Masterarbeit MSTR-2016-73

Saleem, Muhammad Usman: Cost optimization for data placement strategies in an analytical cloud service.
Universität Stuttgart, Fakultät Informatik, Elektrotechnik und Informationstechnik, Masterarbeit Nr. 73 (2016).
106 Seiten, englisch.

Analyzing a large amount of business-relevant data in near-realtime in order to assist decision making became a crucial requirement for many businesses in the last years. Therefore, all major database system vendors offer solutions that assist customers in this requirement with systems that are specially tuned for accelerating analytical workloads. Before the decision is made to buy such a huge and expensive solution, customers are interested in getting a detailed workload analysis in order to estimate potential benefits. Therefore, a more agile solution is desirable having lower barriers to entry that allows customers to assess analytical solutions for their workloads and lets data scientists experiment with available data on test systems before rolling out valuable analytical reports on a production system. In such a scenario where separate systems are deployed for handling transactional workloads of daily customers business and conducting business analytics on either a cloud service or a dedicated accelerator appliance, data management and placement strategies are of high importance. Multiple approaches exist for keeping the data set in-sync and guaranteeing data coherence with unique characteristics regarding important metrics that impact query performance, such as the latency when data will be propagated, achievable throughputs for larger data volumes, or the amount of required CPU to detect and deploy data changes. So the important heuristics are analyzed and evolved in order to develop a general model for data placement and maintenance strategies. Based on this theoretical model, a prototype is also implemented that predicts these metrics.

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Abteilung(en)Universität Stuttgart, Institut für Parallele und Verteilte Systeme, Anwendersoftware
BetreuerMitschang, Prof. Bernhard; Beier, Felix
Eingabedatum6. Juni 2019
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