Scaling Reliably: Improving the Scalability of the Erlang Distributed Actor Platform

Authors: Trinder, P. et al.

Journal: ACM Trans. Program. Lang. Syst.

Volume: 39

Pages: 17:1-17:46

Publisher: ACM

ISSN: 0164-0925

DOI: 10.1145/3107937

This data was imported from arXiv:

Authors: Trinder, P. et al.

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.

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