mlsanityData to 7 Oct 2026

Occupy the Cloud: Distributed Computing for the 99%

arXiv 1702.04024 ↗First posted 13 Feb 2017cs.DCcited by 17

Not on the map. Why?

Abstract (arXiv)

Distributed computing remains inaccessible to a large number of users, in spite of many open source platforms and extensive commercial offerings. While distributed computation frameworks have moved beyond a simple map-reduce model, many users are still left to struggle with complex cluster management and configuration tools, even for running simple embarrassingly parallel jobs. We argue that stateless functions represent a viable platform for these users, eliminating cluster management overhead, fulfilling the promise of elasticity. Furthermore, using our prototype implementation, PyWren, we show that this model is general enough to implement a number of distributed computing models, such as BSP, efficiently. Extrapolating from recent trends in network bandwidth and the advent of disaggregated storage, we suggest that stateless functions are a natural fit for data processing in future computing environments.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Feb 2017.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 6; 2022: 2; 2023: 1; 2024: 4; 2025: 2; 2026: 2.

17 papers.

Sort:

By area:

  1. 2021Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threadscited by 51
  2. 2021LocalNewton: Reducing Communication Bottleneck for Distributed Learningcited by 15
  3. 2021GraphTheta: A Distributed Graph Neural Network Learning System With Flexible Training Strategycited by 8
  4. 2021Distributed Double Machine Learning with a Serverless Architecturecited by 5
  5. 2022Distributed Sketching for Randomized Optimization: Exact Characterization, Concentration and Lower Boundscited by 5
  6. 2021Serverless Data Science -- Are We There Yet? A Case Study of Model Servingcited by 3
  7. 2025What Limits Agentic Systems Efficiency?cited by 3
  8. 2022FedLesScan: Mitigating Stragglers in Serverless Federated Learningcited by 2
  9. 2023Laminar: A New Serverless Stream-based Framework with Semantic Code Search and Code Completioncited by 2
  10. 2024Computing in the Era of Large Generative Models: From Cloud-Native to AI-Nativecited by 2
  11. 2024FSD-Inference: Fully Serverless Distributed Inference with Scalable Cloud Communicationcited by 1
  12. 2024Newton Meets Marchenko-Pastur: Massively Parallel Second-Order Optimization with Hessian Sketching and Debiasingcited by 1
5 more
  1. 2021Accelerating Serverless Computing by Harvesting Idle Resourcescited by 0
  2. 2024StraightLine: An End-to-End Resource-Aware Scheduler for Machine Learning Application Requestscited by 0
  3. 2025AI Factories: It's time to rethink the Cloud-HPC dividecited by 0
  4. 2026Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioningcited by 0
  5. 2026ServerlessT2I: Efficient Text-to-Image Workflow Serving on a Serverless Platformcited by 0