Enabling Distributed Applications Optimization in Cloud Environment

The past few years have seen dramatic growth in the popularity of public clouds, such as Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), and Container-as-a-Service (CaaS). In both commercial and scientific fields, quick environment setup and application deployment become a mandatory requirement. As a result, more and more organizations choose cloud environments instead of setting up the environment by themselves from scratch. The cloud computing resources such as server engines, orchestration, and the underlying server resources are served to the users as a service from a cloud provider. Most of the applications that run in public clouds are the distributed applications, also called multi-tier applications, which require a set of servers, a service ensemble, that cooperate and communicate to jointly provide a certain service or accomplish a task. Moreover, a few research efforts are conducting in providing an overall solution for distributed applications optimization in the public cloud. In this dissertation, we present three systems that enable distributed applications optimization: (1) the first part introduces DocMan, a toolset for detecting containerized application’s dependencies in CaaS clouds, (2) the second part introduces a system to deal with hot/cold blocks in distributed applications, (3) the third part introduces a system named FP4S, a novel fragment-based parallel state recovery mechanism that can handle many simultaneous failures for a large number of concurrently running stream applications

Sentiment Analysis of Long-term Social Data during the COVID-19 Pandemic

The COVID-19 pandemic has bringing the “infodemic” in the social media worlds. Various social platforms play a significant role in instantly acquiring the latest updates of the pandemic. Social media such as Twitter and Facebook produce vast amounts of posts related to the virus, vaccines, economics, and politics. In order to figure out how public opinion and sentiments are expressed during the pandemic, this work analyzes the long-term social posts from social media and conducts sentiment analysis on tweets within 12 months. Our findings show the trend topics of long-term social communities during the pandemic and express people’s attitudes towards progress of major actions during the pandemic. We explore the main topics during the prolonged pandemic, including information surrounding economics, vaccines, and politics. Besides, we show the differences in gender-based attitudes and propose future research questions refer to the “infodemic”. We believe that our work contributes to attracting public attention to the “infodemic” of the social crisis

AI for Archives: Using Facial Recognition to Enhance Metadata

The goal of this research project was to determine the most effective facial recognition applications that could be implemented into digital archive image collections from libraries, museums, and cultural heritage institutions. Computer scientists and librarians at Florida International University collaborated to conduct qualitative assessments of both face detection and face search using photographs from FIU’s digital collections. Specifically, the facial recognition platforms OpenCV, Face++, and Amazon AWS were analyzed. This project seeks to assist LYRASIS community members who wish to incorporate facial recognition and other artificial intelligence technology into their digital collections and repositories as a method to reduce research time and enhance their collections with more complete metadata

Finite Frequency Vibration Control for Polytopic Active Suspensions via Dynamic Output Feedback

This paper presents a disturbance attenuation strategy for active suspension systems with frequency band constraints, where dynamic output feedback control is employed in consideration that not all the state variables can be measured on-line. In view of the fact that human are sensitive to the virbation between 4–8 Hz in vertical direction, the control based on generalized Kalman-Yakubovich-Popov (KYP) lemma is developed in this specific frequency, in order to achieve the targeted disturbance attenuation. Moreover, practical constraints required in active suspension design are guaranteed in the whole time domain. At the end of the paper, the outstanding performance of the system using finite frequency approach is confirmed by simulation

Finite Frequency Vibration Control for Polytopic Active Suspensions via Dynamic Output Feedback

This paper presents a disturbance attenuation strategy for active suspension systems with frequency band constraints, where dynamic output feedback control is employed in consideration that not all the state variables can be measured on-line. In view of the fact that human are sensitive to the virbation between 4–8 Hz in vertical direction, the control based on generalized Kalman-Yakubovich-Popov (KYP) lemma is developed in this specific frequency, in order to achieve the targeted disturbance attenuation. Moreover, practical constraints required in active suspension design are guaranteed in the whole time domain. At the end of the paper, the outstanding performance of the system using finite frequency approach is confirmed by simulation