Access Type

Open Access Dissertation

Date of Award

January 2017

Degree Type

Dissertation

Degree Name

Ph.D.

Department

Electrical and Computer Engineering

First Advisor

Shiyong Lu

Second Advisor

Jiang Song

Abstract

Big data workflows have recently emerged as the next generation of data-centric workflow technologies to address the five “V” challenges of big data: volume, variety, velocity, veracity, and value. More formally, a big data workflow is the computerized modeling and automation of a process consisting of a set of computational tasks and their data interdependencies to process and analyze data of ever increasing in scale, complexity, and rate of acquisition. The convergence of big data and workflows creates new challenges in workflow community.

First, the variety of big data results in a need for integrating large number of remote Web services and other heterogeneous task components that can consume and produce data in various formats and models into a uniform and interoperable workflow. Existing approaches fall short in addressing the so-called shimming problem only in an adhoc manner and unable to provide a generic solution. We automatically insert a piece of code called shims or adaptors in order to resolve the data type mismatches.

Second, the volume of big data results in a large number of datasets that needs to be queried and analyzed in an effective and personalized manner. Further, there is also a strong need for sharing, reusing, and repurposing existing tasks and workflows across different users and institutes. To overcome such limitations, we propose a folksonomy- based social workflow recommendation system to improve workflow design productivity and efficient dataset querying and analyzing.

Third, the volume of big data results in the need to process and analyze data of ever increasing in scale, complexity, and rate of acquisition. But a scalable distributed data model is still missing that abstracts and automates data distribution, parallelism, and scalable processing. We propose a NoSQL collectional data model that addresses this limitation.

Finally, the volume of big data combined with the unbound resource leasing capability foreseen in the cloud, facilitates data scientists to wring actionable insights from the data in a time and cost efficient manner. We propose BARENTS scheduler that supports high-performance workflow scheduling in a heterogeneous cloud-computing environment with a single objective to minimize the workflow makespan under a user provided budget constraint.

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