Final Examination Schedule


Winter 2018

Friday, January 19

Yun-Ming Shih

Chair: Dr. Munehiro Fukuda
Candidate: Master of Science in Computer Science & Software Engineering
10:00 A.M.; UW1 370
MASS HDFS: Multi-Agent Spatial Simulation Hadoop Distributed File System

Parallel computing has been widely used for processing big data, but most systems only handle simple structured data types and do not support complex structured data. As many scientific data are highly structured, such as climatological data, the requirements have to be addressed. UWCA is a web serviced climate analysis application that uses Multi-Agent Spatial Simulation (MASS), a parallelization library created by Distributed Systems Laboratory at Computing & Software Systems Division, University of Washington Bothell (DSLab), for its computations. Without a proper file handling system, the master node becomes a bottleneck causing slow performances. This inspired DSLab to develop the MASS Parallel I/O layer for parallel file reading.

Similar frameworks have been developed to handle structured data in parallel. ROMIO gives a precise control to structured data, but the system has too many features making the system complicated. SciHadoop gives a simple computing model but requires converting science data to text type for processing. Our proposed system, MASS HDFS, is a MASS Parallel I/O layer with additional write functionality and uses HDFS for file distribution. It aims to provide the capability to handle structured data while maintaining simplicity of usage. MASS HDFS can read data into distributed arrays without introducing a single point of bottleneck or data conversion.

The performance evaluation shows that for a 200MB file with up to eight computing nodes, MASS HDFS spends 0.6 seconds on file open and 0.09 seconds on file read.
Opening a 50MB file using 12.5 million Place elements takes 25 minutes. Using 50 million Place elements to open a 50MB file takes four times longer. Reading data with 50 million Place elements can be done within 2.5 minutes, and eight times faster using 12.5 million Place elements. Our project made parallel I/O possible as well as demonstrating the potentiality of processing data on a per-data-item scale using four computing nodes.

Friday, February 23

Miles Dowe

Chair: Dr. David Socha
Candidate: Master of Science in Computer Science & Software Engineering
4:00 P.M.; DISC 464
Informing a Laughter Recognition Algorithm Through Qualitative Coding

Computer-assisted qualitative data analysis software (CAQDAS) are applications that help qualitative research by storing coded data samples in queryable databases. While some CAQDAS use machine learning to simplify the work of a user, it appears no relationship has been demonstrated where qualitative coding applications help to influence machine learning models. In refactoring a laugh finding algorithm to operate at scale, a web application labeled the Laughter Analysis System (LAS) was developed and hosted on Microsoft Azure. It consists of a web interface, a RESTful backend affective computing service, and a relational database. Functioning as a sort of CAQDAS, the LAS enables a user to immediately find and qualitatively code laughter instances in videos from the BeamCoffer corpus, while also providing coded instances as samples for model re-training. New models can then be created, while earlier models can also be reused for refining.

Wednesday, February 28

Clayton Johnson

Chair: Dr. Geetha Thamilarasu
Candidate: Master of Science in Cyber Security Engineering
1:00 P.M.; DISC 464
Software Defined Networking: Modifying the OpenFlow Protocol for Mutual Authentication

The innovation of hardware virtualization has become the foundation for modern data centers worldwide as cloud technology continues to grow in popularity. However, this virtual revolution has yet to transcend to the network infrastructure. Traditional networks require dedicated devices, each with their own control logic that is managed independently. These legacy setups are reaching a critical point where they can no longer keep pace with new services such as Big Data and IoT. In response to this virtual era, Software Defined Networking (SDN) was introduced as a new network architecture which provides improved scalability, reduce maintenance overhead, and higher-level programmable functionality, achieved by decoupling the network control logic from the forwarding devices (switches). Over the last several years this architecture has been explored and a promising protocol known as OpenFlow has been introduced for communicating control logic. The OpenFlow protocol has been widely adopted as the foundation for many SDN solutions; however, this protocol relies heavily on the security of SSL/TLS and is often made optional. In this project, we demonstrate that OpenFlow protocol should provide enhanced security features beyond an optional encrypted channel to serve as the backbone for future networked services. We propose modifications to the OpenFlow protocol such that it provides message integrity, mutual authentication, and confirmation of policy state changes. Using experiments, we show that our solution enhances the security of the SDN architecture and can be easily adopted by the Open Networking Foundation.

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