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DC FieldValueLanguage
dc.contributorDepartment of Computingen_US
dc.creatorWong, Petrie Ke Fang-
dc.publisherHong Kong Polytechnic University-
dc.rightsAll rights reserveden_US
dc.titleParallel analytics as a serviceen_US
dcterms.abstractRecently, massively parallel processing relational database systems (MPPDBs) have gained much momentum in the big data analytic market. With the advent of hosted cloud computing, this thesis envisions that the offering of MPPDB-as-a-Service (MPPDBaaS) will become attractive for companies having analytical tasks on only hundreds gigabytes to some ten terabytes of data because they can enjoy high-end parallel analytics at a cheap cost. This thesis presents Thrifty, a prototype implementation of MPPDB-as-a-service. The major research issue is how to achieve a lower total cost of ownership by consolidating thousands of MPPDB tenants on to a shared hardware infrastructure, with a performance SLA that guarantees the tenants can obtain the query results as if they are executing their queries on dedicated machines. Thrifty achieves the goal by using a tenant-driven design that includes (1) a cluster design that carefully arranges the nodes in the cluster into groups and creates an MPPDB for each group of nodes, (2) a tenant placement that assigns each tenant to several MPPDBs (for high availability service through replication), and (3) a query routing algorithm that routes a tenant’s query to the proper MPPDB at run-time. Experiments show that in a MPPDBaaS with 5000 tenants, where each tenant requests 2 to 32 nodes MPPDB to query against 200GB to 3.2TB of data, Thrifty can serve all the tenants with a 99.9% performance SLA guarantee and a high availability replication factor of 3, using only 18.7% of the nodes requested by the tenants.en_US
dcterms.extentxiv, 58 pages : illustrations ; 30 cmen_US
dcterms.isPartOfPolyU Electronic Thesesen_US
dcterms.educationalLevelAll Masteren_US
dcterms.LCSHParallel programming (Computer science)en_US
dcterms.LCSHBig data -- Managementen_US
dcterms.LCSHCloud computingen_US
dcterms.LCSHHong Kong Polytechnic University -- Dissertationsen_US
dcterms.accessRightsopen accessen_US

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Please use this identifier to cite or link to this item: https://theses.lib.polyu.edu.hk/handle/200/7533