Scaling Reliably: Improving the Scalability of the Erlang Distributed Actor Platform
Abstract
Distributed actor languages are an effective means of constructing scalable reliable systems, and the Erlang programming language has a well-established and influential model. While Erlang model conceptually provides reliable scalability, it has some inherent scalability limits and these force developers to depart from the model at scale. This article establishes the scalability limits of Erlang systems, and reports the work to improve the language scalability. We systematically study the scalability limits of Erlang and address the issues at the virtual machine (VM), language, and tool levels. More specifically: (1) We have evolved the Erlang VM so that it can work effectively in large scale single-host multicore and NUMA architectures. We have made important architectural improvements to the Erlang/OTP. (2) We have designed and implemented Scalable Distributed (SD) Erlang libraries to address language-level scalability issues, and provided and validated a set of semantics for the new language constructs. (3) To make large Erlang systems easier to deploy, monitor, and debug we have developed and made open source releases of five complementary tools, some specific to SD Erlang. Throughout the article we use two case studies to investigate the capabilities of our new technologies and tools: a distributed hash table based Orbit calculation and Ant Colony Optimisation (ACO). Chaos Monkey experiments show that two versions of ACO survive random process failure and hence that SD Erlang preserves the Erlang reliability model. Even for programs with no global recovery data to maintain, SD Erlang partitions the network to reduce network traffic and hence improves performance of the Orbit and ACO benchmarks above 80 hosts. ACO measurements show that maintaining global recovery data dramatically limits scalability; however scalability is recovered by partitioning the recovery data.
Keywords
Cite
@article{arxiv.1704.07234,
title = {Scaling Reliably: Improving the Scalability of the Erlang Distributed Actor Platform},
author = {Phil Trinder and Natalia Chechina and Nikolaos Papaspyrou and Konstantinos Sagonas and Simon Thompson and Stephen Adams and Stavros Aronis and Robert Baker and Eva Bihari and Olivier Boudeville and Francesco Cesarini and Maurizio Di Stefano and Sverker Eriksson and Viktoria Fordos and Amir Ghaffari and Aggelos Giantsios and Rickard Green and Csaba Hoch and David Klaftenegger and Huiqing Li and Kenneth Lundin and Kenneth Mackenzie and Katerina Roukounaki and Yiannis Tsiouris and Kjell Winblad},
journal= {arXiv preprint arXiv:1704.07234},
year = {2017}
}