The RESTless cloud

Operating system is a compulsory subject students STMIK Indonesia Padang. This course describes computer's operating system for control and management of hardware and computer software. Computer operating system is located on the first layer of hard disk drive. When computer is turned on, software first work is operating system and other new applications are processed. The results of observations on STMIK Indonesia Padang, 30% of students have not been able to understand the subject of Operating System. Instructional media should be formulated so that material is easy to understand. The computer as a learning tool Operating System can be made interactive learning media applications. Many interactive CD Operating System in circulation, only contains general material and do not develop subject of discussion. Research objective of designing learning media Operating System using Macromedia Flash, which can be used as ingredients in learning process for students. The subjects were students STMIK Indonesia Padang subjects Operating Systems.

This paper analyzes the optimization features of machine learning (ML) model training procedures using multi-GPU systems to enhance cyber security in telecommunication networks. A key aspect of the study is the use of data parallelism, which allows the distribution of the training load across multiple GPUs, significantly reducing training time and improving model accuracy-critical factors for rapid threat detection in cyberspace. A novel approach for optimizing data batch size using Mutual Information (MI) is proposed, which harmonizes the utilization of computational resources with the information content of the training data. MI helps to determine the optimal data batch size that minimizes training errors and improves model accuracy without a significant increase in training time. Experimental results demonstrate the substantial advantages of multi-GPU configurations compared to single-GPU setups, providing faster training and improved model accuracy. It was particularly emphasized that MI-guided batch size tuning significantly outperforms traditional manual tuning methods, ensuring higher validation accuracy and reducing training time. The study showed that the MI-based approach is an effective tool for addressing the problem of optimizing ML model training processes in real-world scenarios where cyber security is critical. The proposed methods allow ML models to train faster and more accurately identify potential threats, making them particularly relevant for telecommunication networks where a rapid response to new threats in real time is required. The implementation of modern computational technologies such as multi-GPU systems and MI-optimized training enhances the efficiency and accuracy of machine learning models. This, in turn, improves cyber security measures and ensures a more reliable defence of telecommunication networks against malicious attacks. It is noted that the proposed approaches can be adapted not only for cyber security but also for other areas where high model accuracy and fast training are important. Future research prospects include the development of new machine learning methods, particularly deep neural networks, the exploration of alternative computational architectures such as quantum computing or distributed systems, and their integration into real-time systems. Special attention should be paid to the ethical aspects of implementing automated cyber security systems, particularly in preventing bias in algorithms and ensuring fairness in their application.

The trend of softwarization within networks, such as SDN, NFV, and 5G, has increased the need for a network operating system and all its associated elements. In this paper we discuss how the layering and abstractions that commonly appear within computer operating systems are missing within the domain of networking. As such, the lack of abstractions means that interacting with and managing networks has already become a difficult and sometimes cumbersome task. The softwarization process is magnifying this effect. The abstractions that appear in operating systems hide the underlying features of the hardware, presenting elements to programmers and system managers that are easier to understand and to interact with. In this sense, the networking world is far behind. We present some of the lessons learned from these operating system abstractions and consider what could appear in the networking world.

Consolidating multiple virtual machines into a single server has been widely adopted in cloud computing to improve system utilization. However, the sharing of physical CPUs among virtual machines in consolidated systems poses a new challenge in providing an illusion of continuous CPU execution to the guest operating systems (OS). Due to the time-sharing of physical CPUs, the execution of a guest OS is periodically interrupted, while the guest OS may not be aware of the discontinuity of virtual time against the real time. The virtual time discontinuity problem causes the delayed processing of critical OS operations, such as interrupt handling and lock processing. Although there have been several prior studies to mitigate the problem, they address only a subset of symptoms, require the modification of guest OSes, or change the processor architecture. This paper proposes a novel way to comprehensively reduce the inefficiency of guest OS execution in consolidated systems. It migrates the short-lived critical OS tasks to dedicated micro-sliced cores, minimizing the delays caused by time sharing. The hypervisor identifies the critical OS tasks without any OS intervention, and schedules the critical code sections onto the dynamically partitioned cores at runtime. The dedicated micro-sliced cores employ a short sub-millisecond quantum to minimize the response latencies for consolidated virtual machines. By readily servicing the critical tasks, the proposed scheme can minimize the adverse artifact of virtual machine consolidation without any modification of guest OSes.

This article presents an overview of computer operating systems (OS) and emerging trends. OS is simply defined as an interface between computer hardware and the user. The objective of the study is to investigate the emerging trends of OS and to find out the direction of OS for modern computing systems. To achieve this goal the paper looks at the concepts of OS, its underlying architectures and evolution. The papers also outline the components of the OS provide knowledge on security issues with OS architectures and provide best practices to secure OS. The paper found out that the current trends in OS include IoT OS, Cloud OS, AI-powered OS, Blockchain OS, Hybrid OS and Container OS. The paper compares the strengths and weaknesses of the major OS. The Paper proposes a double-layer security approach where the OS is hardened with security policies and embedding security protocols in the Hardware architecture of the OS. It was discovered that every technology comes with its unique architecture and OS. This makes it worrisome for engineers to crack their brains to develop such a system. The paper further proposes the development of a universal OS for all architectures leaving room for further expansion while mitigating power consumption issues by incorporating green computing technology in the design architecture.

This chapter introduces the enormous impact of mobile computing on both companies and individuals. Companies face many issues related to mobile computing. For example: which devices will be supported by the organization? which devices will fulfill the business objectives? which form factor will win? which features and networks will future devices offer? which operating systems will they run? what will all this cost? what are the security issues involved? what are the business drivers? This chapter will discuss the major business drivers in the mobile computing field, and provide an analysis of the top two operating systems that are currently running the majority of mobile devices. These platforms are the 1) Palm operating system (OS), and 2) Microsoft Windows CE operating system. The chapter will analyze the strengths and weaknesses of each operating system and discuss market share and future growth.

We address, in a unified manner, different mechanisms of controlling the application's environment. Today's applications can run over distributed, heterogeneous environments: different operating systems and networks. The resources used by the applications are also distributed across the environment. Therefore, the application developer needs to implement different mechanisms in order to the resources. Resource reservation, resource allocation, and resource are such mechanisms; these are not platform independent but instead, are operating system- and network-dependent. These mechanisms can be presented as universal service objects to the application developer and can support functionality to resources with respect to priorities of users and tasks. We present an implementation of control for a video conference application. We apply the units as mechanisms to enable quality of service parameters.

Computer operating system (OS) is one of the most important course of computer science. According to the difficult situation in the teaching of computer operating system course, the article analyses the reasons of main problems in the teaching process, and puts forward a new related teaching method based on association. Firstly, the method regards the computer system as a complete social system, and strengthen the crossing and connection with other courses. Secondly, strengthen the specific association and correspondence between experiments and theoretic. Finally, specific examples are given and explained.

Workover operations, with complex equipment, heavy work and numerous personnel, are the core of maintaining daily operation, stabilizing and enhancing production of oil and gas fields. Risk monitoring is particularly important for ensuring operation quality, efficiency, safety and economic benefits. At present, the risks of workover operations are mainly judged by on-site workers, and automatic acquisition of operation data, intelligent risk identification and warning have not been realized. In order to improve the quality, efficiency, safety and economic benefits of workover operations, it is necessary to conduct research on the intelligent risk monitoring technology. With the aim of intelligent risk monitoring of workover operations, the study on key technologies for intelligent risk monitoring is carried out from three aspects of hardware, algorithm and software. First, the data acquisition device of workover operations is designed and developed, the data processing method is proposed, and the database of workover operations is established. Second, the intelligent risk monitoring and warning models of workover operations based on big data analysis are established. Third, the digital supervision system of workover operations is designed and developed, and the on-site testing and evaluation of the system are conducted. The data acquisition device of workover operations includes basic sensors, central control unit, management terminal, etc. The digital supervision system of workover operations includes functional modules of operation monitoring, risk warning, historical data, operation management, comprehensive analysis, and system management. With the use of data acquisition device and digital supervision system of workover operations, the key operation parameters, such as hook height, string depth, string weight, string speed, wellhead pressure, and hazardous gas, can be acquired and analyzed automatically. Meanwhile, the risks of abnormal operation parameters can be identified in time. The operation risks can be reduced, and the quality, efficiency, safety and economic benefits of workover operations can be ensured. The data acquisition device and digital supervision system of workover operations have been tested in 38 wells. Typical risks of abnormal operation parameters occurred 455 times, and the risks were identified 433 times by using this system, with a risk identification accuracy rate of 95.16%. With the use of intelligent risk monitoring technology for workover operations, the closed-loop management of data acquisition, risk monitoring, risk identification, risk warning, risk disposal and result feedback is realized, and the operation quality, efficiency, safety and economic benefits are improved effectively.

A process control system designer is required to have an understanding of multiple disciplines in order to develop an appropriate design. This inc ludes knowledge of the process to be controlled, the habits of the user, and the architecture of the available control instrumentation. When a pro grammable digital computer is used, it is necessary to define models to help evaluate design tradeoffs. One such model defines a three axis matrix relating to the processes, the computer operating systems and the computer languages. A concept of levels of language matched with the operating system and the processes can then be developed. An evaluation with this model indicates that the major inadequacies of process control languages result when the system designer must default to an incorrect language to resolve interactions. The requirements of the process control clearly indicate that the computer should be adapted to the user. Most formal languages compromise these requirements significantly by forcing the user, the process and the operating system to conform to the language.

A completely open source based computing system for computer generation of Fourier holograms

OSCON (Operating System CONsultant) is an operating system consultant which gives English answers to English queries about computer operating systems. The program currently answers queries for over 40 commands from the UNIX and MS-DOS operating systems. OSCON answers a wide variation of queries that users may wish to ask. OSCON is intended to be a consultant for various types of users who may ask vague and detailed queries. OSCON is programmed in Quintus Prolog and answers queries in less than 2.5 seconds. An empirical study with the Wizard-of-Oz technique provides important data for the further development of OSCON.

While the digital twins paradigm has attracted the interest of several research communities over the past twenty years, it has also gained ground recently in industrial environments, where mature technologies such as cloud, edge and IoT promise to enable the cost-effective implementation of digital twins. In the industrial manufacturing field, a digital model refers to a virtual representation of a physical product or process that integrates data taken from various sources, such as application program interface (API) data, historical data, embedded sensor data and open data, and that is capable of providing manufacturers with unprecedented insights into the product’s expected performance or the defects that may cause malfunctions. The EU-funded IoTwins project aims to build a solid platform that manufacturers can access to develop hybrid digital twins (DTs) of their assets, deploy them as close to the data origin as possible (on IoT gateway or on edge nodes) and take advantage of cloud-based resources to off-load intensive computational tasks such as, e.g., big data analytics and machine learning (ML) model training. In this paper, we present the main research goals of the IoTwins project and discuss its reference architecture, platform functionalities and building components. Finally, we discuss an industry-related use case that showcases how manufacturers can leverage the potential of the IoTwins platform to develop and execute distributed DTs for the the predictive-maintenance purpose.

Machine learning has enabled many interesting applications and is extensively being used in big data systems. The popular approach - training machine learning models in frameworks like Tensorflow, Pytorch and Keras - requires movement of data from database engines to analytical engines, which adds an excessive overhead on data scientists and becomes a performance bottleneck for model training. In this demonstration, we give a practical exhibition of a solution for the enablement of distributed machine learning natively inside database engines. During the demo, the audience will interactively use Python APIs in Jupyter Notebooks to train multiple linear regression models on synthetic regression datasets and neural network models on vision and sensory datasets directly inside Teradata SQL Engine.

The rapid development of big data makes our life age transition from the “information age” to the “data age”. All walks of life use big data to change production and life. This change will inevitably affect the field of news dissemination. Combining the application of big data in all social classes, we first discuss the concepts of online media and big data, starting with the relationship between online media and big data, explaining the factors of online media using big data and applying big data to online media Variety. Among them, the factors of big data multimedia Web applications include: visual content presentation, mobile communication channels, personalized information acceptance and social information interaction. Although people have done a lot of research on new ways of news dissemination, there are still many shortcomings. This paper studies the extremely fragile topology of wireless sensorless networks facing selective attacks, and uses the principle of local optimization to propose a secure routing algorithm for multi-feature decision-making in wireless sensor networks based on topology. The model defines parameters such as node power transmission efficiency, load and packet loss rate as features, and uses relative entropy to calculate the weight of each feature to create a multi-feature decision model. The node selects the best path according to the decision model. This model takes into account the dynamic characteristics of the network and effectively compensates for the deficiencies of the existing algorithms. These algorithms only solve the problem of scale-free topology attacks from the network structure. Using the method in this article, the spread of big data news has increased by 18%.