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Thursday, September 26 • 1:30pm - 2:00pm
Profit Optimization for Splitting and Sampling Based Resource Management in Big Data Analytics-as-a-Service Platforms in Cloud Computing Environments

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Yali Zhao (The University of Melbourne), Athanasios Vasilakos (Lulea University of Technology), James Bailey (The University of Melbourne), and Richard Sinnott (The University of Melbourne)

Exploring optimal big data analytics solutions for problem solving in various application domains becomes an ever-important research area. Big data Analytics-as-a-Service (AaaS) platforms offer online AaaS to various domains in a pay-per-use model. Big data analytics incurs expensive costs and takes lengthy processing times due to large-scale computing requirements. To tackle the cost and time challenges for big data processing, we focus on proposing automatic and efficient resource management algorithms to maximize profits and minimize times while guaranteeing Service Level Agreements (SLAs) on Quality of Service (QoS) requirements of queries. For query processing constrained by tight deadlines and limited query budgets, the proposed algorithms enable data splitting and sampling based resource scheduling for parallel and approximate processing that significantly reduce data processing times and resource costs. We formulate the multi-objective optimization resource scheduling problem to maximize profits for AaaS platforms and minimize query response times. We design extensive experiments for algorithm performance evaluation, results show our proposed scheduling algorithms outperform state-of-the-art algorithms that improve query admission rates, maximize profits, minimize query times, provide elastic and automatic large-scale resource configurations to minimize resource costs, and deliver timely, cost-effective, and reliable AaaS with SLA guarantees.


Yali Zhao

The University of Melbourne

Thursday September 26, 2019 1:30pm - 2:00pm PDT
Kon Tiki Room