| dc.contributor.author | Eugster, Patrick | |
| dc.contributor.author | Jayalath, Chamikara | |
| dc.contributor.author | Kogan, Kirill | |
| dc.contributor.author | Stephen, Julian | |
| dc.date.accessioned | 2021-07-13T09:27:38Z | |
| dc.date.available | 2021-07-13T09:27:38Z | |
| dc.date.issued | 2017-06-09 | |
| dc.identifier.issn | 0018-9162 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.12761/270 | |
| dc.description.abstract | Dealing with big data is a major challenge for our society. The cloud offers a possible response to many needs of big data analytics. However, the cloud abstraction offers the illusion of ubiquitous resources which can mislead users to believe that \details" of cloud implementation such as location of cloud datacenters with respect to users does not matter. Such location-agnosticism can adversely affect performance when computing on data that is distributed or partitioned across several datacenters. We present several solutions for efficiently computing on such data. | |
| dc.language.iso | eng | |
| dc.publisher | IEEE | |
| dc.title | Big Data Analytics beyond the Single Datacenter | en |
| dc.type | magazine | |
| dc.journal.title | IEEE Computer Magazine | |
| dc.type.hasVersion | AM | |
| dc.rights.accessRights | restricted access | |
| dc.volume.number | 50 | |
| dc.issue.number | 6 | |
| dc.identifier.doi | https://doi.org/10.1109/MC.2017.163 | |
| dc.page.final | 68 | |
| dc.page.initial | 60 | |
| dc.subject.keyword | Big data | |
| dc.subject.keyword | geo-distributed data centers | |
| dc.description.status | pub | |
| dc.eprint.id | http://eprints.networks.imdea.org/id/eprint/1444 | |
