virtual-Machine Logging and Replay
      • Scheduler Activations: Effective Kernel Support for the User-Level Management of Parallelism
      • SnapMirror: File-System-Based Asynchronous Mirroring for Disaster Recovery
      • The Linux Scheduler: a Decade of Wasted Cores
      • The Unwritten Contract of Solid State Drives
      • Venti: A New Approach to Archival Storage
    • Earlier Notes
      • How to read a paper
  • FIXME
    • Template for Paper Reading Notes
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  • One-line Summary
  • Paper Structure Outline
  • Background & Motivation
  • Design and Implementation
  • Compatibility Layer
  • Intercloud Layer
  • Peering Layer
  • Speculations about the Future
  • Links

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  1. Machine Learning Systems
  2. Big Data Systems - Index

[2021 HotOS] From Cloud Computing to Sky Computing

Previous[2019 FAST] DistCache: Provable Load Balancing for Large-Scale Storage Systems with Distributed...Next[2021 EuroSys] NextDoor: Accelerating graph sampling for graph machine learning using GPUs

Last updated 2 years ago

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One-line Summary

This paper envisions sky computing, the possible future, and a more commoditized version of cloud computing, by drawing lessons from the history of the Internet. It then introduces the technical/economical barriers of fulfilling this vision of utility computing.

Paper Structure Outline

  1. Introduction

  2. Historical Context

  3. Lessons from the Internet

  4. Compatibility Layer

  5. Intercloud Layer

  6. Peering Between Clouds

  7. Speculations about the Future

  8. Conclusion

Background & Motivation

Computation may someday be organized as a public utility, just as the telephone system is a public utility. We can envisage computer service companies whose subscribers are connected to them [...]. Each subscriber needs to pay only for the capacity that he actually uses, but he has access to all programming languages characteristic of a very large system.” -- John McCarthy on the future of computing, 1961

Currently, from the user's point of view, many of the cloud computing services (AWS, Microsoft, Google, etc.) are proprietary/differentiated (e.g., APIs for cluster management, object store, data warehouse, serverless offering), and thus applications developed on one cloud cannot be easily migrated to another.

From the provider's point of view, business models are built around "attracting and retaining customers", which goes against the idea of offering a purely commoditized service.

The benefits of sky computing are:

  • New capabilities: If one cloud in the sky provides access to new hardware (e.g., TPU), any app in the sky can use it

  • Better security: Eliminate a single point of attack by distributing trust across multiple clouds

  • Better reliability: Avoids major cloud outages

  • Better performance: Aggregates all resources to use the best resources for a job

  • Lower cost: Use most cost-effective cloud for a job

Design and Implementation

To fulfill the sky computing vision, three design issues (the Internet also faced them) must be addressed:

  • Compatibility layer: Mask low-level technical differences/heterogeneity

  • Intercloud/Routing layer: Route jobs to the right cloud

  • Peering layer: Allow clouds to have agreements with each other about how to exchange services

Compatibility Layer

Similar to the IP layer, a compatibility layer abstracts away the services provided by a cloud and allows an application developed on top of this layer to run on different clouds without change. The authors conclude that this is not technically difficult, as the high-level management and service interfaces users interact with are now more than ever supported by open source software (OSS). The compatibility layer could be constructed out of some set of the OSS solutions. One glaring gap is the storage layer (AWS has S3, Azure has Blob storage, etc.), but there are currently efforts underway to provide more compatibility and fill this gap.

OSS projects for different levels of the software stack include:

  • OS: Linux

  • Cluster resource managers: Kubernetes, Apache Mesos

  • Application packaging: Docker

  • Databases: MySQL, Postgres

  • Big data execution engines: Apache Spark, Apache Hadoop

  • Streaming engines: Apache Flink, Apache Spark, Apache Kafka

  • Distributed query engines and db: Cassandra, MongoDB, Presto, SparkSQL, Redis

  • ML libraries: PyTorch, Tensorflow, MXNet, MLFlow, Horovod, Ray RLlib

  • General distributed frameworks: Ray, Erlang, Akka

Intercloud Layer

The intercloud layer should allow users to specify policies describing the tradeoff between performance, availability, and cost (e.g., a user might specify that this is a Tensorflow job, it involves data that cannot leave Germany, and must be finished within the next two hours for under a certain cost), but not require users to make low-level decisions. There should be few technical limitations as this (moving jobs across clouds) is similar to moving jobs within the same cloud across datacenters.

Peering Layer

Under certain scenarios, instead of processing all data in the same cloud, moving data between clouds can be cost-effective. For example, although moving a 150 GB ImageNet dataset out of AWS costs $13, training ResNet50 on ImageNet on AWS costs ~$40, while training on Azure costs $20. If clouds adopt reciprocal data peering arrangements, it allows data to be moved freely between peering clouds and enables greater freedom in job movement.

Speculations about the Future

The authors' vision is as follows. While large providers may not be incentivized to build a compatibility layer, smaller cloud providers will embrace such a layer and form a sky. Within the sky, providers may specialize in supporting one or more services. E.g., Oracle can provide a database-optimized cloud, NVIDIA can provide GPU-optimized, hardware-assisted ML services, Samsung can provide a storage-optimized cloud. In the long term, both the standalone providers and in-sky providers will exist: the standalone providers compete with each other and the sky, and the in-sky providers both compete within the sky and collectively compete with the standalone providers.

Links

Paper PDF
Presentation video at HotOS '21
Status quo: multi-cloud, porting from one cloud to another is expensive
What sky computing will bring