Traditional Operating Systems Research Articles

HClave: An isolated execution environment design for hypervisor runtime security

Virtualization is the cornerstone of cloud computing, but the hypervisor, the crucial software component that enables virtualization, is known to suffer from various attacks. It is challenging to secure the hypervisor due to at least two reasons. On one hand, commercial hypervisors are usually integrated into a privileged Operating System (OS), which brings in a larger attack surface. On the other hand, multiple Virtual Machines (VM) share a single hypervisor, thus a malicious VM could leverage the hypervisor as a bridge to launch “cross-VM” attacks. In this work, we propose HClave, an isolated execution environment (IEE) design for hypervisor runtime. We decouple the virtualization layer into a tiny trusted computing base (TCB), a large non-secure OS, and multiple HClave IEEs through a bidirectional isolation approach. HClave extends the nested kernel approach to deprive the traditional OS from accessing the tiny TCB’s memory and creates an IEE for hypervisor runtime. We implemented HClave based on KVM and evaluated its effectiveness and efficiency through case studies. Experimental results show that HClave can significantly improve the security of the hypervisor with reasonable runtime overhead.

Theoretical Validation and Hardware Implementation of Dynamic Adaptive Scheduling for Heterogeneous Systems on Chip

Domain-specific systems on chip (DSSoCs) aim to narrow the gap between general-purpose processors and application-specific designs. CPU clusters enable programmability, whereas hardware accelerators tailored to the target domain minimize task execution times and power consumption. Traditional operating system (OS) schedulers can diminish the potential of DSSoCs, as their execution times can be orders of magnitude larger than the task execution time. To address this problem, we propose a dynamic adaptive scheduling (DAS) framework that combines the advantages of a fast, low-overhead scheduler and a sophisticated, high-performance scheduler with a larger overhead. We present a novel runtime classifier that chooses the better scheduler type as a function of the system workload, leading to improved system performance and energy-delay product (EDP). Experiments with five real-world streaming applications indicate that DAS consistently outperforms fast, low-overhead, and slow, sophisticated schedulers. DAS achieves a 1.29× speedup and a 45% lower EDP than the sophisticated scheduler under low data rates and a 1.28× speedup and a 37% lower EDP than the fast scheduler when the workload complexity increases. Furthermore, we demonstrate that the superior performance of the DAS framework also applies to hardware platforms, with up to a 48% and 52% reduction in the execution time and EDP, respectively.

Low-cost vision-based embedded control of a 2DOF robotic manipulator

Simulations are frequently used to validate underactuated control of the planar two-link manipulator, a typical benchmark system. These new control strategies need to be evaluated on a mechanical setup in order to further research their validity. In this work the benchmark system is specifically designed and built to fit on a table and to be as low-cost as possible. One of the large discrepancies between the simulator and the real setup is the presence of friction. With the help of a carefully designed bearing topology and by using image vision to measure the states of the system, as opposed to mechanical sensors, this friction can be kept to a minimum. The image vision requires a computer that is capable to process the images and send the control effort to the actuator in a timely manner. For this purpose a Raspberry Pi 4 is used to interface with the camera. The architecture consists of a traditional Raspbian operating system, which is expanded with Robot Operating System (ROS) and Xenomai. With the latter, tasks can be done in hard real-time, which is needed to achieve a fixed sampling time. In this case, a sampling time of 9ms is reached. An open-loop and closed-loop experiment with PD-controller are performed to validate the software architecture.

Application of Industrial Big Data Cloud Control Platform Based on Fusion Transmission Sensor

In order to solve the problem that the traditional operation and maintenance system of photovoltaic operation and power station is mainly based on statistical analysis and multisite and multilevel deployment mode, which wastes software and hardware resources, is not easy to expand and has extremely low efficiency, which cannot meet the urgent needs of users to reduce costs and increase efficiency. This research proposes a methodology for integrating big data, cloud computing, and photovoltaic operation and maintenance. This method constructs the business model, data model, application model, and technical model of the photovoltaic power station operation and maintenance cloud platform. The results obtained are as follows: the system provides diagnostic services for the application system of a 30 MWp photovoltaic power station in a certain place, and a total of 87 defects are found, the defect elimination rate is 88.51%, and the monthly power generation of the power station is increased by 122,529 kWh; the use effect of the system in this research after it goes online. The evaluation is 93.04 points, ranging from very satisfactory (A) to satisfactory (B) and biased towards A, indicating that the use effect is good. It is proved that the successful research and promotion of the system in this research will be of great significance to improve the intelligent operation and maintenance level of photovoltaic power plants and improve the operation and maintenance efficiency of photovoltaic power plants.

Intrusion detection framework for securing privacy attack in cloud computing environment using DCCGAN‐RFOA

AbstractCloud computing (CC) is vulnerable for the attacks of current information technology, because it prolongs and uses the information technology infrastructure, traditional operating system, and applications. Several security issues in detecting irregular network performances are confronted by the CC environment. With the intention of solving these security issues, dual‐channel capsule generation adversarial network optimized with red fox optimization algorithm fostered intrusion detection framework (IDS‐CC‐DCCGAN‐RFOA) is proposed in this article, for securing the privacy attacks in CC environment. Initially, the data are taken from NSL‐KDD benchmark dataset. Then the data are given to the preprocessing segment. When using the developed random forest and local least squares in preprocessing segment, the redundancy, and missing values are purged. Then the output of preprocessing is given to the feature selection stage. In feature selection, the optimal features are extracted using univariate ensemble feature selection (UEFS) technique. Based on the optimal features, the data are classified into secured data and privacy attack data based on dual‐channel capsule generative adversarial network. Here, the weight of dual‐channel capsule generative adversarial network is optimally tuned using the red fox optimization algorithm for generating an effective and optimal solution to detect the intruders. The proposed IDS‐CC‐DCCGAN‐RFOA method is implemented in MATLAB. Here, the performance metrics, like accuracy, specificity, sensitivity, F‐score, precision, false positive rate, AUC are analyzed. Then, the proposed IDS‐CC‐CCGAN‐WSOA method provides 13.9367%, 11.12%, 13.268%, and 13.739% higher accuracy and 24.54%, 29.76%, 12.87%, and 34.21% higher sensitivity compared with the existing methods, like intrusion detection framework in cloud computing using deep belief network with salp swarm algorithm (IDS‐CC‐DBN‐CSSA), intrusion detection framework in cloud computing using deep neural network with improved genetic algorithm and simulated annealing algorithm (IDS‐CC‐DNN‐IGASAA), intrusion detection for cloud computing using multilayer perceptron neural network and artificial bee colony optimization algorithm (IDS‐CC‐MLPNN‐ABC), and intrusion detection for cloud computing using recurrent convolutional neural network with ant lion optimization algorithm (IDS‐CC‐RCNN‐ALO).

DAS: Dynamic Adaptive Scheduling for Energy-Efficient Heterogeneous SoCs

Domain-specific systems-on-chip (DSSoCs) aim at bridging the gap between application-specific integrated circuits (ASICs) and general-purpose processors. Traditional operating system (OS) schedulers can undermine the potential of DSSoCs since their execution times can be orders of magnitude larger than the execution time of the task itself. To address this problem, we propose a dynamic adaptive scheduling (DAS) framework that combines the benefits of a fast (low-overhead) scheduler and a slow (sophisticated, high-performance but high-overhead) scheduler. Experiments with five real-world streaming applications show that DAS consistently outperforms both the fast and slow schedulers. For 40 different workloads, DAS achieves on average 1.29x speedup and 45% lower EDP compared to the sophisticated scheduler at low data rates and 1.28x speedup and 37% lower EDP than the fast scheduler when the workload complexity increases.

Analysis of Business Decisions that caused Nokia to drop its Legacy and its Rebirth in 2017 using Nostalgic Marketing Approach: A Case Study

Purpose: This article will investigate the causes for Nokia’s failure to stay afloat in the market, as well as how the company resurrected in 2017 by employing a nostalgic or sentimental marketing strategy. Technology management on a strategic level at Nokia Corporation is thoroughly examined and analyzed in this study. Nokia used to be the market’s dominant corporation, leader, and pacesetter until it had a massive market disaster. We inferred that the problem at Nokia was not the absenteeism of advancement, but in its place, it was due to a lack of innovation estimation and a misunderstanding that the requirements in the mobile phone market were not only about displaying a cell phone that makes verdicts, sends messages, and connects to the internet, but also the stage that connects all of these volumes together. Finally, this article describes how Nokia’s revival was achieved through the use of a nostalgic or sentimental marketing strategy. Objectives: We aim to present the reasons behind the failure of Nokia and its return using Nostalgic marketing approach to do a comparison analysis with its competitors and make recommendations to improve the company based on the findings Design/Methodology/Approach: Journals, as well as a variety of internet resources such as websites and blogs, were used to conduct this company analysis. A SWOC Analysis was used to analyses the Nokia corporation. Findings/Result: Till 2008 Nokia was the pioneer in the mobile phone market. Based on the study done it’s clear that Nokia failed to acquire smart phone market because Nokia couldn’t recognize the customer needs, didn’t Analyze the Market Accurately and also lack of implementing innovative technology in its product which customer needs. Nokia was focusing on implementing traditional Symbian operating system to its smart phone but Samsung choose android as its operating systems for its smartphone at the right time, which met the customer requirements. In 2017 Nokia came with nostalgic marketing approach by re-creating its old Nokia 3310 handset with modern features such Bluetooth, GPS, Wifi. Originality/Value: Based on data from secondary sources, this article investigates the reasons why Nokia failed to gain access to the smartphone market, and explores its comeback through nostalgic marketing strategies. Paper Type: Research Case Study.

An efficient Capacitor Bank Operating System for Single Phase Power Factor Correction using Neural Network Estimations

Wastage of electricity occurs in all places starting from a small house electrical loading to a heavy industrial electrical loading. KiloVolt-Ampere Reactive (KVAR) power metering devices are employed in industrial applications for measuring the energy utilization which measure the energy wastage along with it. This urges a consumer to pay for the unutilized or wasted energy as well. To avoid this, certain capacitor bank units are connected to the industrial application motor units. The right choice of capacitor rating are helpful in minimizing the wasted power observation in the KVAR meters. The selection of capacitor rating is analysed with respect to the power factor calculation. The power factor is a derivation of working power to the apparent power in an electrical system. An optimum power factor to be maintained in an electrical system is 1. The motive of the proposed work is to maintain the power factor by selecting an optimum capacitor bank on the operation of an electrical system at various load conditions. The requirement of capacitor bank values get changed with respect to the load given to an electrical system. A neural network based prediction model is employed in the work for estimating the right choice of capacitor bank. The efficiency of the proposed work is verified and found satisfied with a traditional capacitor bank operating system.

Exploration on Curriculum Reform of Operating System Based on Mixed Teaching Mode

Based on Mixed Teaching mode to explore the teaching curriculum reform as a new teaching idea, for the operating system course teaching provides a new train of thought, than in the traditional operating system course teaching mode is difficult to promote students to understand the problem of the course content, the hybrid time flexibility of teaching has the advantage, Traditional classroom teaching and online teaching are organically combined to realize the integration of information technology and curriculum. Teaching has been widely applied in various fields such as corporate training, teaching education, etc, in view of the present application status of the investigation and analysis of related and combined with the special properties of the operating system course, design based on the mixed mode of the operating system course teaching mode, not only enriches the teaching activities, improve the students' interest in learning, improve the learning efficiency and quality, It is also conducive to the cultivation of students' comprehensive ability and improves the quality of teaching.

Chronological salp swarm algorithm based deep belief network for intrusion detection in cloud using fuzzy entropy

AbstractCloud computing is susceptible to the existing information technology attacks, as it extends and uses the traditional operating system, information technology infrastructure, and applications. However, in addition to the existing threats, the cloud computing environment faces various security issues in detecting anomalous network behaviors. In order to resolve the security issues, an effective intrusion detection system named Chronological Salp Swarm Algorithm‐based Deep Belief Network is proposed for detecting the suspicious intrusions in a cloud environment. Accordingly, the proposed Chronological Salp Swarm Algorithm‐based Deep Belief Network is developed by integrating the Chronological concept with the Salp Swarm Algorithm. The optimal solution for detecting the intrusion is revealed using the fitness function, which accepts the minimal error vale as the optimum solution. Here, the weights are optimally tuned by the proposed algorithm to generate an effective and optimal solution for detecting the intruders. The proposed Chronological Salp Swarm Algorithm‐based Deep Belief Network obtained better performance through the facility of exploitation and the exploration in search space. The performance of the proposed method is analyzed using two datasets, namely KDD cup dataset and BoT‐IoT dataset, the comparative analysis is performed with the existing methods, such as Host based intrusion detection system, Deep learning, and Deep Neural Network + Genetic Algorithm. The proposed Chronological Salp Swarm Algorithm‐based Deep Belief Network obtained better performance in terms of accuracy, sensitivity, and specificity, with the values of 0.9618%, 0.9702%, and 0.9307% using KDD cup dataset, and 0.9764%, 0.9824%, and 0.9309% using BoT‐IoT dataset.

Using intelligent prediction machine and dynamic workflow for banking customer satisfaction in IoT environment

Performance of prediction and workflow is very important for enterprise process automation. It has advantages such as increasing the efficiency of banking operations, improving resource utilization and increasing banking flexibility for business execution. The retail banks’ branch offices distributed information technology system. Traditional operating systems have limitations which make it difficult for branch banking to support other new user services. Connecting with other bank systems or computer devices poses a difficulty. The method proposed in this paper is based on artificial intelligence and dynamic workflow technology. The method is implemented by an object-oriented and edge computing method, which could be used to create a flexible banking system in an IoT environment, allowing new transaction business processes to be added and modified, requiring real time in cloud computing in IoT. This makes banking user services flexible and more competitive in terms of achieving customer satisfaction. Through this study, we devised a unified process control module to help users implement flexible banking transaction processes, decreasing banking system development time and error rates during banking system development and maintenance. Our process can also increase the usability of banking computer servers and transaction system stability, giving banking services more flexibility and increased competitiveness.

Design of power line safety operation and maintenance monitoring system based on cloud computing

Aiming at the problem of long response time of the traditional power line operation and maintenance monitoring system, a kind of power line safety operation and maintenance monitoring system based on cloud computing is proposed. The secure access platform is used to connect the mobile terminal and the mobile application server, and the data is stored by means of flash external. The signal transmission of the sensor and the analogue-to-digital converter is realised by the signal amplification operation circuit. The user allocation of the user interface is carried out by the HASH algorithm to realise the safety operation and maintenance monitoring of power lines. Compared with the three existing methods, the results show that the maximum response time of the system is 2.79 s, and the time is the shortest, indicating that the system is more suitable for the operation and maintenance monitoring of power lines.

Design of Ship Visual Communication Teleoperation System Based on Virtual Reality

Ji, Y., 2020. Design of ship visual communication teleoperation system based on virtual reality. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 975–978. Coconut Creek (Florida), ISSN 0749-0208.In view of slow response, small number of users' access and inaccurate anomaly warning of traditional ship remote operation system, this paper designs a ship visual communication remote operation system based on virtual reality. Ceator software is used to build the three-dimensional model of the ship control system. Real-time operation control algorithm is adopted to drive the control program, introducing Vega to develop the ship visual communication remote operation system. Eclipse is used to build the system operation environment. The proposed ship visual communication remote operation system has faster response, larger number of users access, and more accurate anomaly warning is, which makes up for the shortcomings of the traditional system.

OS-Based Energy Accounting for Asynchronous Resources in IoT Devices

Rapid advancements in computing, communication, sensing, and actuation have seen the growth of Internet of Things (IoT) devices in our daily life. One of the fundamental constraints of a typical IoT device is energy as IoT devices rely on a battery. Therefore, it is crucial for their operating system (OS) to be able to accurately account for system-wide energy usage. Specifically, the OS should be able to attribute such accounted energy to the running applications accurately. Traditional OSs have limited capability when it comes to tracking components such as sensors, actuators and network interfaces, as they are often used in an asynchronous fashion. This would make it difficult to conduct energy accounting accurately. This paper proposes a new mechanism to accurately account for the asynchronous energy usage of resources in mobile systems and IoT devices. Our insight is that by accurately relating the application requests with kernel requests to device and corresponding device responses, we can accurately attribute time of use to the requesting process. However, resources such as WiFi reception violate this assumption. In such cases, we can measure usage by the number of bytes in each individual transaction. Using such a hybrid approach, we can account for energy usage with 94% accuracy and perform much better than using each of these models individually.

A New Security Model for Web Browser Local Storage

In recent years, the web browser has taken over many roles of the traditional operating system, such as acting as a host platform for web applications. Web browser storage, where the web applications can save data locally was one of the new functionalities added in HTML5. However, web functionality has increased significantly since HTML5 was introduced. As web functionality increased, so did the threats facing web users. One of the most prevalent threats was the user’s privacy violations. This study examines the existing security issues related to the usage of web browser storage and proposes a new model to secure the data saved in the browser’s storage. The model was designed and implemented as a web browser extension to secure the saved data. The model was experimentally demonstrated and the result was evaluated.

TDCS: a new scheduling framework for real-time multimedia OS

ABSTRACTThe emerging real-time hyper-physical system (CPS), such as autonomous vehicle and live interactive media application, requires time deterministic behaviour. This is challenging to achieve by using the traditional general purpose operating system (GPOS). This paper presents a new design of the real-time operating system (OS) scheduling mechanism called ‘time deterministic cyclic scheduling’ (TDCS) mainly for live multimedia tasks processing. This new scheduler shares a similar philosophy as classic cyclic execution but with flexibility and dynamic configuration. This hybrid design is based on both time-reserved based cyclic execution and priority-based pre-emptive scheduling for mixed criticality applications. The simulation results show that this scheduling scheme can achieve predictable timing behaviour of task delay and jitter under high CPU utilisation. This shows that the proposed scheme is promising for low latency high-performance multimedia censoring tasks that occur in a periodic manner.

An AR Operation and Maintenance System for Data Center

This thesis proposes an operation and maintenance system based on Augmented Reality (AR) technology using in data center equipment room. It includes two subsystems: AR inspection, AR maintenance. The functions include data management during inspection and maintenance; intelligent guidance of inspection operations; all-round identification and display of equipment front-end and back-end data; data tracking and recording of the whole maintenance process; remote expert maintenance assist in real time. It effectively solves the practical problems of low efficiency in traditional operation and maintenance, missed inspection, and high labor costs in operation and maintenance. In addition, this thesis also describes a research case. A traditional operation and maintenance system without AR technology was developed to compare the user experience with the AR operation and maintenance system (AR-OM system). Through actual user feedback data, this thesis makes a detailed analysis of AR technology for data center inspection efficiency and user experience enhancement.

Effect of Algal Cell Immobilization Technique on Sequencing Batch Reactors for Sewage Wastewater Treatment

Sequencing batch reactors (SBRs) are widely used for wastewater treatment for bioremediation of complex pollutants. The present study investigated the ability of algal cell immobilization technique (Chlorella vulgaris) in the improvement of SBRs during sewage wastewater treatment for 2 h before it reenters a non-fresh surface water bodies. The investigated sewage wastewater treatment plants were operating by biological treatment method (extended aeration) as an influent characterized with high nutrients (nitrogen and phosphate). The treatment efficiency of immobilization technique was 54% for (EC), 51% for (TDS), 59% for (TS), 97% for (TSS), 97% for (COD), 98% for (BOD), 98% for (O&G), 98% for (PO4), 98% for (NH3), and 91% for (T.N), and the pH value was 7.84. Also results revealed that the removal efficiency of differed heavy metals was improved by 57% of Cr, 54% of Fe, 56% of Ni, 53% of Cu, 67% of Zn, 100% of Cd, and 100% of Pb. The designing of SBRs with adding algal cell immobilization technique as the last step during treatment of plant influent significantly improved the quality of treated wastewater after only 2–6 h. The obtained results recommended upgrading of traditional operation systems via modern techniques, such as algal immobilization technique or use SBRs with algal cell immobilization.

Review on open source operating systems for internet of things

Internet of Things (IoT) is an environment in which everywhere and every device became smart in a smart world. Internet of Things is growing vastly; it is an integrated system of uniquely identifiable communicating devices which exchange information in a connected network to provide extensive services. IoT devices have very limited memory, computational power, and power supply. Traditional operating systems (OS) have no way to meet the needs of IoT systems. In this paper, we thus analyze the challenges of IoT OS and survey applicable open source OSs.

Vulnerable GPU Memory Management: Towards Recovering Raw Data from GPU

Abstract According to previous reports, information could be leaked from GPU memory; however, the security implications of such a threat were mostly over-looked, because only limited information could be indirectly extracted through side-channel attacks. In this paper, we propose a novel algorithm for recovering raw data directly from the GPU memory residues of many popular applications such as Google Chrome and Adobe PDF reader. Our algorithm enables harvesting highly sensitive information including credit card numbers and email contents from GPU memory residues. Evaluation results also indicate that nearly all GPU-accelerated applications are vulnerable to such attacks, and adversaries can launch attacks without requiring any special privileges both on traditional multi-user operating systems, and emerging cloud computing scenarios.