The New Jersey Institute of Technology's
Electronic Theses & Dissertations Project

Title:	Performance optimization of big data computing workflows for batch and stream data processing in multi-clouds
Author:	Cao, Huiyan
View Online:	njit-etd2020-089 (xvi, 133 pages ~ 8.1 MB pdf)
Department:	Department of Computer Science
Degree:	Doctor of Philosophy
Program:	Computer Science
Document Type:	Dissertation
Advisory Committee:	Wu, Chase Qishi (Committee chair) Borcea, Cristian (Committee member) Chen, Yi (Committee member) Ding, Xiaoning (Committee member) Basu Roy, Senjuti (Committee member)
Date:	2020-12
Keywords:	Big data Cloud computing Distributed systems Performance optimization Scientific workflows Workflow mapping
Availability:	Unrestricted
Abstract:	Workflow techniques have been widely used as a major computing solution in many science domains. With the rapid deployment of cloud infrastructures around the globe and the economic benefits of cloud-based computing and storage services, an increasing number of scientific workflows have migrated or are in active transition to clouds. As the scale of scientific applications continues to grow, it is now common to deploy various data- and network-intensive computing workflows such as serial computing workflows, MapReduce/Spark-based workflows, and Storm-based stream data processing workflows in multi-cloud environments, where inter-cloud data transfer oftentimes plays a significant role in both workflow performance and financial cost. Rigorous mathematical models are constructed to analyze the intra- and inter-cloud execution process of scientific workflows and a class of budget-constrained workflow mapping problems are formulated to optimize the network performance of big data workflows in multi-cloud environments. Research shows that these problems are all NP-complete and a heuristic solution is designed for each that takes into consideration module execution, data transfer, and I/O operations. The performance superiority of the proposed solutions over existing methods are illustrated through extensive simulations and further verified by real-life workflow experiments deployed in public clouds.