Repository logo
 
Publication

On Power Consumption Profiles for Data Intensive Workloads in Virtualized Hadoop Clusters

dc.contributor.authorQureshi, Basit
dc.contributor.authorAlwehaibi, Sultan
dc.contributor.authorKoubâa, Anis
dc.date.accessioned2017-06-02T13:34:11Z
dc.date.available2017-06-02T13:34:11Z
dc.date.issued2017
dc.descriptionIEEE International Conference on Computer Communications (INFOCOM 2017). 1 to 4, May, 2017, Workshop Big Data and Cloud Performance. Atlanta, U.S.A..pt_PT
dc.description.abstractAlthough reduction in operating costs remains to be a key motivation for migration to Cloud environments, Power consumption is a big concern for data centers and cloud service providers. Many big data applications execute on Hadoop MapReduce framework for processing large workloads. In this paper, we investigate the tradeoff between energy consumption and workload running on Hadoop clusters using multiple virtual machines. We characterize power consumption profiles for various data intensive workloads and correlate these to quality of service (QoS) metrics such as job execution time. Based on experiments, we ascertain that power consumption profiles for big data applications can be used to optimize energy efficiency in data centers. We infer that these profiles can be used by Cloud service providers and consumers to specify green metrics in Service Level Agreements (SLA).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1109/INFCOMW.2017.8116350pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.22/9859
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relation.ispartofseriesINFOCOM;2017
dc.subjectMapReducept_PT
dc.subjectEnergy efficiencypt_PT
dc.subjectVirtual Hadoop clusterspt_PT
dc.subjectPower consumptionpt_PT
dc.titleOn Power Consumption Profiles for Data Intensive Workloads in Virtualized Hadoop Clusterspt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceAtlanta, U.S.A.pt_PT
oaire.citation.titleIEEE International Conference on Computer Communications (INFOCOM 2017). 1 to 4, May, 2017, Workshop Big Data and Cloud Performancept_PT
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT

Files

Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
COM3_CISTER_2017.pdf
Size:
1.46 MB
Format:
Adobe Portable Document Format
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description: