2017/09/24 by Eric Jonas, Qifan Pu, Shivaram Venkataraman +2 · 1 citation
Computer Science · #Cloud Computing and Resource Management #IoT and Edge/Fog Computing #Distributed and Parallel Computing Systems #Computer science #Stateless protocol #Distributed computing #Cloud computing #Overhead (engineering) #Embarrassingly parallel #Elasticity (physics) #Computer cluster #Bandwidth (computing) #Computation #Simple (philosophy) #Cluster (spacecraft) #Operating system #Computer network
paper · doi:10.1145/3127479.3128601
openalex created_date 2017/03/03 · openalex publication_date 2017/09/24 · openalex updated_date 2026/07/29
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.