dc.contributor.author | Gembala, Bartosz Gembala | |
dc.contributor.author | Yazidi, Anis | |
dc.contributor.author | Haugerud, Hårek | |
dc.contributor.author | Nichele, Stefano | |
dc.date.accessioned | 2019-02-18T10:50:30Z | |
dc.date.accessioned | 2019-07-09T06:59:25Z | |
dc.date.available | 2019-02-18T10:50:30Z | |
dc.date.available | 2019-07-09T06:59:25Z | |
dc.date.issued | 2018 | |
dc.identifier.citation | Gembala, Yazidi A, Haugerud H, Nichele S: Autonomous configuration of network parameters in operating systems using evolutionary algorithms. In: NN N. Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems, 2018. Association for Computing Machinery (ACM) p. 118-125 | en |
dc.identifier.isbn | 978-1-4503-5885-9 | |
dc.identifier.uri | https://hdl.handle.net/10642/7254 | |
dc.description.abstract | By default, the Linux network stack is not configured for highspeed large file transfer. The reason behind this is to save memory resources. It is possible to tune the Linux network stack by increasing the network buffers size for high-speed networks that connect server systems in order to handle more network packets. However, there are also several other TCP/IP parameters that can be tuned in an Operating System (OS). In this paper, we leverage Genetic Algorithms (GAs) to devise a system which learns from the history of the network traffic and uses this knowledge to optimize the current performance by adjusting the parameters. This can be done for a standard Linux kernel using sysctl or /proc. For a Virtual Machine (VM), virtually any type of OS can be installed and an image can swiftly be compiled and deployed. By being a sandboxed environment, risky configurations can be tested without the danger of harming the system. Different scenarios for network parameter configurations are thoroughly tested, and an increase of up to 65% throughput speed is achieved compared to the default Linux configuration. | en |
dc.language.iso | en | en |
dc.publisher | ACM | en |
dc.relation.ispartofseries | Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems; | |
dc.rights | © Authors | ACM 2018. This is the authors' version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in RACS '18 Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems, http://dx.doi.org/10.1145/3264746.3264799. | en |
dc.subject | Machine learning | en |
dc.subject | Genetic algorithms | en |
dc.subject | Networks | en |
dc.subject | Configuration | en |
dc.subject | Parameter optimization | en |
dc.subject | Virtual Machine | en |
dc.title | Autonomous configuration of network parameters in operating systems using evolutionary algorithms | en |
dc.type | Chapter | en |
dc.type | Peer reviewed | en |
dc.date.updated | 2019-02-18T10:50:29Z | |
dc.description.version | acceptedVersion | en |
dc.identifier.doi | https://dx.doi.org/10.1145/3264746.3264799 | |
dc.identifier.cristin | 1632855 | |
dc.source.isbn | 978-1-4503-5885-9 | |