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Description
With the emergence of edge computing paradigm, many applications such as image recognition and augmented reality require to perform machine learning (ML) and artificial intelligence (AI) tasks on edge devices. Most AI and ML models are large and computational heavy, whereas edge devices are usually equipped with limited computational and storage resources. Such models can be compressed and reduced in order to be placed on edge devices, but they may loose their capability and may not generalize and perform well compared to large models. Recent works used knowledge transfer techniques to transfer information from a large network (termed teacher) to a small one (termed student) in order to improve the performance of the latter. This approach seems to be promising for learning on edge devices, but a thorough investigation on its effectiveness is lacking.
The purpose of this work is to provide an extensive study on the performance (both in terms of accuracy and convergence speed) of knowledge transfer, considering different student-teacher architectures, datasets and different techniques for transferring knowledge from teacher to student.
A good performance improvement is obtained by transferring knowledge from both the intermediate layers and last layer of the teacher to a shallower student. But other architectures and transfer techniques do not fare so well and some of them even lead to negative performance impact. For example, a smaller and shorter network, trained with knowledge transfer on Caltech 101 achieved a significant improvement of 7.36\% in the accuracy and converges 16 times faster compared to the same network trained without knowledge transfer. On the other hand, smaller network which is thinner than the teacher network performed worse with an accuracy drop of 9.48\% on Caltech 101, even with utilization of knowledge transfer.
The purpose of this work is to provide an extensive study on the performance (both in terms of accuracy and convergence speed) of knowledge transfer, considering different student-teacher architectures, datasets and different techniques for transferring knowledge from teacher to student.
A good performance improvement is obtained by transferring knowledge from both the intermediate layers and last layer of the teacher to a shallower student. But other architectures and transfer techniques do not fare so well and some of them even lead to negative performance impact. For example, a smaller and shorter network, trained with knowledge transfer on Caltech 101 achieved a significant improvement of 7.36\% in the accuracy and converges 16 times faster compared to the same network trained without knowledge transfer. On the other hand, smaller network which is thinner than the teacher network performed worse with an accuracy drop of 9.48\% on Caltech 101, even with utilization of knowledge transfer.
ContributorsSistla, Ragini (Author) / Zhao, Ming (Thesis advisor, Committee member) / Li, Baoxin (Committee member) / Tong, Hanghang (Committee member) / Arizona State University (Publisher)
Created2018
Description
Recent trends in big data storage systems show a shift from disk centric models to memory centric models. The primary challenges faced by these systems are speed, scalability, and fault tolerance. It is interesting to investigate the performance of these two models with respect to some big data applications. This thesis studies the performance of Ceph (a disk centric model) and Alluxio (a memory centric model) and evaluates whether a hybrid model provides any performance benefits with respect to big data applications. To this end, an application TechTalk is created that uses Ceph to store data and Alluxio to perform data analytics. The functionalities of the application include offline lecture storage, live recording of classes, content analysis and reference generation. The knowledge base of videos is constructed by analyzing the offline data using machine learning techniques. This training dataset provides knowledge to construct the index of an online stream. The indexed metadata enables the students to search, view and access the relevant content. The performance of the application is benchmarked in different use cases to demonstrate the benefits of the hybrid model.
ContributorsNAGENDRA, SHILPA (Author) / Huang, Dijiang (Thesis advisor) / Zhao, Ming (Committee member) / Maciejewski, Ross (Committee member) / Chung, Chun-Jen (Committee member) / Arizona State University (Publisher)
Created2017
Description
Compartmentalizing access to content, be it websites accessed in a browser or documents and applications accessed outside the browser, is an established method for protecting information integrity [12, 19, 21, 60]. Compartmentalization solutions change the user experience, introduce performance overhead and provide varying degrees of security. Striking a balance between usability and security is not an easy task. If the usability aspects are neglected or sacrificed in favor of more security, the resulting solution would have a hard time being adopted by end-users. The usability is affected by factors including (1) the generality of the solution in supporting various applications, (2) the type of changes required, (3) the performance overhead introduced by the solution, and (4) how much the user experience is preserved. The security is affected by factors including (1) the attack surface of the compartmentalization mechanism, and (2) the security decisions offloaded to the user. This dissertation evaluates existing solutions based on the above factors and presents two novel compartmentalization solutions that are arguably more practical than their existing counterparts.
The first solution, called FlexICon, is an attractive alternative in the design space of compartmentalization solutions on the desktop. FlexICon allows for the creation of a large number of containers with small memory footprint and low disk overhead. This is achieved by using lightweight virtualization based on Linux namespaces. FlexICon uses two mechanisms to reduce user mistakes: 1) a trusted file dialog for selecting files for opening and launching it in the appropriate containers, and 2) a secure URL redirection mechanism that detects the user’s intent and opens the URL in the proper container. FlexICon also provides a language to specify the access constraints that should be enforced by various containers.
The second solution called Auto-FBI, deals with web-based attacks by creating multiple instances of the browser and providing mechanisms for switching between the browser instances. The prototype implementation for Firefox and Chrome uses system call interposition to control the browser’s network access. Auto-FBI can be ported to other platforms easily due to simple design and the ubiquity of system call interposition methods on all major desktop platforms.
The first solution, called FlexICon, is an attractive alternative in the design space of compartmentalization solutions on the desktop. FlexICon allows for the creation of a large number of containers with small memory footprint and low disk overhead. This is achieved by using lightweight virtualization based on Linux namespaces. FlexICon uses two mechanisms to reduce user mistakes: 1) a trusted file dialog for selecting files for opening and launching it in the appropriate containers, and 2) a secure URL redirection mechanism that detects the user’s intent and opens the URL in the proper container. FlexICon also provides a language to specify the access constraints that should be enforced by various containers.
The second solution called Auto-FBI, deals with web-based attacks by creating multiple instances of the browser and providing mechanisms for switching between the browser instances. The prototype implementation for Firefox and Chrome uses system call interposition to control the browser’s network access. Auto-FBI can be ported to other platforms easily due to simple design and the ubiquity of system call interposition methods on all major desktop platforms.
ContributorsZohrevandi, Mohsen (Author) / Bazzi, Rida A (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Doupe, Adam (Committee member) / Zhao, Ming (Committee member) / Arizona State University (Publisher)
Created2018
Description
The Internet of Things ecosystem has spawned a wide variety of embedded real-time systems that complicate the identification and resolution of bugs in software. The methods of concurrent checkpoint provide a means to monitor the application state with the ability to replay the execution on like hardware and software, without holding off and delaying the execution of application threads. In this thesis, it is accomplished by monitoring physical memory of the application using a soft-dirty page tracker and measuring the various types of overhead when employing concurrent checkpointing. The solution presented is an advancement of the Checkpoint and Replay In Userspace (CRIU) thereby eliminating the large stalls and parasitic operation for each successive checkpoint. Impact and performance is measured using the Parsec 3.0 Benchmark suite and 4.11.12-rt16+ Linux kernel on a MinnowBoard Turbot Quad-Core board.
ContributorsPrinke, Michael L (Author) / Lee, Yann-Hang (Thesis advisor) / Shrivastava, Aviral (Committee member) / Zhao, Ming (Committee member) / Arizona State University (Publisher)
Created2018
Description
Blockchain scalability is one of the issues that concerns its current adopters. The current popular blockchains have initially been designed with imperfections that in- troduce fundamental bottlenecks which limit their ability to have a higher throughput and a lower latency.
One of the major bottlenecks for existing blockchain technologies is fast block propagation. A faster block propagation enables a miner to reach a majority of the network within a time constraint and therefore leading to a lower orphan rate and better profitability. In order to attain a throughput that could compete with the current state of the art transaction processing, while also keeping the block intervals same as today, a 24.3 Gigabyte block will be required every 10 minutes with an average transaction size of 500 bytes, which translates to 48600000 transactions every 10 minutes or about 81000 transactions per second.
In order to synchronize such large blocks faster across the network while maintain- ing consensus by keeping the orphan rate below 50%, the thesis proposes to aggregate partial block data from multiple nodes using digital fountain codes. The advantages of using a fountain code is that all connected peers can send part of data in an encoded form. When the receiving peer has enough data, it then decodes the information to reconstruct the block. Along with them sending only part information, the data can be relayed over UDP, instead of TCP, improving upon the speed of propagation in the current blockchains. Fountain codes applied in this research are Raptor codes, which allow construction of infinite decoding symbols. The research, when applied to blockchains, increases success rate of block delivery on decode failures.
One of the major bottlenecks for existing blockchain technologies is fast block propagation. A faster block propagation enables a miner to reach a majority of the network within a time constraint and therefore leading to a lower orphan rate and better profitability. In order to attain a throughput that could compete with the current state of the art transaction processing, while also keeping the block intervals same as today, a 24.3 Gigabyte block will be required every 10 minutes with an average transaction size of 500 bytes, which translates to 48600000 transactions every 10 minutes or about 81000 transactions per second.
In order to synchronize such large blocks faster across the network while maintain- ing consensus by keeping the orphan rate below 50%, the thesis proposes to aggregate partial block data from multiple nodes using digital fountain codes. The advantages of using a fountain code is that all connected peers can send part of data in an encoded form. When the receiving peer has enough data, it then decodes the information to reconstruct the block. Along with them sending only part information, the data can be relayed over UDP, instead of TCP, improving upon the speed of propagation in the current blockchains. Fountain codes applied in this research are Raptor codes, which allow construction of infinite decoding symbols. The research, when applied to blockchains, increases success rate of block delivery on decode failures.
ContributorsChawla, Nakul (Author) / Boscovic, Dragan (Thesis advisor) / Candan, Kasim S (Thesis advisor) / Zhao, Ming (Committee member) / Arizona State University (Publisher)
Created2018
Description
The Coffee Hutch project is derived from the field of Computer Science and consists of a website, a database, and a mobile application for Android devices. This three-tiered scheme is designed to support a point-of-sale payment system to be integrated with a standalone product dispensing machine. The website contains landing pages which provide navigation and functional capabilities for users. The site also features a variety of PHP web services which communicate with the database using SQL commands. The application, programmed in the Java language, makes use of these services in a simple, utilitarian design aimed at modification of user data stored in the database. This database, developed with MySQL and managed with the phpMyAdmin application, contains limited information in order to maximize speed of read and write accesses from the website and Android app. Together, these three components comprise an effective payment management system model with mobile capabilities. All of the components of this project were built at no cost. The website hosting service is free and the third-party services required (such as Paypal payment services) are simulated. These simulations allowed me to demonstrate the functionality of the three-tiered product without the necessity for monetary supplication. This thesis features every aspect of the development and testing of The Coffee Hutch software components. Requirements for each function of the software are specified in one section, and they are aligned with various pieces of the code in the source documentation. Test cases which address each requirement are outlined in another section of the thesis.
ContributorsHutchison, Caleb Ryan (Author) / Burger, Kevin (Thesis director) / Zhao, Ming (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
As robots become more prevalent, the need is growing for efficient yet stable control systems for applications with humans in the loop. As such, it is a challenge for scientists and engineers to develop robust and agile systems that are capable of detecting instability in teleoperated systems. Despite how much research has been done to characterize the spatiotemporal parameters of human arm motions for reaching and gasping, not much has been done to characterize the behavior of human arm motion in response to control errors in a system. The scope of this investigation is to investigate human corrective actions in response to error in an anthropomorphic teleoperated robot limb. Characterizing human corrective actions contributes to the development of control strategies that are capable of mitigating potential instabilities inherent in human-machine control interfaces. Characterization of human corrective actions requires the simulation of a teleoperated anthropomorphic armature and the comparison of a human subject's arm kinematics, in response to error, against the human arm kinematics without error. This was achieved using OpenGL software to simulate a teleoperated robot arm and an NDI motion tracking system to acquire the subject's arm position and orientation. Error was intermittently and programmatically introduced to the virtual robot's joints as the subject attempted to reach for several targets located around the arm. The comparison of error free human arm kinematics to error prone human arm kinematics revealed an addition of a bell shaped velocity peak into the human subject's tangential velocity profile. The size, extent, and location of the additional velocity peak depended on target location and join angle error. Some joint angle and target location combinations do not produce an additional peak but simply maintain the end effector velocity at a low value until the target is reached. Additional joint angle error parameters and degrees of freedom are needed to continue this investigation.
ContributorsBevilacqua, Vincent Frank (Author) / Artemiadis, Panagiotis (Thesis director) / Santello, Marco (Committee member) / Trimble, Steven (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor)
Created2013-05
Description
Stress is one of the critical factors in daily lives, as it has a profound impact onperformance at work and decision-making processes. With the development of IoT
technology, smart wearables can handle diverse operations, including networking and
recording biometric signals. Also, it has become easier for individual users to selfdetect stress with recorded data since these wearables as well as their accompanying
smartphones now have data processing capability. Edge computing on such devices
enables real-time feedback and in turn preemptive identification of reactions to stress.
This can provide an opportunity to prevent more severe consequences that might
result if stress is unaddressed. From a system perspective, leveraging edge computing
allows saving energy such as network bandwidth and latency since it processes data in
proximity to the data source. It can also strengthen privacy by implementing stress
prediction at local devices without transferring personal information to the public
cloud.
This thesis presents a framework for real-time stress prediction using Fitbit and
machine learning with the support from cloud computing. Fitbit is a wearable tracker
that records biometric measurements using optical sensors on the wrist. It also provides developers with platforms to design custom applications. I developed an application for the Fitbit and the user’s accompanying mobile device to collect heart rate
fluctuations and corresponding stress levels entered by users. I also established the
dataset collected from police cadets during their academy training program. Machine
learning classifiers for stress prediction are built using classic models and TensorFlow
in the cloud. Lastly, the classifiers are optimized using model compression techniques
for deploying them on the smartphones and analyzed how efficiently stress prediction
can be performed on the edge.
ContributorsSim, Sang-Hun (Author) / Zhao, Ming (Thesis advisor) / Roberts, Nicole (Committee member) / Zou, Jia (Committee member) / Arizona State University (Publisher)
Created2022
Description
Personalized learning is gaining popularity in online computer science education due to its characteristics of pacing the learning progress and adapting the instructional approach to each individual learner from a diverse background. Among various instructional methods in computer science education, hands-on labs have unique requirements of understanding learners' behavior and assessing learners' performance for personalization. Hands-on labs are a critical learning approach for cybersecurity education. It provides real-world complex problem scenarios and helps learners develop a deeper understanding of knowledge and concepts while solving real-world problems. But there are unique challenges when using hands-on labs for cybersecurity education. Existing hands-on lab exercises materials are usually managed in a problem-centric fashion, while it lacks a coherent way to manage existing labs and provide productive lab exercising plans for cybersecurity learners. To solve these challenges, a personalized learning platform called ThoTh Lab specifically designed for computer science hands-on labs in a cloud environment is established. ThoTh Lab can identify the learning style from student activities and adapt learning material accordingly. With the awareness of student learning styles, instructors are able to use techniques more suitable for the specific student, and hence, improve the speed and quality of the learning process. ThoTh Lab also provides student performance prediction, which allows the instructors to change the learning progress and take other measurements to help the students timely. A knowledge graph in the cybersecurity domain is also constructed using Natural language processing (NLP) technologies including word embedding and hyperlink-based concept mining. This knowledge graph is then utilized during the regular learning process to build a personalized lab recommendation system by suggesting relevant labs based on students' past learning history to maximize their learning outcomes. To evaluate ThoTh Lab, several in-class experiments were carried out in cybersecurity classes for both graduate and undergraduate students at Arizona State University and data was collected over several semesters. The case studies show that, by leveraging the personalized lab platform, students tend to be more absorbed in a lab project, show more interest in the cybersecurity area, spend more effort on the project and gain enhanced learning outcomes.
ContributorsDeng, Yuli (Author) / Huang, Dijiang (Thesis advisor) / Li, Baoxin (Committee member) / Zhao, Ming (Committee member) / Hsiao, Sharon (Committee member) / Arizona State University (Publisher)
Created2021
Description
Demand for processing machine learning workloads has grown incredibly over the past few years. Kubernetes, an open-source container orchestrator, has been widely used by public and private cloud providers for building scalable systems for meeting this demand. The data used to train machine learning workloads can be sensitive in nature, and organizations may prefer to be responsible for their data security and governance by housing it on on-premises systems. Hybrid cloud gives organizations the flexibility to use both on-premises and cloud infrastructure together, leveraging the advantages of both. While there is a long list of benefits, Kubernetes has limitations by design that limit a user’s abilities in a hybrid cloud environment. The Kubernetes control plane does not allow for the management of worker nodes across cloud providers. This boundary puts new responsibilities on the end-user when deploying a hybrid cloud workload. The end-user must create their clusters and specify which cluster the workload will be scheduled to ahead of time. The Kubernetes scheduler will not take the capacity of another cluster into account. To address these limitations, this thesis presents a new hybrid cloud Kubernetes scheduler that can create new clusters on-demand and burst machine learning workloads to a public cloud when on-premises resources are insufficient. Workloads begin scheduling on an on-premises Kubernetes cluster. When the on-premises cluster’s capacity is exhausted, a new Kubernetes cluster is created on-demand in a public cloud provider, and machine learning tasks waiting in the Kubernetes scheduling queue are dynamically migrated to the public cloud provider’s Kubernetes cluster. The public Kubernetes cluster is dynamically sized and auto scaled based on the pending tasks’ demand. When migrating tasks, the data dependencies among tasks are considered, and a region is dynamically chosen to reduce migration time and cost. The scheduler is experimentally evaluated with real-world machine learning workloads, including predicting if a subscriber will stay with a subscription service, predicting the discount needed to retain a subscription customer, predicting if a credit card transaction is fraudulent, and simulated real-world job arrival behavior in a real hybrid cloud environment. Results show that the scheduler can substantially reduce the workload execution time by dynamically migrating tasks from on-premises to public cloud and minimizing the cost by dynamically sizing and scaling the public cluster.
ContributorsKieley, James (Author) / Zhao, Ming (Thesis advisor) / Huang, Dijiang (Committee member) / Zou, Jia (Committee member) / Arizona State University (Publisher)
Created2021