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Conference Paper: Moving big data to the cloud
Title | Moving big data to the cloud |
---|---|
Authors | |
Keywords | Competitive ratio Computation paradigms Data aggregation Fixed horizons Geographical locations On-line algorithms Resource access Transmitting data |
Issue Date | 2013 |
Publisher | IEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000359 |
Citation | The 32nd IEEE Conference on Computer Communications (IEEE INFOCOM 2013), Turin, Italy, 14-19 April 2013. In IEEE Infocom Proceedings, 2013, p. 405-409 How to Cite? |
Abstract | Cloud computing, rapidly emerging as a new computation paradigm, provides agile and scalable resource access in a utility-like fashion, especially for the processing of big data. An important open issue here is how to efficiently move the data, from different geographical locations over time, into a cloud for effective processing. The de facto approach of hard drive shipping is not flexible, nor secure. This work studies timely, cost-minimizing upload of massive, dynamically-generated, geo-dispersed data into the cloud, for processing using a MapReduce-like framework. Targeting at a cloud encompassing disparate data centers, we model a cost-minimizing data migration problem, and propose two online algorithms, for optimizing at any given time the choice of the data center for data aggregation and processing, as well as the routes for transmitting data there. The first is an online lazy migration (OLM) algorithm achieving a competitive ratio of as low as 2.55, under typical system settings. The second is a randomized fixed horizon control (RFHC) algorithm achieving a competitive ratio of 1+ 1/l+1 κ/λ with a lookahead window of l, where κ and λ are system parameters of similar magnitude. © 2013 IEEE. |
Description | Mini-Conference - IEEE INFOCOM 2013 |
Persistent Identifier | http://hdl.handle.net/10722/186478 |
ISBN | |
ISSN | 2023 SCImago Journal Rankings: 2.865 |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Zhang, L | en_US |
dc.contributor.author | Wu, C | en_US |
dc.contributor.author | Li, Z | en_US |
dc.contributor.author | Guo, C | en_US |
dc.contributor.author | Chen, M | en_US |
dc.contributor.author | Lau, FCM | en_US |
dc.date.accessioned | 2013-08-20T12:11:09Z | - |
dc.date.available | 2013-08-20T12:11:09Z | - |
dc.date.issued | 2013 | en_US |
dc.identifier.citation | The 32nd IEEE Conference on Computer Communications (IEEE INFOCOM 2013), Turin, Italy, 14-19 April 2013. In IEEE Infocom Proceedings, 2013, p. 405-409 | en_US |
dc.identifier.isbn | 978-1-4673-5946-7 | - |
dc.identifier.issn | 0743-166X | - |
dc.identifier.uri | http://hdl.handle.net/10722/186478 | - |
dc.description | Mini-Conference - IEEE INFOCOM 2013 | - |
dc.description.abstract | Cloud computing, rapidly emerging as a new computation paradigm, provides agile and scalable resource access in a utility-like fashion, especially for the processing of big data. An important open issue here is how to efficiently move the data, from different geographical locations over time, into a cloud for effective processing. The de facto approach of hard drive shipping is not flexible, nor secure. This work studies timely, cost-minimizing upload of massive, dynamically-generated, geo-dispersed data into the cloud, for processing using a MapReduce-like framework. Targeting at a cloud encompassing disparate data centers, we model a cost-minimizing data migration problem, and propose two online algorithms, for optimizing at any given time the choice of the data center for data aggregation and processing, as well as the routes for transmitting data there. The first is an online lazy migration (OLM) algorithm achieving a competitive ratio of as low as 2.55, under typical system settings. The second is a randomized fixed horizon control (RFHC) algorithm achieving a competitive ratio of 1+ 1/l+1 κ/λ with a lookahead window of l, where κ and λ are system parameters of similar magnitude. © 2013 IEEE. | - |
dc.language | eng | en_US |
dc.publisher | IEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000359 | - |
dc.relation.ispartof | IEEE Infocom Proceedings | en_US |
dc.subject | Competitive ratio | - |
dc.subject | Computation paradigms | - |
dc.subject | Data aggregation | - |
dc.subject | Fixed horizons | - |
dc.subject | Geographical locations | - |
dc.subject | On-line algorithms | - |
dc.subject | Resource access | - |
dc.subject | Transmitting data | - |
dc.title | Moving big data to the cloud | en_US |
dc.type | Conference_Paper | en_US |
dc.identifier.email | Wu, C: cwu@cs.hku.hk | en_US |
dc.identifier.email | Lau, FCM: fcmlau@cs.hku.hk | en_US |
dc.identifier.authority | Wu, C=rp01397 | en_US |
dc.identifier.authority | Lau, FCM=rp00221 | en_US |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/INFCOM.2013.6566804 | - |
dc.identifier.scopus | eid_2-s2.0-84883056824 | - |
dc.identifier.hkuros | 217642 | en_US |
dc.identifier.spage | 405 | - |
dc.identifier.epage | 409 | - |
dc.publisher.place | United States | - |
dc.customcontrol.immutable | sml 140822 | - |
dc.identifier.issnl | 0743-166X | - |