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

Trinder, P., Chechina, N., Papaspyrou, N., Sagonas, K., Thompson, S., Adams, S., Aronis, S., Baker, R., Bihari, E., Boudeville, O., Cesarini, F., Stefano, M.D., Eriksson, S., förd Hos, V., Ghaffari, A., Giantsios, A., Green, R., Hoch, C., Klaftenegger, D., Li, H., Lundin, K., Mackenzie, K., Roukounaki, K., Tsiouris, Y. and Winblad, K., 2017. Scaling Reliably: Improving the Scalability of the Erlang Distributed Actor Platform. ACM Transactions on Programming Languages and Systems, 39 (4), 17.

Full text available as:

[img]
Preview
PDF
release-summary-arxiv.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

3MB

DOI: 10.1145/3107937

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 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

Item Type:Article
ISSN:0164-0925
Uncontrolled Keywords:Erlang; reliability; scalability
Group:Faculty of Science & Technology
ID Code:30276
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:29 Jan 2018 12:21
Last Modified:29 Jan 2018 12:21

Downloads

Downloads per month over past year

More statistics for this item...
Repository Staff Only -