How do I know if my Windows Server is syncing with NTP?
Answers from top 7 papers
More filters
| Papers (7) | Insight |
|---|---|
24 Nov 2009 5 Citations | By setting multiple NTP servers in each network domain and utilizing NTP Pool Project guidelines, it will help to evenly distribute the throughput of each server and enhance the stability of the system. |
21 Jul 2013 1 Citations | We propose a round-trip hop-counts-capable NTP server response for the accurate selection of the nearest server. |
| The results indicate that if software is properly controlled, submillisecond timing resolution is achievable under Windows with both old and new computers alike. | |
01 Oct 2014 2 Citations | We observe that the NTP offset can accurately indicate the network congestion, measured from the monitoring station to the NTP server. |
17 Citations | Maintaining or increasing NTP is less predictable. |
20 Sep 2010 1 Citations | In order to reduce access frequency to the NTP time server, and efficiently decreases over-loaded phenomena of server. |
08 Oct 2007 5 Citations | Under the same accuracy requirement, the algorithm has much less time requests to the NTP server and higher stability in performance, which decreases a lot on the load of server. |
My columns
Related Questions
Can Palo Alto firewall act as NTP server?10 answers
How can I tell if IIS is running on Windows Server 2019?5 answers
How can I tell if my Windows server is patched?8 answers
How do I check my ESXI NTP server?3 answers
How to check Netbios name in Windows Server 2016?4 answers
What port do NTP servers use?8 answers
See what other people are reading
What is TCP and UDP?
4 answers
TCP (Transmission Control Protocol) and UDP (User Datagram Protocol) are two fundamental transport layer protocols used for communication. TCP is connection-oriented, ensuring reliable end-to-end connections with mechanisms like congestion control and packet reordering. On the other hand, UDP is connectionless, employing a send-and-forget mechanism without the overhead of connection setup or reliability features. While TCP accounts for a significant portion of network traffic, UDP's share is increasing due to real-time and multimedia applications. Studies have shown that UDP flows can impact TCP performance by capturing network resources, leading to a decrease in TCP's efficiency. Understanding the characteristics and differences between TCP and UDP is crucial for optimizing network performance and resource utilization in various communication scenarios.Can the implementation of a ticketing system improve the accuracy and timeliness of disaster reporting?5 answers
The implementation of a ticketing system can indeed enhance the accuracy and timeliness of disaster reporting. By integrating machine learning with traditional ticketing systems, businesses can optimize operational efficiency in customer service departments, leading to improved response times and productivity. Additionally, geographically distributed ticket servers can prioritize network flows related to disaster response, military operations, or emergencies, ensuring timely and accurate handling of critical information. Moreover, the use of electronic ticketing systems can streamline ticket reporting processes, resulting in more effective recording systems and increased ticket numbers, as seen in the case of PT. Astra Otoparts Tbk.. These findings collectively suggest that the adoption of ticketing systems can significantly enhance the accuracy and timeliness of disaster reporting processes.How does offsite construction contribute to faster recovery times in disaster affected areas?5 answers
Offsite construction methods, such as modular construction, play a crucial role in expediting recovery times in disaster-affected areas. These methods offer time-efficiency efficiency advantages by reducing the need for on-site labor, overcoming local labor constraints, and enhancing productivity in a controlled environment^[Context_4. Additionally, offsite construction allows for concurrent and non-seasonal work, leading to shorter schedules, reduced site congestion, and improved labor productivity, akin to an assembly line process. By leveraging offsite construction technologies, post-disaster housing reconstruction can be accelerated, providing quick and efficient solutions for temporary and permanent housing needs in the aftermath of natural disasters.What is a freeway?5 answers
A freeway is a type of high-speed road designed for uninterrupted traffic flow, typically connecting major urban areas and facilitating long-distance travel. Freeways are part of a larger highway system, such as the Interstate Highway System in the United States, which was meticulously planned to enhance transportation efficiency and reduce travel times and costs. Freeways often incorporate advanced features like heating/cooling fluid systems for road surfaces to improve safety and performance. Additionally, innovative models like Freeway have been developed to optimize network capacity and data transfer speeds over high-bandwidth inter-DC WANs, addressing issues like packet loss and flow control bottlenecks. Overall, freeways play a crucial role in modern transportation networks, shaping the spatial organization of regions and influencing travel patterns globally.What is the state of the art in cybersecurity monitoring?10 answers
The state of the art in cybersecurity monitoring encompasses a broad spectrum of technologies and methodologies designed to protect digital infrastructures and ensure the secure operation of systems. At the forefront, cybersecurity monitoring devices have evolved to detect electromagnetic (EM) fields emanating from electrical cables, enabling the monitoring of data transmission without direct electrical contact. This advancement allows for the decoding of signals across different communication protocols, enhancing the ability to detect and report potential security breaches remotely.
In the realm of critical infrastructures, the integration of cyber components necessitates a resilient cybersecurity framework that assesses the potential impacts of cyberattacks on both cyber and physical systems. This involves aggregating diverse modeling and simulation-based approaches to characterize the impact of attacks, guiding future impact analysis studies. Network Security Monitoring (NSM) systems play a pivotal role in detecting security incidents by monitoring network events, with a taxonomy developed to assess current NSM tools and identify challenges in modern network deployments, such as Software Defined Networks (SDN) and the Internet of Things (IoT).
Cybersecurity monitoring extends to the electrical power grid, where monitoring nodes generate time-series data to assess the operation of grid components. This data is used to rank components based on risk, enabling focused monitoring of critical components to detect cyber-attacks or faults. Trap-based monitoring systems have also been identified as an efficient method for inferring Internet threat activities, utilizing sensors to detect and analyze cyberattacks.
Innovative approaches include the use of AI techniques in avionics and critical control systems for cybersecurity monitoring, learning to predict normal behaviors and outputs to detect deviations indicative of cyber intrusions. The flexibility of Network Security Monitors (NSMs) has been enhanced to accommodate the diversity of packet-oriented layers below TCP/IP, improving the performance and integration of lower-layer protocol dissectors.
The economic aspect of cybersecurity monitoring is highlighted by the development of dynamic IoT architectures for industrial NSM, offering significant savings in installation and operation costs while promoting environmentally friendly operations. The commercial sector's contribution to cybersecurity monitoring is underscored by the identification of new products and trends, facilitating the relationship between commercial developments and military applications. Collectively, these advancements represent the cutting edge in cybersecurity monitoring, addressing both technical and economic challenges to protect against the ever-evolving landscape of cyber threats.Can transfer learning be used to improve the efficiency and effectiveness of edge computing for face recognition?9 answers
Transfer learning has emerged as a pivotal technique in enhancing the efficiency and effectiveness of edge computing for face recognition tasks. By leveraging pre-trained models and adapting them to new, but related tasks, transfer learning can significantly reduce the computational resources required, which is crucial for edge devices with limited processing capabilities. The EdgeFace network, inspired by the hybrid architecture of EdgeNeXt, demonstrates how combining CNN and Transformer models with a low-rank linear layer can achieve high face recognition accuracy optimized for edge devices, showcasing the potential of sophisticated model architectures in edge computing scenarios.
Moreover, the practice of face recognition, which is widely applied in various sectors, benefits from deep learning advancements, particularly CNNs. The use of pre-trained CNN models like VGG16, ResNet-50, and MobileNet through transfer learning has shown promising results in face recognition, indicating the effectiveness of leveraging deep learning in edge computing environments. Additionally, the proposal of a face recognition-based video retrieval system for edge computing environments further underscores the practical applications of these technologies in real-world scenarios.
The efficiency of transfer learning is highlighted by the ETL technique, which retains only cross-task aware filters from a pre-trained model, resulting in a sparse transferred model. This approach not only reduces the size and inference time of the model but also retains high accuracy, demonstrating the potential for lightweight yet effective face recognition models on edge devices. Similarly, incorporating transfer learning into vehicular edge networks has shown to improve the agility of environment construction for computation-intensive tasks, further validating the approach's utility in edge computing.
Facial Expression Recognition (FER) systems also benefit from transfer learning, with the EfficientNet architecture achieving high accuracy on small datasets, showcasing the technique's power in enhancing model performance with limited data. Lastly, the application of transfer learning in a Siamese network for face recognition further illustrates its versatility and effectiveness in improving recognition rates, even in challenging conditions.
In conclusion, transfer learning significantly enhances the efficiency and effectiveness of edge computing for face recognition by enabling the use of advanced deep learning models on devices with limited computational resources, thereby facilitating real-time, accurate, and efficient face recognition applications.What is the importance of management support in IT integration post M?4 answers
Management support plays a crucial role in IT integration post M&A activities. The success of post-merger information systems integration heavily relies on effective decision-making processes and management backing. Additionally, the provision of appropriate information systems and technologies is vital for the effectiveness and sustainability of standardized corporate management systems. Furthermore, monitoring and management support are essential for maintaining enterprise integration, especially in scenarios where different applications and services are involved, requiring comprehensive monitoring mechanisms. Moreover, a management support method involving the operation of reference servers and applications is highlighted as a key element in ensuring continuous operation and decision-making in IT management. Overall, top management team support is identified as a critical factor for the success of management control systems innovations, emphasizing the importance of TMT belief and participation in MCS innovations.Security measures on mobile health application that will aggregate user data to provide fitness insights?9 answers
To ensure the security of mobile health applications that aggregate user data to provide fitness insights, a multifaceted approach is necessary. Firstly, employing robust encryption algorithms like the Advanced Encryption Standard (AES) is crucial for protecting user data from unauthorized access and manipulation, as highlighted in the context of fitness mobile applications. Additionally, implementing comprehensive security and privacy criteria, including authentication, authorization, access management, data storage, integrity, encryption, and privacy policies, can guide developers in creating secure applications.
The use of mobile security frameworks such as MobSF and MARA, which assess security levels based on established classifications and provide safety metrics, is essential for identifying vulnerabilities and enhancing the security posture of mobile health applications. Moreover, considering user-centric design principles can significantly improve the privacy and security aspects of these applications by addressing specific vulnerability issues and incorporating user feedback into the development process.
Blockchain technology offers a promising solution for ensuring data confidentiality and secure access to electronic health records (EHRs) by leveraging distributed ledger technology for secure and authorized data sharing among stakeholders. Furthermore, a lightweight, sharable, and traceable secure mobile health system can facilitate efficient keyword search and fine-grained access control of encrypted data, supporting secure data sharing and user revocation.
Adopting privacy models such as the Privacy Policy Model (PPM) can address inherent threats to user privacy by providing a fine-grained and expandable permission model, ensuring the responsible use of collected data. Lastly, empirical investigations into end-user security awareness can inform the development of guidelines for creating secure and usable mobile health applications, emphasizing the importance of balancing security features with usability.
In conclusion, securing mobile health applications that aggregate user data for fitness insights requires a comprehensive strategy that includes robust encryption, adherence to comprehensive security criteria, user-centric design, blockchain for data confidentiality, lightweight security systems for data sharing, privacy models to protect user data, and an understanding of end-user security awareness.What are some efficient computational methods that can be used to address scalability challenges in AI algorithms?5 answers
Efficient computational methods to address scalability challenges in AI algorithms include secure multiparty computation (SMC), quasi-homomorphic encryption, federated learning (FL), and Graph Equilibrium Models (GEQs). While SMC and FL focus on secure distributed processing and data privacy, GEQs tackle long-range dependency issues in graph neural networks. To enhance scalability, a method called VEQ has been proposed, which optimizes the equilibrium finding process by utilizing virtual equilibrium and mini-batch training. These methods aim to balance computational complexity, confidentiality, and performance in large-scale AI applications, offering solutions to the challenges posed by the increasing size and complexity of modern AI algorithms.How does a proactive and data-driven approach to quality control impact the overall efficiency of industrial systems?5 answers
A proactive and data-driven approach to quality control significantly enhances the efficiency of industrial systems by leveraging real-time data, machine learning techniques, and predictive analytics. By utilizing Industry 4.0 technologies, such as edge computing and wireless networks, industrial control systems can optimize control performance through dynamic edge offloading, ensuring stability under fluctuating cyber-physical conditions. Additionally, the integration of quality management key performance indicator visualization systems enables the quantification of relationships between IoT data and product quality, leading to integrated quality control and improved product quality. These approaches not only enhance process reliability, reduce downtime, and increase performance but also provide a user-friendly and intuitive method for quality management in manufacturing industries.Can bus privacy?4 answers
Bus privacy is a critical concern in various contexts such as in-vehicle networks, public WiFi systems, and customized-bus sharing services. In the realm of in-vehicle networks, the lack of authentication and encryption in Controller Area Network (CAN) buses poses a risk of compromise by untrusted agents. Similarly, public WiFi spots on buses raise privacy concerns due to open access, with studies showing high probabilities of users being uniquely re-identified from leaked information. Moreover, in customized-bus sharing services, ride clustering for optimal bus routes can inadvertently expose users' locations and travel patterns, necessitating privacy-preserving schemes like fog computing and cryptographic techniques to safeguard user data without compromising clustering quality. These diverse scenarios highlight the importance of addressing bus privacy to ensure data security and user confidentiality in various transportation settings.