Monitoring SLAC High Performance UNIX Computing Systems

Most existing monitoring systems do not provide semantic information for their monitoring data. Thus, monitoring data in the repository can only be understood by the system that has generated the data. Round Robin Database (RRD) is an important effort toward building monitoring data semantics [1]. However, the RRD model has missing semantic information, such as what QoS metric or which system entity a monitoring data stream is for. Our goal is to enhance the semantics in RRD to facilitate semantic based monitoring data retrieval and to improve the interoperability of monitoring systems and analysis software. We leverage the RRD data semantics and develop the new SE-RRD (Semantic Enhanced RRD) model. We also develop the SE-RRDtool to provide semantic-based accesses to monitoring data. With SE-RRDtool, it is also possible to reason and automatically perform data alignment between the data requests and the monitoring data streams available in the repository. SE-RRDtool greatly enhances the capability of RRDtool and cloud monitoring systems can use it for semantic-rich monitoring data repository.

Simulation software in MANET research is vital. Such a tool provides a versatile mechanism to understand all the involved aspects of these particular systems, from the radio interface to the last communication layer. In this chapter, the authors present the SimuMANET project, a tool for both simulation and field tests purpose. It allows the deployment of wireless reconfigurable ad-hoc networks and MANETs, assisted by a real-time graphical user interface (GUI) for network traffic monitoring and management of radio electric features of the links established between the active network nodes. Due to a set of functionalities, such as GUI, network topology visualization, traffic and motion pattern configuration, and real-time network status analysis, the simulator introduced here becomes a valid tool for both research and education targets. Two scenarios with different types of motion and traffic are simulated using the SimuMANET tool, and the results are shown and commented to illustrate some capabilities of this software.

A Signal of Interest (SOI) is a signal that has been recorded for further analysis. This is driven by mission requirements for both known and anomaly signals. Identifying anomalies/SOIs is reliant on the system operator’s knowledge which can be prone to human error. The objective of this project is to improve situational awareness by automating the identification of SOIs with Machine Learning/Artificial Intelligence techniques. In year one, data science techniques were applied and proved the daily emitter and frequency distributions in the Hampton Roads area have a consistent subset of emitter traffic. Anomalies were identified based on this fingerprint, and an ANN algorithm was implemented in an application which provides a graphic highlighting anomalies/SOIs. During year two, the algorithm was validated, the data collection method was improved, and prototype functionality was successfully integrated into the tactical system that streams live Radio Frequency into a real-time Graphical User Interface and predicts potential anomalies/SOIs in real-time.

The arrival of Artificial Intelligence (AI) and the Internet of Things (IoT) has led to the development of programming-based environments for domestic and commercial applications of automation. This work is distributed into three sections: virtual assistant (VA), domestic robotics, and advanced user interface demonstration. A dissimilar public library is utilised for a virtual assistant and information to identify them, concluding the communication recognition procedure and then replying by viewing all the effects on the display screen. Domestic automation includes monitoring and changing applications. IoT-based methods were used to control the brightness of the bulb, and an IoT-based technique was used to control the speed of the DC fan. Finally, the output was displayed using a Graphical User Interface (GUI) constructed using the QT-designer tool and PyQT library. The primary value of this work is the ability to combine an AI-based virtual assistant, home automation, and a real-time graphical user interface into easy use system. Operator instructions are examined to deliver optimum solutions. The proposed scheme has better functional coverage, real-time visualisation, and better user interaction in comparison with the existing benchmark systems, which mostly concentrate on voice-based helpers and distributed IoT control systems. This scheme is recommended for use as an intellectual virtual assistant, which understands human speech and answers through created voices. This work is new because it is the first work where an AI virtual assistant, IoT based automatization, and real-time smart display are combined to make the interaction and constant situational awareness happen within one platform.

Purpose — The purpose of this paper is to document in part the study that was conducted for a research project ASHRAE RP-1650, which investigates the training requirements for sustainable high-performance building (HPB) operations and maintenance (O&M). This paper documents the intent, procedures and findings of a portion of the research project, which includes a survey of O&M practices for high performance buildings in the industry. The purpose of the survey as reported in this paper was to confirm and refine the level of understanding of KSCs for operating HPBs as perceived by O&M personnel in the industry. The details of the survey and the results are presented in this paper. The survey was sent out to facility managers in the industry who are involved with the O&M of HPB systems and equipment. 221 facility managers were surveyed in this study. Design/methodology/approach — This study utilizes a concurrent mixed-method research, which considers both qualitative and quantitative responses. Findings — The study found that while most technicians were ‘moderately well' to ‘very well' trained for the configuration and components as well as the installation, repair and replacement of different high performance equipment and systems, they lacked essential knowledge to address issues such as: controls; tracking of variables; programming and operation of modulation devices implemented in high-performance systems and equipment. This finding implied a lack of ‘Systems Thinking' approach, which the study found to be pertinent and essential for the O&M of HPB systems and equipment. Originality/value — The study is utilized to identify gaps between the training requirements for O&M of HPBs identified from the literature review of the research project and the current industry practices. The identified gaps are valuable to develop learning objectives for training programs that are aimed to train personnel responsible for the O&M of HPBs.

This paper presents a novel method and system for flexible data storage management that incorporates dynamic encryption key techniques to ensure data security.The proposed approach determines storage operations and selects optimal storage resources based on predefined criteria such as performance, cost, and data access patterns.The integration of cryptographic keys ensures the confidentiality and integrity of data, addressing security concerns in diverse and large-scale data storage environments.By leveraging adaptive key management, the system can respond to changing security threats and operational requirements in real-time.This adaptability enhances the resilience of data storage solutions against emerging cyber threats and operational disruptions.The system leverages advanced algorithms to study data access patterns, optimizing storage operations for both rapid performance and efficiency.Additionally, the proposed method features a robust mechanism for key rotation and revocation, which reduces the risk of key compromise and bolsters overall security.The dynamic selection of storage resources considers multiple factors, ensuring a balanced approach that meets both performance and cost-efficiency criteria.This comprehensive strategy not only enhances security but also boosts the overall performance of data storage systems.Extensive testing in diverse environments has shown significant improvements in data retrieval times, reduced storage costs, and faster encryption and decryption processes.The results demonstrate the practical applicability of the proposed system in real-world scenarios, showcasing its potential to revolutionize data storage management.The proposed system also integrates seamlessly with existing infrastructure, making it a versatile solution for a wide range of applications.By providing a detailed analysis of key performance indicators, this paper offers valuable insights into the benefits of integrating adaptive cryptographic mechanisms in data storage management.The innovative combination of dynamic operations and adaptive security measures positions this system as a leading-edge solution in the field.Future research will focus on further enhancing these adaptive mechanisms and exploring their application in more diverse storage environments.Additionally, the integration of machine learning algorithms to predict and adapt to data access patterns will be investigated to further optimize system performance.The findings of this paper contribute to the ongoing discourse on secure and efficient data storage, offering a novel perspective that bridges the gap between performance optimization and robust security measures.

Connection of a local area network to the Internet brings with it a certain amount of risk. Once connected, local computer systems are subject to attacks by other users on the Internet. To maintain system integrity, system administrators need tools to accurately display the status of communication between local hosts and remote hosts. The paper describes an ongoing research and development project for Surveying Network Information Flow (SNIF). The SNIF tool provides system administrators and security officers an encapsulated view of network communication and information flow for a set of monitored subnetworks. The tool provides a real-time graphical interface, configurable monitoring, alarms and signals, connection statistics, and an extensible interface to permit links to external systems (LES). This tool is one of many in a suite of security tools being developed at the University of Idaho. >

Conceptual modeling is drawing more attention among researchers from many organizations and different disciplines including high-performance low-power multicore systems. As the popularity and demand grow, more processing power is required by the mission critical systems. Several chip vendors have developed teraflop computing systems (like Intel 80-Core Research Chip). Even though IBM offers the Roadrunner supercomputer, NSF and other research organization are looking for more powerful petascale computer system. In a petascale computer system, thousands to millions of processing cores are expected to work together using petabytes of memory to produce the expected performance. A high performance system like Roadrunner requires a very sophisticated power management system. No wonder, such a gigantic system costs millions of dollars. Conceptual modeling offers the opportunity to conduct research on complex and expensive systems in an inexpensive way. In this work, we address some key conceptual modeling issues and discuss a methodology to develop conceptual models of petascale computer systems. Important features of this methodology include data acquisition to improve accuracy by capturing the important details about the system under consideration and (system-to-) model abstraction to make the modeling manageable without eliminating any significant characteristics of the target system. We believe our findings help develop sound conceptual models of complex high performance petascale computer systems.

Vibration in electrical and mechanical systems is an important parameter which is related to the systems health, stability and efficiency. In practical condition, the system is distributed and most of the time unreachable during their operating time. In this paper, triple axis wireless vibration monitoring system for distributed system is described. The system also includes development of real time graphical user interface with MATLAB. Here triple axis accelerometer ADXL335 is used for acceleration measurement , cost effective ARM M4 processor TM4C123GXL is used as processing unit and low power Zigbee transceiver is used to transfer the vibration data. To prevent data tapping, the data is encrypted by Advanced Encryption Standard (AES) algorithm in sending side and then decrypted in receiver end. The overall latency of the system is also measured. The received signal strength based performance analysis is also done where the RSSI value is approximately -43dBm at 587.94m distance. The system is tested for vibration measurement of a single phase induction motor.

Silicon photonics offers great opportunities to replace the bulky free-space systems with compact, highly integrated ones. However, dedicated optimization work is required to realize high performance systems for practical applications. This talk presents a library of optimized photonic components and a high-performance system as example.

BackgroundResearchers have increasingly adopted AI and next-generation sequencing (NGS), revolutionizing genomics and high-throughput screening (HTS), and transforming our understanding of cellular processes and disease mechanisms. However, these advancements generate vast datasets requiring effective data stewardship and curation practices to maintain data integrity, privacy, and accessibility. This review consolidates existing knowledge on key aspects, including data governance, quality management, privacy measures, ownership, access control, accountability, traceability, curation frameworks, and storage systems.MethodsWe conducted a systematic literature search up to January 10, 2024, across PubMed, MEDLINE, EMBASE, Scopus, and additional scholarly platforms to examine recent advances and challenges in managing the vast and complex datasets generated by these technologies. Our search strategy employed structured keyword queries focused on four key thematic areas: data governance and management, curation frameworks, algorithmic bias and fairness, and data storage, all within the context of AI applications in genomics and microscopy. Using a realist synthesis methodology, we integrated insights from diverse frameworks to explore the multifaceted challenges associated with data stewardship in these domains. Three independent reviewers, who systematically categorized the information across critical themes, including data governance, quality management, security, privacy, ownership, and access control conducted data extraction and analysis. The study also examined specific AI considerations, such as algorithmic bias, model explainability, and the application of advanced cryptographic techniques. The review process included six stages, starting with an extensive search across multiple research databases, resulting in 273 documents. Screening based on broad criteria, titles, abstracts, and full texts followed this, narrowing the pool to 38 highly relevant citations.ResultsOur findings indicated that significant research was conducted in 2023 by highlighting the increasing recognition of robust data governance frameworks in AI-driven genomics and microscopy. While 36 articles extensively discussed data interoperability and sharing, AI-model explain ability and data augmentation remained underexplored, indicating significant gaps. The integration of diverse data types—ranging from sequencing and clinical data to proteomic and imaging data—highlighted the complexity and expansive scope of AI applications in these fields. The current challenges identified in AI-based data stewardship and curation practices are lack of infrastructure and cost optimization, ethical and privacy considerations, access control and sharing mechanisms, large scale data handling and analysis and transparent data-sharing policies and practice. Proposed solutions to address issues related to data quality, privacy, and bias management include advanced cryptographic techniques, federated learning, and blockchain technology. Robust data governance measures, such as GA4GH standards, DUO versioning, and attribute-based access control, are essential for ensuring data integrity, security, and ethical use. The study also emphasized the critical role of Data Management Plans (DMPs), meticulous metadata curation, and advanced cryptographic techniques in mitigating risks related to data security and identifiability. Despite advancements, significant challenges persisted in balancing data ownership with research accessibility, integrating heterogeneous data sources, ensuring platform interoperability, and maintaining data quality. Ongoing risks of unauthorized access and data breaches underscored the need for continuous innovation in data management practices and stricter adherence to legal and ethical standards.ConclusionsThese findings explored the current practices and challenges in data stewardship, offering a roadmap for strengthening the governance, security, and ethical use of AI in genomics and microscopy. While robust governance frameworks and ethical practices have established a foundation for data integrity and transparency, there remains an urgent need for collaborative efforts to develop interoperable platforms and transparent data-sharing policies. Additionally, evolving legal and ethical frameworks will be crucial to addressing emerging challenges posed by AI technologies. Fostering transparency, accountability, and ethical responsibility within the research community will be key to ensuring trust and driving ethically sound scientific advancements.

Baby weight and length measurement system has been designed based on ultrasonic and load cell sensors with data storage using MySQL database and website interface. This research aims to produce a baby weight and length measurement system with microcontroller NodeMCU ESP32. The system is designed with an ultrasonic sensor HC-SR04 and four half-bridge load cells of 50 kg. The ultrasonic sensor works on the piezoelectric and inverse piezoelectric principles, while load cell sensors work on strain gauge principles. An ultrasonic sensor measures body length, while a load cell sensor measures body weight. According to the Ministry of Health of the Republic of Indonesia’s regulations, this system also has an LCD 20×4 to display measurement and nutritional status assessment results. The experiment used four doll objects with different lengths and weights. This research shows that the ultrasonic sensor HC-SR04 used in this research has an average error of 0.494% in length measurement. The load cell sensor and HX711 ADC used in this research has an average error of 0.949% in weight measurement. The designed system automatically assesses the nutritional status and stores the measurement result and nutritional status in the database to be displayed on the website.

Development of a distributed high-speed data acquisition and monitoring system based on a special data packet format for HUST RF negative ion source

The number of Earth observation satellites from China increases dramatically recently and those satellites are acquiring a large volume of imagery daily. As the main portal of image processing and distribution from those Chinese satellites, the China Centre for Resources Satellite Data and Application (CRESDA) has been working with PCI Geomatics during the last three years to solve two issues in this regard: processing the large volume of data (about 1,500 scenes or 1 TB per day) in a timely manner and generating geometrically accurate orthorectified products. After three-year research and development, a high performance system has been built and successfully delivered. The high performance system has a service oriented architecture and can be deployed to a cluster of computers that may be configured with high end computing power. The high performance is gained through, first, making image processing algorithms into parallel computing by using high performance graphic processing unit (GPU) cards and multiple cores from multiple CPUs, and, second, distributing processing tasks to a cluster of computing nodes. While achieving up to thirty (and even more) times faster in performance compared with the traditional practice, a particular methodology was developed to improve the geometric accuracy of images acquired from Chinese satellites (including HJ-1 A/B, ZY-1-02C, ZY-3, GF-1, etc.). The methodology consists of fully automatic collection of dense ground control points (GCP) from various resources and then application of those points to improve the photogrammetric model of the images. The delivered system is up running at CRESDA for pre-operational production and has been and is generating good return on investment by eliminating a great amount of manual labor and increasing more than ten times of data throughput daily with fewer operators. Future work, such as development of more performance-optimized algorithms, robust image matching methods and application workflows, is identified to improve the system in the coming years.

Data storage is an important application of cloud computing, where clients can remotely store their data into the cloud. By uploading their data into the cloud, clients can be relieved from the burden of local data storage and maintenance. This new paradigm of data storage service also introduces new security challenges. One of these risks that can attack the cloud computing is the integrity of the data stored in the cloud. In order to overcome the threat of integrity of data, the client must be able to use the assistance of a Third Party Auditor (TPA), in such a way that the TPA verifies the integrity of data stored in cloud with the client's public key on the behalf of the client. The existing schemes with single verifier (TPA) may not scale well for this purpose. In this paper, we propose An Efficient Distributed Verification Protocol (EDVP) to verify the integrity of data in a distributed manner with support of multiple verifiers (Multiple TPAs) instead of single Verifier (TPA). Through the extensive security, performance and experimental results, we show that our scheme is more efficient than single verifier based scheme.