Edge Replication Strategies for Wide-Area Distributed Processing
| dc.contributor.author | Semmler, Niklas | |
| dc.contributor.author | Rost, Matthias | |
| dc.contributor.author | Smaragdakis, Georgios | |
| dc.contributor.author | Feldmann, Anja | |
| dc.date.accessioned | 2020-06-12T09:31:27Z | |
| dc.date.available | 2020-06-12T09:31:27Z | |
| dc.date.issued | 2020-04-27 | |
| dc.description.abstract | The rapid digitalization across industries comes with many challenges. One key problem is how the ever-growing and volatile data generated at distributed locations can be efficiently processed to inform decision making and improve products. Unfortunately, wide-area network capacity cannot cope with the growth of the data at the network edges. Thus, it is imperative to decide which data should be processed in-situ at the edge and which should be transferred and analyzed in data centers. In this paper, we study two families of proactive online data replication strategies, namely ski-rental and machine learning algorithms, to decide which data is processed at the edge, close to where it is generated, and which is transferred to a data center. Our analysis using real query traces from a Global 2000 company shows that such online replication strategies can significantly reduce data transfer volume in many cases up to 50% compared to naive approaches and achieve close to optimal performance. After analyzing their shortcomings for ease of use and performance, we propose a hybrid strategy that combines the advantages of both competitive and machine learning algorithms. | en |
| dc.description.sponsorship | EC/H2020/679158/EU/Resolving the Tussle in the Internet: Mapping, Architecture, and Policy Making/ResolutioNet | en |
| dc.description.sponsorship | BMBF, 01IS18025A, Verbundprojekt BIFOLD-BBDC: Berlin Institute for the Foundations of Learning and Data | en |
| dc.description.sponsorship | BMBF, 01IS18037A, Verbundprojekt BIFOLD-BZML: Berlin Institute for the Foundations of Learning and Data | en |
| dc.identifier.isbn | 978-1-4503-7132-2 | |
| dc.identifier.uri | https://depositonce.tu-berlin.de/handle/11303/11323 | |
| dc.identifier.uri | http://dx.doi.org/10.14279/depositonce-10208 | |
| dc.language.iso | en | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject.ddc | 006 Spezielle Computerverfahren | de |
| dc.subject.other | data replication | en |
| dc.subject.other | distributed systems | en |
| dc.subject.other | edge computing | en |
| dc.title | Edge Replication Strategies for Wide-Area Distributed Processing | en |
| dc.type | Conference Object | en |
| dc.type.version | acceptedVersion | en |
| dcterms.bibliographicCitation.doi | 10.1145/3378679.3394532 | en |
| dcterms.bibliographicCitation.originalpublishername | Association for Computing Machinery (ACM) | en |
| dcterms.bibliographicCitation.originalpublisherplace | New York, NY | en |
| dcterms.bibliographicCitation.proceedingstitle | Proceedings of the Third ACM International Workshop on Edge Systems, Analytics and Networking (EdgeSys '20) | en |
| tub.accessrights.dnb | free | en |
| tub.affiliation | Fak. 4 Elektrotechnik und Informatik::Inst. Telekommunikationssysteme::FG Internet Measurement and Analysis (IMA) | de |
| tub.affiliation.faculty | Fak. 4 Elektrotechnik und Informatik | de |
| tub.affiliation.group | FG Internet Measurement and Analysis (IMA) | de |
| tub.affiliation.institute | Inst. Telekommunikationssysteme | de |
| tub.publisher.universityorinstitution | Technische Universität Berlin | en |