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Distributed actor languages are an effective means of constructing scalablereliable systems, and the Erlang programming language has a well-establishedand influential model. While Erlang model conceptually provides reliablescalability, it has some inherent scalability limits and these force developersto depart from the model at scale. This article establishes the scalabilitylimits of Erlang systems, and reports the work to improve the languagescalability.  We systematically study the scalability limits of Erlang and address theissues at the virtual machine (VM), language, and tool levels. Morespecifically: (1) We have evolved the Erlang VM so that it can work effectivelyin large scale single-host multicore and NUMA architectures. We have madeimportant architectural improvements to the Erlang/OTP. (2) We have designedand implemented Scalable Distributed (SD) Erlang libraries to addresslanguage-level scalability issues, and provided and validated a set ofsemantics for the new language constructs. (3) To make large Erlang systemseasier to deploy, monitor, and debug we have developed and made open sourcereleases of five complementary tools, some specific to SD Erlang.  Throughout the article we use two case studies to investigate thecapabilities of our new technologies and tools: a distributed hash table basedOrbit calculation and Ant Colony Optimisation (ACO). Chaos Monkey experimentsshow that two versions of ACO survive random process failure and hence that SDErlang preserves the Erlang reliability model. Even for programs with no globalrecovery data to maintain, SD Erlang partitions the network to reduce networktraffic and hence improves performance of the Orbit and ACO benchmarks above 80hosts. ACO measurements show that maintaining global recovery data dramaticallylimits scalability; however scalability is recovered by partitioning therecovery data.

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