Channel Resource Allocation for Multi-camera Video Streaming in Vehicular Ad-hoc Networks

This thesis studies the problem of channel resource allocation in a vehicular video communication system. The goal of the work is to investigate the allocation of resources in the case when each user estimates the channel without coordination from other users and then transmits the video data. The analysis in this work has been carried out in two different stages. The first stage, reproduced the simulations conducted in early studies and then extended. In the second stage, an environment has been recreated based on the specifications of the standard IEEE 802.11p where the antennas are static and close to each other, so that packet losses can be caused mostly by congestion and collisions in random multiple access channels. To set up a network completely based on such standards specific antennas adapted to work at the specific frequency have been employed. At both stages, in order to transmit a video stream in the network and to collect the channel estimation information, an instant messaging software has been exploited, among those available we chose to use Skype. This thesis shows results from simulations performed in a real-world environment. These prove that the behavior from the different users in the network has a direct effect on the quality perceived by the users themselves. From this study it was possible to define a new transmission system which involves greater knowledge from users regarding the network. Using this system it would be possible to achieve a more stable and fair level of visual quality for all users involved in the system

OSS PESTO : An Open Source Software Project Evaluation and Selection TOol

Open source software (OSS), playing an increasingly critical role nowadays, has been commonly adopted and integrated in various software products. For many practitioners, selecting and adopting suitable OSS can help them greatly. Though many studies have been conducted on proposing OSS evaluation and selection models, a limited number are followed and used in the industry. Meanwhile, many existing OSS evaluation tools, though providing valuable details, fall short on offering intuitive suggestions in terms of framework-supported evaluation factors. Towards filling the gap, we propose an Open Source Software Project Evaluation and Selection TOol (OSS PESTO). Targeting OSS on Github, the largest OSS source code host, it facilitates the evaluation practice by enabling practitioners to compare candidates therein in terms of selected OSS evaluation models. It also allows in-time Github data collection and customized evaluation that enriches its effectiveness and ease of use.acceptedVersionPeer reviewe

A machine and deep learning analysis among SonarQube rules, product, and process metrics for fault prediction

Background: Developers spend more time fixing bugs refactoring the code to increase the maintainability than developing new features. Researchers investigated the code quality impact on fault-proneness, focusing on code smells and code metrics. Objective: We aim at advancing fault-inducing commit prediction using different variables, such as SonarQube rules, product, process metrics, and adopting different techniques. Method: We designed and conducted an empirical study among 29 Java projects analyzed with SonarQube and SZZ algorithm to identify fault-inducing and fault-fixing commits, computing different product and process metrics. Moreover, we investigated fault-proneness using different Machine and Deep Learning models. Results: We analyzed 58,125 commits containing 33,865 faults and infected by more than 174 SonarQube rules violated 1.8M times, on which 48 software product and process metrics were calculated. Results clearly identified a set of features that provided a highly accurate fault prediction (more than 95% AUC). Regarding the performance of the classifiers, Deep Learning provided a higher accuracy compared with Machine Learning models. Conclusion: Future works might investigate whether other static analysis tools, such as FindBugs or Checkstyle, can provide similar or different results. Moreover, researchers might consider the adoption of time series analysis and anomaly detection techniques.publishedVersionPeer reviewe

Edge to Cloud Tools: A Multivocal Literature Review

Edge-to-cloud computing is an emerging paradigm for distributing computational tasks between edge devices and cloud resources. Different approaches for orchestration, offloading, and many more purposes have been introduced in research. However, it is still not clear what has been implemented in the industry. This work aims to merge this gap by mapping the existing knowledge on edge-to-cloud tools by providing an overview of the current state of research in this area and identifying research gaps and challenges. For this purpose, we conducted a Multivocal Literature Review (MLR) by analyzing 40 tools from 1073 primary studies (220 PS from the white literature and 853 PS from the gray literature). We categorized the tools based on their characteristics and targeted environments. Overall, this systematic mapping study provides a comprehensive overview of edge-to-cloud tools and highlights several opportunities for researchers and practitioners for future research in this area

The anatomy of a vulnerability database: A systematic mapping study

Software vulnerabilities play a major role, as there are multiple risks associated, including loss and manipulation of private data. The software engineering research community has been contributing to the body of knowledge by proposing several empirical studies on vulnerabilities and automated techniques to detect and remove them from source code. The reliability and generalizability of the findings heavily depend on the quality of the information mineable from publicly available datasets of vulnerabilities as well as on the availability and suitability of those databases. In this paper, we seek to understand the anatomy of the currently available vulnerability databases through a systematic mapping study where we analyze (1) what are the popular vulnerability databases adopted; (2) what are the goals for adoption; (3) what are the other sources of information adopted; (4) what are the methods and techniques; (5) which tools are proposed. An improved understanding of these aspects might not only allow researchers to take informed decisions on the databases to consider when doing research but also practitioners to establish reliable sources of information to inform their security policies and standards.publishedVersionPeer reviewe

Workrs: Fault Tolerant Horizontal Computation Offloading

The broad development and usage of edge devices has highlighted the importance of creating resilient and computationally advanced environments. When working with edge devices these desiderata are usually achieved through replication and offloading. This paper reports on the design and implementation of Workrs, a fault tolerant service that enables the offloading of jobs from devices with limited computational power. We propose a solution that allows users to upload jobs through a web service, which will be executed on edge nodes within the system. The solution is designed to be fault tolerant and scalable, with no single point of failure as well as the ability to accommodate growth, if the service is expanded. The use of Docker checkpointing on the worker machines ensures that jobs can be resumed in the event of a fault. We provide a mathematical approach to optimize the number of checkpoints that are created along a computation, given that we can forecast the time needed to execute a job. We present experiments that indicate in which scenarios checkpointing benefits job execution. The results achieved are based on a working prototype which shows clear benefits of using checkpointing and restore when the completion jobs' time rises compared with the forecast fault rate. The code of Workrs is released as open source, and it is available at \url{https://github.com/orgs/P7-workrs/repositories}. This paper is an extended version of \cite{edge2023paper}.Comment: extended version of a paper accepted at IEEE Edge 202

Cloud Continuum: The Definition

publishedVersionPeer reviewe

Applications of MLOps in the Cognitive Cloud Continuum

Background. Since the rise of Machine Learning, the automation of software development has been a desired feature. MLOps is targeted to have the same impact on software development as DevOps had in the last decade. Objectives. The goal of the research is threefold: (RQ1) to analyze which MLOps tools and platforms can be used in the Cognitive Cloud Continuum, (RQ2) to investigate which combination of such tools and platforms is more beneficial, and (RQ3) to define how to distribute MLOps to nodes across the Cognitive Cloud Continuum. Methods. The work can be divided into three main blocks: analysis, proposal and identification, and application. The first part builds the foundations of the work, the second proposes a vision on the evolution of MLOps then identifies the key concepts while the third validates the previous steps through practical applications. Contribution. The thesis’s contribution is a set of MLOps pipelines that practitioners could adopt in different contexts and a practical implementation of an MLOps system in the Cognitive Cloud Continuum.Peer reviewe

Channel Resource Allocation for Multi-camera Video Streaming in Vehicular Ad-hoc Networks

This thesis studies the problem of channel resource allocation in a vehicular video communication system. The goal of the work is to investigate the allocation of resources in the case when each user estimates the channel without coordination from other users and then transmits the video data. The analysis in this work has been carried out in two different stages. The first stage, reproduced the simulations conducted in early studies and then extended. In the second stage, an environment has been recreated based on the specifications of the standard IEEE 802.11p where the antennas are static and close to each other, so that packet losses can be caused mostly by congestion and collisions in random multiple access channels. To set up a network completely based on such standards specific antennas adapted to work at the specific frequency have been employed. At both stages, in order to transmit a video stream in the network and to collect the channel estimation information, an instant messaging software has been exploited, among those available we chose to use Skype. This thesis shows results from simulations performed in a real-world environment. These prove that the behavior from the different users in the network has a direct effect on the quality perceived by the users themselves. From this study it was possible to define a new transmission system which involves greater knowledge from users regarding the network. Using this system it would be possible to achieve a more stable and fair level of visual quality for all users involved in the system