Spring 2021 Thesis Defense Schedule

 

Final Examination Schedule

PLEASE JOIN US AS THE FOLLOWING CANDIDATES PRESENT THEIR CULMINATING WORK.

Spring 2021

For spring quarter 2021, all Final Examination and Defenses will not be held in person due to public health guidelines. For a link to attend a candidate's online defense, please contact our office at stemgrad@uw.edu.

Monday, May 17

Avantika Agarwal

Chair: Dr. Marc Dupuis
Candidate: Master of Science in Cybersecurity Engineering

11:00 A.M.; Online
Project: Comparison of E2EE group chats provided by various communication platforms and implementing RBAC for E2EE group chats

Currently, there are quite a few communication platforms that people all over the world use such as WhatsApp, Signal, Zoom and Google Meet which provide End-to-End Encryption (E2EE) messaging solutions. Given the variety of communication platforms providing encrypted communication solutions, it is crucial to analyze the details of how E2EE works on these communication platforms especially group messaging. The author conducts a literature review and performs practical investigations of encryption strategies used by these communication platforms. The practical investigations are carried out by packet dissection and network analysis of encrypted group messaging traffic. This paper also deep dives into one specific aspect of group messaging – group management. Group messaging brings about interesting challenges with group management for large groups. As the number of people in such groups grow, it may not be easy for a limited set of administrators to carry out all administrative actions. To solve this challenge, this paper proposes a solution to perform group management through Role-based access control (RBAC) for E2EE groups. To demonstrate this protocol, the author has implemented it as an Android application based on top of Signal SDK. This paper also conducts a security and network analysis of the proposed solution.

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Thursday, May 20

Christopher Ijams

Chair: Dr. Marc Dupuis
Candidate: Master of Science in Cybersecurity Engineering

11:00 A.M.; Online
Project: Ethical Penetration Test for AAA Washington

Penetration testing is a type of ethical hacking in which an organization hires a skilled professional to find and exploit vulnerabilities on their network. With the continued rise of cyberattacks, modern best practices indicate that vulnerability scanning and penetration testing are essential for an organization to maintain a secure posture. To remain PCI-DSS compliant, organizations acting as a payment gateway must regularly execute penetration tests on their infrastructure. AAA Washington has expressed a need for an external penetration test on their internet-facing resources. This project sought to perform and document such a test for the organization while establishing a repeatable process for future work. The project identified and exploited vulnerabilities and weak configurations within assets owned by AAA Washington. A methodology tailored explicitly for external penetration testing was established during this process. The test documented here emphasizes interacting with hardened, internet-facing resources and a rigorous inspection of web applications. This project ends with a redacted six-chaptered penetration test report outlining all findings and recommendations for remediation. 

Princeton See

Chair: Dr. Marc Dupuis
Candidate: Master of Science in Cybersecurity Engineering

1:15 P.M.; Online
Project: Control Gap Analysis for AAA Washington

Every organization with data to protect needs to ensure that they have controls in place to mitigate or minimize cyber threats and risks. Due to the evolving nature of the cybersecurity threat landscape, a yearly risk assessment is crucial for keeping up to date with the latest attacks. As part of a larger risk assessment, the control gap analysis allows an organization to perform a detailed breakdown of how the controls in place measure up to commonplace standards. AAA Washington plans to migrate into the hybrid-cloud environment and has requested for a control gap analysis to be conducted on their organization. This project used the CIS Top 20 Control Standards as the base of the gap analysis and will also devise a theoretical risk model to assist in standardizing the current risks to the organization. The goal of the project is the creation of a risk assessment document that is accepted by AAA Washington and used as its reference for future years. The successful implementation of the theoretical risk model may see it adopted for use in yearly risk assessments.

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Friday, May 21

Sanjusha Cheemakurthi

Chair: Dr. Min Chen
Candidate: Master of Science in Computer Science & Software Engineering

11:00 A.M.; Online
Project: Mobile application for sign language learning

Sign language plays a significant role in bridging the communication gap between the deaf, mute and general public. Though there are sign language learning applications available these days, there is a considerable deficiency in the area of real time testing. This research aims to provide a solution for this challenge by building a real time sign language learning application on mobile devices. This iOS application is used to learn alphabets and digits of American sign language (ASL) and it also helps users to evaluate their skills in real time. To facilitate real time evaluation, a machine learning model is trained using ASL image dataset. This dataset is a combination of static sign language gestures captured using an iPhone, dataset developed by Massey University and publicly available ASL alphabet from Kaggle. Preprocessing is performed to bring all the images in the dataset to a common size. Later, segmentation is performed to subtract the background using skin color detection. These processed images are used to train the model using Convolutional neural network. The model consists of multiple convolutional layers and filters which are helpful in extracting the features and training the model effectively. The proposed framework is successfully implemented on smart phone platform and performs with an average testing accuracy of 99.7% using 5-fold cross-validation and evaluation accuracy of 70%.

Satine Paronyan

Chair: Dr. Munehiro Fukuda
Candidate: Master of Science in Computer Science & Software Engineering

3:30 P.M.; Online
Project: Agent-Based Computational Geometry

The Multi-Agent Spatial Simulation (MASS) library is a parallel programming library that uses agent-based modeling (ABM) parallelization approach over a distributed cluster. The MASS library contains several applications solving computational geometry problems using ABM algorithms. This research aims to build additional four ABM algorithm-based applications: (1) range search, (2) point location, (3) largest empty circle, and (4) Euclidean shortest path. This research presents ABM solutions implemented with MASS library as well as divide and conquer (D&C) solutions to four problems implemented with big data parallelization platforms MapReduce and Spark. In this paper, we discuss design approaches used in solutions for the four problems. We present ABM and D&C algorithms with MASS, MapReduce, and Spark platforms. We provide a detail analysis of programmability and execution performance metrics of ABM algorithm-based implementations with MASS against D&C algorithm-based versions with MapReduce and Spark. Results showed that MASS library provides an intuitive approach to developing parallel solutions to computational geometry problems. We observed that ABM MASS solutions produce competitive performance results when performing computations in-memory over distributed structured datasets.

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Monday, May 24

Xiaotian (Alex) Li

Chair: Dr. Munehiro Fukuda
Candidate: Master of Science in Computer Science & Software Engineering

8:45 A.M.; Online
Project: Agent-based parallelization of multidimensional semantic database model

In normal database systems, people always ignore the semantic meaning in autonomous databases. It is not easy to extract significant information based on different querying contexts from a database system. Mathematical Model of Meaning (MMM) is a meta-database system that extracts features from the database and explains the database by those features. It provides users with the capabilities to extract significant information under different semantic spaces. The semantic space is created dynamically with user-defined impression words to compute semantic equivalence and similarity between data items. MMM computes semantic correlations between the key data item and other data items to achieve dynamic data querying.

Multi-Agent Spatial Simulation library (MASS) is a parallel programming library that utilizes agent-based modeling (ABM) to parallelize big data analysis. This project presents parallel solutions to improve the performance of MMM using MASS. Multiple parallel solutions were implemented to improve the efficiency of MMM. Compared to the sequential MMM program, the parallel solution using MASS achieved 23 times speedup over the sequential program on matrix multiplication. MASS also reduced the processing time of distance sorting of multidimensional vectors by 23.70%. Additionally, this work also conducted benchmark analysis between MASS and MPI Java to indicate the advantages of agent-based behavior. It also performed the quantitative and qualitative programmability analysis regarding boilerplate ratio, number of extra classes and functions and developing effort. 

Sneha Manchukonda

Chair: Dr. Min Chen
Candidate: Master of Science in Computer Science & Software Engineering

3:30 P.M.; Online
Project: Classification of Customer Reviews

Fake review is a review written by someone who has not used the product or the service. Fake reviews are designed to give false impression to the customer during the time of purchasing. Emotion, competition, and intellectual laziness are some of the reasons for writing fake reviews. They effect the purchase decisions and result in the financial losses to the customers. Therefore, an effective solution is necessary to identify the fake reviews. Existing approaches of fake reviews detection consists of a machine learning models with features related to reviewer, review, and social. The problem with the existing approaches is that the model and the features to the model are website dependent. Different websites like Yelp, Amazon, Trip Advisor consist of different levels of user meta data information for the reviews. Some websites might be containing rating, name, verified purchase in the reviews section while others might not. To address this issue, we develop two uber machine learning models, that can be plugged into any website, which takes text of the review. The textual features obtained from reviews are fed to the machine learning models to predict the classification of review. The average accuracy of fake reviews detection among different websites is 80%. In the future, any website can extend the current machine learning model, by adding more reviewer features pertaining to that website, for greater accuracy.

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Wednesday, May 26

Robert Laurenson

Chair: Dr. Clark Olson
Candidate: Master of Science in Computer Science & Software Engineering

11:00 A.M.; Online
Thesis: Method of Adding Color Information to Spatially-Enhanced, Bag-of-Visual-Words Models

This thesis provides a late-fusing method, based on the HoNC (Histogram of Normalized Colors) descriptor, for combining color with shape in spatially-enhanced-BOVW models to improve predictive accuracy for image classification. The HoNC descriptor is a pure color descriptor that has several useful properties, including the ability to differentiate achromatic colors (e.g., white, grey, black), which are prevalent in real-world images, and to provide illumination intensity invariance. The method is distinguishable from prior late-fusing methods that utilize alternative descriptors, e.g., hue and color names descriptors, that are lacking with respect to one or both of these properties. The method is shown to boost the predictive accuracy by between about 1.9% - 3.2% for three different spatially-enhanced BOVW model types, selected for their suitability for real-time use cases, when tested against two datasets (i.e., Caltechl0l, Caltech256), across a range of vocabulary sizes. The method adds between about 150 - 190 mS to the model's total inference time.  

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Thursday, May 27

Zican Li

Chair: Dr. Wooyoung Kim
Candidate: Master of Science in Computer Science & Software Engineering

11:00 A.M.; Online
Project: Network Motif Detection: Toward efficient and complete package

Network motifs, with their statistical significance, are frequent and unique subgraph patterns in a network. Although various algorithms for network motif detection have been introduced, a traditional approach includes the following three steps. First, it enumerates all the occurrences of subgraph patterns in a given size from the original graph. After that, it generates a large number of random graphs with the same degree sequence as the original graph and repeats the first step. Lastly, it determines the significance of subgraph patterns through statistical analysis. In 2007, Grochow and Kellis proposed a new approach which is a motif-centric method and differentiated it from the traditional approach as a network-centric approach. While the network-centric approach finds network motifs from a given input, the motif-centric approach finds the occurrence of a specific subgraph pattern from the original network. To provide both approaches in one platform, we developed a web-based program, Nemo, which includes both network-centric and motif-centric programs.

On the other hand, while we were investigating the network-centric approach for a possible improvement of computational efficiency, we realized the second step in the traditional approach can be greatly improved. The second step is to generate a large number of random graphs so that the network motif can be determined by comparing the frequency in the random pool. Wernicke classified it as EXPLICIT because it generates random graphs explicitly. He, then, proposed an alternative algorithm named DIRECT, which determines network motifs without explicit graph generation. However, it was never adapted to detecting network motifs in practice due to its ambiguous statistical testing method. Here, we investigated DIRECT method, implemented the algorithm with different statistical measurement, and applied to various biological networks to detect network motifs. Experiment results support that for subgraphs in small sizes, the DIRECT method is a feasible alternative for EXPLICIT, since they have consistent results, but with superior performance.

Additionally, we added the motif-centric algorithm and the DIRECT method of network-centric approach as an extension to an open-source library NemoLib, which originally contained a network-centric approach with EXPLICIT random generation method only. We expect the NemoLib with the additional features can be useful to accelerate the use of network motifs in real-world applications.

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Friday, May 28

Iswarya Hariharan

Chair: Dr. Erika Parsons
Candidate: Master of Science in Computer Science & Software Engineering

3:30 P.M.; Online
Project: Associate-Degree-Plan scheduling and recommendation system improvement for Virtual Academic Advisor system

Community college students come from diverse backgrounds and experience levels.  They begin their education path pursuing a degree in a major of their choice. Most students aim to get transferred to certain universities, an academic path that demands to fulfill specific requirements, which makes students eligible for the transfer. Academic advisors at community colleges help students in creating academic plans trying their best to incorporate students’ interests, life constraints, and background. Being a heavily manual process that demands experience and familiarity with the process, there is a clear need to automate this process. The Virtual Academic Advisor (VAA) system aims to address the problem of automating academic plan creation for community colleges. The VAA is a research project paired with the development of an interactive software system that supports creating and displaying academic plans based on the needs and preferences of students. Work previously done by various students, focused on automated recommendation of core courses for targeted majors. However, no research or development has been done to incorporate selection of elective-course choices when generating an academic plan, nor a clear strategy on how to integrate elective-recommendation with the VAA system has been outlined.  Incorporating electives opens up a whole new research aspect of automated scheduling.  Furthermore, elective-course selection is crucial for scheduling associate degrees plans.  Associate degrees are offered by community colleges and students can earn such a degree before/without getting transferred to a university.  In this capstone project, we incorporate the logic and functionality of scheduling elective courses along with the core courses to generate associate degree schedules for the intended major and university of the student. We gather and collect the necessary data for the elective courses and test our scheduler for the associate degree schedules. This project also addresses the research and implementation necessary to generate alternative-schedule recommendations and its integration with the VAA system using APIs. This will assist students in exploring alternate academic paths.

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Tuesday, June 1

Daniel Blashaw

Chair: Dr.  Munehiro Fukuda
Candidate: Master of Science in Computer Science & Software Engineering

8:45 A.M.; Online
Project: Interactive Environment to Support Agent-Based Graph Programming in MASS Java

MASS is an Agent-Based Modeling (ABM) library that allows for parallelized simulation over a distributed computing cluster. In these simulations, Place objects act as the environment for Agents to interact with and may be internally organized as multi-dimensional arrays, graphs, or trees. Graph-based simulations are best suited for systems where Places’ relationship to one another may dynamically change or where Places have an indefinite number of neighbors, such as in social networks. However, these graphs are often very complex and present increased difficulty of debugging and verification for the programmer. To address this problem, the goal of this project is to extend the MASS Java library to include a development environment which allows the programmer to step through a graph-based ABM and visually inspect associated Places and Agents. To accomplish this successfully, we have incorporated Java’s JShell for line-by-line execution, checkpointing, and rollback of a simulation; expanded MASS-Cytoscape integration with a full control panel, Agent visualizations, and choice to view subgraphs from MASS; and added Agent Tracking functions to the MASS API. These additions result in a development environment which allows programmers the flexibility to rapidly explore and iterate graph-based ABMs, free to focus on the logic of their simulations and not the infrastructure needed to validate their output. Further, although the functionality discussed in this project were designed for graph-based ABMs, their implementation benefits many other non-graph applications and provide a solid foundation for further expansion of the MASS Java library, such as with real-time cluster monitoring and visualization of other simulation data structures.

Jeffy Jahfar Poozhithara

Chair: Dr.  Hazeline Asuncion
Candidate: Master of Science in Computer Science & Software Engineering

11:00 A.M.; Online
Thesis: Automated Vulnerability Prediction in Software Systems and Lightweight Identification of Design Patterns in Source Code

The heavy investment in terms of cost and time that software development companies put into post-production support required for fixing security vulnerabilities in their products demands the need for an automated mechanism to identify these security vulnerabilities during and after software development. Such an approach can help in reducing these costs by including corresponding solutions like security design patterns when making architectural decisions. This will reduce system-wide architectural changes required post-development and enable efficient documentation and maintenance of the software systems. As part of this research, we created a natural language processing-based model that predicts security vulnerabilities in software systems using keywords and n-grams extracted from software documentation. We analyzed the correlation of certain keywords and n-grams with the occurrence of various security vulnerabilities as well as the correlation between different vulnerabilities. Additionally, we analyzed the performance of classification algorithms (Logistic Regression, Support Vector Machines, K-Nearest Neighbors, Multi-level perception, and Random Forest) in the prediction. To enable the analysis, we also created a dataset by mapping over 200,000 vulnerability reports on the CVE website with technical/functional documentation of 3602 products. The preliminary analysis shows that the performance of the documentation-based predictor is comparable or better than the prediction using source code as well as other static analysis methods. Further, identifying which design patterns already exist in source code can help maintenance engineers determine if new requirements can be satisfied. There are current techniques for finding design patterns in source code, but some of these techniques require manually labeling training datasets, or manually specifying rules or queries for each pattern. To address this challenge, we introduce PatternScout, a technique for automatically generating SPARQL queries by parsing UML diagrams of design patterns, ensuring that pattern characteristics are matched. We discuss key concepts and the design of PatternScout. Our results indicate that PatternScout can automatically generate queries for the three types of design patterns (i.e., creational, behavioral, structural), with accuracy that is comparable, or perform better than, existing techniques.

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Wednesday, June 2

Jiashun Gou

Chair: Dr.  Munehiro Fukuda
Candidate: Master of Science in Computer Science & Software Engineering

11:00 A.M.; Online
Project: Containerization Support for Multi-Agent Spatial Simulation

The Multi-Agent Spatial Simulation (MASS) library is a parallel-computing library designed to execute applications over a cluster of computing nodes. In this capstone project, we aim to improve MASS developers’ experience by adding containerization support to the MASS library and its applications as well as adding a Continuous Integration/Continuous Delivery (CI/CD) pipeline to all related code repositories. First, we designed and implemented containerization support for two versions of MASS libraries and three sample containerized MASS applications. Second, we added a CI/CD pipeline to each code repository of containerized MASS library and MASS applications. Third, we evaluated implementation of the containerized MASS library and applications from five aspects, including reliability, usability, efficiency, maintainability, and portability. In comparison to the original MASS, the containerized MASS library and its applications demonstrates a noticeable increase in usability and maintainability. The project successfully carries out two achievements: (1) containerized MASS and applications provides MASS developers a consistent developing environment and (2) the CI/CD pipeline simplifies MASS developers’ workload, especially testing and releasing procedures.

Manjusha Kalidindi

Chair: Dr.  William Erdly
Candidate: Master of Science in Computer Science & Software Engineering

3:30 P.M.; Online
Project: Virtual reality-based tools for vision therapy (VT) for near-vision disorders

Strabismus (crossed eyes) is one of the most common eye conditions in children. If left untreated, it can lead to Amblyopia, commonly known as lazy eye. To regain binocular vision, a person with strabismus requires training in five levels of fusion skills -- each level indicating progression in ability and vision complexity. The project aims to use virtual reality (VR) to provide an environment for individualized, supervised therapy for children and adults suffering from strabismus to regain binocular vision. The scope of the project is to achieve first two fusion skills --“Luster” and “Simultaneous Perception.” This pilot project is the first of a new toolkit of VR therapy activities built at the UW Bothell EYE Center for Children’s Learning, Vision, and Technologies (the “EYE Center”). The use of model-view-controller (MVC) architectural design with an object-oriented architectural style helped the project achieve simplicity, portability, readability, and expandability. In addition, the project adopted the Agile software development methodology with Scrum and Kanban frameworks, allowing for engaged and accelerated development. Clinician and patient surveys are conducted to evaluate the success of the project. Future steps include designing tools for the remaining three fusion skills as well as completing additional usability and design studies. 

Keywords: Virtual Reality, Vision Therapy, Strabismus, Serious Games

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Thursday, June 3 

Nathan Ranno

Chair: Dr. Dong Si
Candidate: Master of Science in Computer Science & Software Engineering

3:30 P.M.; Online
Project: Enabling Deep Geometric Learning on Cryo-EM Maps Using Neural Representation

Advances in imagery at atomic and near-atomic resolution, such as cryogenic electron microscopy (cryo-EM), have led to an influx of high resolution images of proteins and other macromolecular structures to data banks worldwide. Producing a protein structure from the discrete voxel grid data of cryo-EM maps involves interpolation into the continuous spatial domain. We present a novel data format called the neural cryo-EM map, which is formed from a set of neural networks that accurately parameterize cryo-EM maps and provide native, spatially continuous data for density and gradient. As a case study of this data format, we create graph-based interpretations of high resolution experimental cryo-EM maps. Normalized cryo-EM map values interpolated using the non-linear neural cryo-EM format are more accurate, consistently scoring less than 0.01 mean absolute error, than a conventional tri-linear interpolation, which scores up to 0.12 mean absolute error. Our graph-based interpretations of 115 experimental cryo-EM maps from 1.15 to 4.0 Angstrom resolution provide high coverage of the underlying amino acid residue locations, while accuracy of nodes is correlated with resolution. The nodes of graphs created from atomic resolution maps (higher than 1.6 Angstrom) provide greater than 99% residue coverage as well as 85% full atomic coverage with a mean of than 0.19 Angstrom root mean squared deviation (RMSD). Other graphs have a mean 84% residue coverage with less specificity of the nodes due to experimental noise and differences of density context at lower resolutions. The fully continuous and differentiable nature of the neural cryo-EM map enables the adaptation of the voxel data to alternative data formats, such as a graph that characterizes the atomic locations of the underlying protein or macromolecular structure. Graphs created from atomic resolution maps are superior in finding atom locations and may serve as input to predictive residue classification and structure segmentation methods. This work may be generalized for transforming any 3D grid-based data format into non-linear, continuous, and differentiable format for the downstream geometric deep learning applications.

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Friday, June 4

Shawn Quinn

Chair: Dr.  Clark Olson
Candidate: Master of Science in Computer Science & Software Engineering

11:00 A.M.; Online
Project: An Autonomous Mobile Robot Software System: Embedded Real-time Perception and Control, Motion Tracking, Optical Flow, Visual Odometry, and Localization

Advanced robotics development requires software at multiple levels of sophistication and complexity, from low level embedded code up to high level algorithm implementations. Computer vision and image processing play an important role in advanced robotics applications. Object tracking, visual navigation, and simultaneous localization and mapping (SLAM) allow a robot to perceive and move through physical space without prior knowledge of it’s environment and surroundings. Recent advances in microcontrollers and edge computing hardware enable execution of highly complex algorithms in embedded systems using a multiple controller and processor architecture. Small, unmanned ground vehicles (UGV) and unmanned aerial vehicles (UAV), or drones, benefit from these computing advances by executing sophisticated sensing and control code without dependence on offline/cloud-based computing resources. This project focuses on several key areas of the robotics development stack, specifically, Robot Operating System (ROS), real-time operating systems (RTOS), locomotion, cameras, motion tracking via optical flow, and visual odometry and localization. Design and architectural trade-offs are discussed, implementations are presented, and inter-relationships between the various elements are examined.

Soheli Sultana

Chair: Dr. Geethapriya Thamilarasu 
Candidate: Master of Science in Computer Science & Software Engineering

3:30 P.M.; Online
Project: Security and Privacy of Smart Home Using Software Firewall at Home Gateway

The rapid development of Internet of Things technology has led to growing the popularity of smart home applications. Smart homes promise to significantly enhance domestic comfort by minimizing user intervention to monitor and control home appliances. However, use of heterogeneous IoT devices and multiple users in smart home environments gives rise to unique security, privacy and usability challenges. In addition, cloud based IoT architectures also contribute to security and privacy concerns as cloud services do not address attacks at the home gateway. Existing solutions for smart home security often rely on use of network-layer firewalls. However, network-layer firewalls do not provide adequate security as they don’t verify the payload content, potentially resulting in smart home devices being compromised. In this project, we design and develop an application layer software firewall on top of the network-layer firewall to enhance the security of smart home networks. The proposed application layer software firewall runs on the smart home device gateway as an embedded server, monitors all the network traffic and works as a proxy to control access to smart home device networks. Our software firewall solution enables user authority to define firewall rules. This provides a more sophisticated way to control and configure smart home devices protecting them from external attacks. In addition, the software firewall rules are well designed for interactions between and use by multiple people. We simulate various attacks to evaluate the performance of the software firewall rules. Experimental results show that application-layer based firewalls are able to successfully mitigate the attacks and help provide enhanced layer of security in smart home environments.

Anish Prasad

Chair: Dr.  Yang Peng
Candidate: Master of Science in Computer Science & Software Engineering

5:45 P.M.; Online
Thesis: A Latency-Aware Provisioning Solution for Mobile Inference Serving at the Edge

With the advancement of machine learning (ML), a growing number of mobile clients rely on ML inference for making time-sensitive and safety-critical decisions. Therefore, the demand for high-quality and low-latency inference services at the network edge has become the key to the modern intelligent society. This paper proposes a novel provisioning solution for reducing the overall latency of mobile inference on edge servers. Unlike existing solutions that either direct inference requests to the nearest edge server or balance the workload between edge servers, the solution we propose provisions each edge server with the optimal type and number of inference serving instances under a holistic consideration of networking, computing, and memory resources. Mobile clients can thus utilize ML inference services on edge servers that offer minimal inference serving latency. The proposed solution has been implemented using TensorFlow Serving and Kubernetes on a cluster of edge servers, including Nvidia Jetson Nano and Jetson Xavier. We demonstrate the proposed solution's effectiveness in reducing the overall inference latency under various system parameters and practical system settings through simulation and testbed experiments, respectively.

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Questions: Please email cssgrad@uw.edu