Big Data Analytics beyond the Single Datacenter

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