Full-fledged Operating System Research Articles

Turnstile File Transfer: A Unidirectional System for Medium-Security Isolated Clusters

Data transfer between isolated clusters is imperative for cybersecurity education, research, and testing. Such techniques facilitate hands-on cybersecurity learning in isolated clusters, allow cybersecurity students to practice with various hacking tools, and develop professional cybersecurity technical skills. Educators often use these remote learning environments for research as well. Researchers and students use these isolated environments to test sophisticated hardware, software, and procedures using full-fledged operating systems, networks, and applications. Virus and malware researchers may wish to release suspected malicious software in a controlled environment to observe their behavior better or gain the information needed to assist their reverse engineering processes. The isolation prevents harm to networked systems. However, there are times when the data is required to move in such quantities or speeds that it makes downloading onto an intermediate device untenable. This study proposes a novel turnstile model, a mechanism for one-way file transfer from one enterprise system to another without allowing data leakage. This system protects data integrity and security by connecting the isolated environment to the external network via a locked-down interconnection. Using medium-security isolated clusters, the researchers successfully developed a unidirectional file transfer system that acts as a one-way “turnstile” for secure file transfer between systems not connected to the internet or other external networks. The Turnstile system (source code available at github.com/monnin/turnstile) provides unidirectional file transfer between two computer systems. The solution enabled data to be transferred from a source system to a destination system without allowing any data to be transferred back in the opposite direction. The researchers found an automated process of transferring external files to isolated clusters optimized the transfer speed of external files to isolated clusters using Linux distributions and commands.

Infusing k-means for securing IoT services in edge computing

Accurate, timely, and safe administration of data from IoT devices is made possible by intelligent computing. As the number of IoT devices proliferates, more and more data will be collected, adding depth and breadth to the existing range of IoT services. Technology that incorporates entire systems on a single integrated circuit has improved to the point where more and more consumer electronics can support full-fledged operating systems. It is impractical to use a single computing model for the entire planet since doing so would cause severe network congestion and security holes. In order to solve this problem, we present a framework that utilizes both blockchain technology and edge computing to provide a lightweight data aggregation and secure data investigation solution for Internet of Things IoT) applications without compromising data security or protection. We use a lightweight data aggregation process at the hub level to keep traffic flowing smoothly and quickly. In order to protect the safety of sensitive data and ensure the precision of IoT AI models, we also offer a multi-level fuzzy hashing process. Preliminary testing demonstrates the viability of the method presented here.

Student assessment in cybersecurity training automated by pattern mining and clustering.

Hands-on cybersecurity training allows students and professionals to practice various tools and improve their technical skills. The training occurs in an interactive learning environment that enables completing sophisticated tasks in full-fledged operating systems, networks, and applications. During the training, the learning environment allows collecting data about trainees’ interactions with the environment, such as their usage of command-line tools. These data contain patterns indicative of trainees’ learning processes, and revealing them allows to assess the trainees and provide feedback to help them learn. However, automated analysis of these data is challenging. The training tasks feature complex problem-solving, and many different solution approaches are possible. Moreover, the trainees generate vast amounts of interaction data. This paper explores a dataset from 18 cybersecurity training sessions using data mining and machine learning techniques. We employed pattern mining and clustering to analyze 8834 commands collected from 113 trainees, revealing their typical behavior, mistakes, solution strategies, and difficult training stages. Pattern mining proved suitable in capturing timing information and tool usage frequency. Clustering underlined that many trainees often face the same issues, which can be addressed by targeted scaffolding. Our results show that data mining methods are suitable for analyzing cybersecurity training data. Educational researchers and practitioners can apply these methods in their contexts to assess trainees, support them, and improve the training design. Artifacts associated with this research are publicly available.

RcdMathLib: An Open Source Software Library for Computing on Resource-Limited Devices.

We developped an open source library called RcdMathLib for solving multivariate linear and nonlinear systems. RcdMathLib supports on-the-fly computing on low-cost and resource-constrained devices, e.g., microcontrollers. The decentralized processing is a step towards ubiquitous computing enabling the implementation of Internet of Things (IoT) applications. RcdMathLib is modular- and layer-based, whereby different modules allow for algebraic operations such as vector and matrix operations or decompositions. RcdMathLib also comprises a utilities-module providing sorting and filtering algorithms as well as methods generating random variables. It enables solving linear and nonlinear equations based on efficient decomposition approaches such as the Singular Value Decomposition (SVD) algorithm. The open source library also provides optimization methods such as Gauss–Newton and Levenberg–Marquardt algorithms for solving problems of regression smoothing and curve fitting. Furthermore, a positioning module permits computing positions of IoT devices using algorithms for instance trilateration. This module also enables the optimization of the position by performing a method to reduce multipath errors on the mobile device. The library is implemented and tested on resource-limited IoT as well as on full-fledged operating systems. The open source software library is hosted on a GitLab repository.

Computational storage: an efficient and scalable platform for big data and HPC applications

In the era of big data applications, the demand for more sophisticated data centers and high-performance data processing mechanisms is increasing drastically. Data are originally stored in storage systems. To process data, application servers need to fetch them from storage devices, which imposes the cost of moving data to the system. This cost has a direct relation with the distance of processing engines from the data. This is the key motivation for the emergence of distributed processing platforms such as Hadoop, which move process closer to data. Computational storage devices (CSDs) push the “move process to data” paradigm to its ultimate boundaries by deploying embedded processing engines inside storage devices to process data. In this paper, we introduce Catalina, an efficient and flexible computational storage platform, that provides a seamless environment to process data in-place. Catalina is the first CSD equipped with a dedicated application processor running a full-fledged operating system that provides filesystem-level data access for the applications. Thus, a vast spectrum of applications can be ported for running on Catalina CSDs. Due to these unique features, to the best of our knowledge, Catalina CSD is the only in-storage processing platform that can be seamlessly deployed in clusters to run distributed applications such as Hadoop MapReduce and HPC applications in-place without any modifications on the underlying distributed processing framework. For the proof of concept, we build a fully functional Catalina prototype and a CSD-equipped platform using 16 Catalina CSDs to run Intel HiBench Hadoop and HPC benchmarks to investigate the benefits of deploying Catalina CSDs in the distributed processing environments. The experimental results show up to 2.2× improvement in performance and 4.3× reduction in energy consumption, respectively, for running Hadoop MapReduce benchmarks. Additionally, thanks to the Neon SIMD engines, the performance and energy efficiency of DFT algorithms are improved up to 5.4× and 8.9×, respectively.

The Role of Edge Computing in Internet of Things

Remarkable advancements in embedded systems- on-a-chip have significantly increased the number of commercial devices that possess sufficient resources to run full-fledged operating systems. This change has extended the potential of the IoT. Many early IoT devices could only collect and send data for analysis. However, the increasing computing capacity of today's devices allow them to perform complex computations on-site, resulting in edge computing. Edge computing extends cloud computing capabilities by bringing services close to the edge of a network and thus supports a new variety of services and applications. In this work, we investigate, highlight, and report on recent advances in edge computing technologies with respect to measuring their impact on IoT. We establish a taxonomy of edge computing by classifying and categorizing existing literature, and by doing so, we reveal the salient and supportive features of different edge computing paradigms for IoT. Moreover, we present the key requirements for the successful deployment of edge computing in IoT and discuss a few indispensable scenarios of edge computing in IoT. Several open research challenges are also outlined.

Our troubles with Linux Kernel upgrades and why you should care

Linux and other open-source Unix variants (and their distributors) provide researchers with full-fledged operating systems that are widely used. However, due to their complexity and rapid development, care should be exercised when using these operating systems for performance experiments, especially in systems research. In particular, the size and continual evolution of the Linux code-base makes it difficult to understand, and as a result, decipher and explain the reasons for performance improvements. In addition, the rapid kernel development cycle means that experimental results can be viewed as out of date, or meaningless, very quickly. We demonstrate that this viewpoint is incorrect because kernel changes can and have introduced both bugs and performance degradations. This paper describes some of our experiences using Linux and FreeBSD as platforms for conducting performance evaluations and some performance regressions we have found. Our results show, these performance regressions can be serious (e.g., repeating identical experiments results in large variability in results) and long lived despite having a large negative effect on performance (one problem was present for more than 3 years). Based on these experiences, we argue: it is sometimes reasonable to use an older kernel version, experimental results need careful analysis to explain why a performance effect occurs, and publishing papers validating prior research is essential.

On the interfacing between QEMU and SystemC for virtual platform construction: Using DMA as a case

In this paper, we present an interface for the hardware modeled in SystemC to access those modeled in QEMU on a QEMU and SystemC-based virtual platform. By using QEMU as the instruction-accurate instruction set simulator (IA-ISS) and its capability to run a full-fledged operating system such as Linux, the virtual platform with the proposed interface can be used to facilitate the co-design of hardware models and device drivers at the early stage of Electronic System Level (ESL) design flow. In other words, by using such a virtual platform, the hardware models and associated device drivers can be cross verified while they are being developed so that malfunctions in the hardware models or the device drivers can be easily detected. Moreover, the virtual platform with the proposed interface is capable of providing statistics of instructions executed, memory accessed, and I/O performed at the instruction-accurate level—thus not only making it easy to evaluate the performance of the hardware models but also making it possible for design space exploration.

A QEMU and SystemC-Based Cycle-Accurate ISS for Performance Estimation on SoC Development

In this paper, we present a fast cycle-accurate instruction set simulator (CA-ISS) for system-on-chip development based on QEMU and SystemC. Even though most state-of-the-art commercial tools have tried very hard to provide all the levels of details to satisfy the different requirements of the software designer, the hardware designer, and even the system architect, the hardware/software co-simulation speed is dramatically slow when co-simulating the hardware models at the register-transfer level (RTL) with a full-fledged operating system (OS). Our experimental results show that the combination of QEMU and SystemC can make the co-simulation at the CA level much faster than the conventional RTL simulation, even with a full-fledged operating system up and running. Furthermore, the statistics indicate that with every instruction executed and every memory accessed since power-on traced at the CA level, it takes 28m15.804s on average to boot up a full-fledged Linux kernel, even on a personal computer. Compared to the kernel boot time reported by Xilinx and SiCortex, the proposed CA-ISS is about 6.09 times faster compared to “SystemC without trace” of Xilinx and about 30.32 times faster compared to “SystemC models converted from RTL” of SiCortex. The main contributions of this paper are threefold: 1) a hardware/software co-simulation environment capable of running a full-fledged OS at the early stage of the electronic system level design flow at an acceptable simulation speed is proposed; 2) a virtual platform constructed using the proposed CA-ISS as the processor model can be used to estimate the performance of a target system from system perspective, which all the previous works, such as QEMU-SystemC, do not provide; and 3) such a virtual platform also provides the modeling capability from the transaction level down to the CA level or the other way around.

Automatic framework generation for hard real-time applications

The communication and tasking infrastructure of a real-time application makes up a significant portion of any modern embedded control system. Traditionally, the tasking and communication constructs are provided by a full-fledged real-time operating system. We present an approach to automatically generate a robust framework for an application from its architectural description. This framework sits atop a Ravenscar-compliant runtime as opposed to a standard real-time operating system. We also present an extension of our approach to support code generation for high-integrity distributed applications.

Tutorial: creating an introductory embedded systems course for the computer science curriculum

It is clear that mobile embedded systems such as cell phones and personal digital assistants (PDAs) comprise a growing market segement. In fact, there is a sea change that has occurred in the area of embedded systems. Somewhat swiftly over the last five years or so, full fledged operating systems are readily available in embedded systems, where not long ago only the higher end embedded systems had such capabilities. Further, these are general purpose operating systems and, where appropriate, deployed with windowing systems as a graphical user interface.This presents an opportunity for computer science students. Formerly, it was something of a leap for them to embark on a career in embedded systems, unless they had received significant hardware training. However, these newer embedded systems provide an environment where such students can contribute significantly using development tools with witch they are familiar. Such a developer can make great progress with only occasional guidance from a hardware guru. Nevertheless, the typical computer science student interested in career in embedded systems make a transition. The development environment for embedded systems is not familiar, although the software tools are.This tutorial will introduce participants to a course targeting computer scientists who wish to make the transition to embedded systems programming. The underlying course is freely available on the Internet, should tutorial participants wish to embark on a detailed course of study or adopt it for their own classroom use. This tutorial will introduce and give a short discussion of these topics:an overview of the embedded system development environmentworking with 'local' targets i.e. floppy diskettes or CDROMs to establish a somewhat prototypical environmentmoving on to working with a more typical 'alien' architecture as the embedded targetconfiguring the workstation for embedded development (software tools etc.)establishing connectivity between work station and targettailoring an operating system destined for the targetadding existing applications/functionality to the targetdeveloping new applications for the targetThe environment for both workstation and target is Linux based to provide maximum flexibility. Participants will be provided with CDs containing the full course.