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 the 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 of the EU RELEASE project to improve the scalability and understandability of the Erlang reliable distributed actor model. We systematically study the scalability limits of Erlang and then address the issues at the virtual machine, language, and tool levels. More specifically: (1) We have evolved the Erlang virtual machine so that it can work effectively in large-scale single-host multicore and NUMA architectures. We have made important changes and architectural improvements to the widely used Erlang/OTP release. (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. While we report measurements on a range of NUMA and cluster architectures, the key scalability experiments are conducted on the Athos cluster with 256 hosts (6,144 cores). 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. We exceed the established scalability limits of distributed Erlang, and do not reach the limits of SD Erlang for these benchmarks at this scale (256 hosts, 6,144 cores).

Large scale servers with hundreds of hosts and tens of thousands of cores are becoming common. To exploit these platforms software must be both scalable and reliable, and distributed actor languages like Erlang are a proven technology in this area. While distributed Erlang conceptually supports the engineering of large scale reliable systems, in practice it has some scalability limits that force developers to depart from the standard language mechanisms at scale. In earlier work we have explored these scalability limitations, and addressed them by providing a Scalable Distributed (SD) Erlang library that partitions the network of Erlang Virtual Machines (VMs) into scalable groups (s_groups). This paper presents the first systematic evaluation of SD Erlang s_groups and associated tools, and how they can be used. We present a comprehensive evaluation of the scalability and reliability of SD Erlang using three typical benchmarks and a case study. We demonstrate that s_groups improve the scalability of reliable and unreliable Erlang applications on up to 256 hosts (6,144 cores). We show that SD Erlang preserves the class-leading distributed Erlang reliability model, but scales far better than the standard model. We present a novel, systematic, and tool-supported approach for refactoring distributed Erlang applications into SD Erlang. We outline the new and improved monitoring, debugging and deployment tools for large scale SD Erlang applications. We demonstrate the scaling characteristics of key tools on systems comprising up to 10 K Erlang VMs.