Performance evaluation of cloud object storage for big data

dc.contributor.advisorDuan, Lide
dc.contributor.authorMhalagi, Swanand R.
dc.contributor.committeeMemberLee, Wonjun
dc.contributor.committeeMemberPrevost, Jeff
dc.contributor.committeeMemberRad, Paul
dc.descriptionThis item is available only to currently enrolled UTSA students, faculty or staff. To download, navigate to Log In in the top right-hand corner of this screen, then select Log in with my UTSA ID.
dc.description.abstractThe need for reliable and fast storage systems is increasingly critical in various fields including artificial intelligence and data analysis. A new architecture for large-scale data storage systems is proposed in this thesis, which focuses on comparing and optimizing performance of different software/hardware-defined storage technologies that effectively reduce the computational latency and improve the performance. The main contributions of this thesis are: (i) the combination of SMR (for storing data) and SSD (for storing metadata) is a viable solution for implementing large data storage systems, and (ii) the combination of CMR (for storing data) and SSD (for storing metadata) shows the highest performance for high performance computing. Our experiments are carried out on multiple settings, demonstrating that the proposed architecture successfully improves performance for sequential and random read/writes. The prototypes are evaluated with some realistic workloads, showing the superiority of the proposed data storage configurations. This provides new opportunities for efficiently processing and storing data and metadata in large-scale data analysis systems.
dc.description.departmentElectrical and Computer Engineering
dc.format.extent69 pages
dc.subjectBig Data Storage
dc.subjectCloud Storage
dc.subjectObject Storage
dc.subjectPerformance Analysis
dc.subjectSingled Magnetic Recording
dc.subject.classificationComputer engineering
dc.subject.classificationElectrical engineering
dc.titlePerformance evaluation of cloud object storage for big data
dcterms.accessRightspq_closed and Computer Engineering of Texas at San Antonio of Science


Original bundle

Now showing 1 - 1 of 1
No Thumbnail Available
853.57 KB
Adobe Portable Document Format