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).

This paper addresses the problem of how best to visualize network data grouped into overlapping sets. We address it by evaluating various existing techniques alongside a new technique. Such data arise in many areas, including social network analysis, gene expression data, and crime analysis. We begin by investigating the strengths and weakness of four existing techniques, namely Bubble Sets, EulerView, KelpFusion, and LineSets, using principles from psychology and known layout guides. Using insights gained, we propose a new technique, SetNet, that may overcome limitations of earlier methods. We conducted a comparative crowdsourced user study to evaluate all five techniques based on tasks that require information from both the network and the sets. We established that EulerView and SetNet, both of which draw the sets first, yield significantly faster user responses than Bubble Sets, KelpFusion and LineSets, all of which draw the network first.