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How much RAM do I need for a Fivem server? 

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The current results show that the server energy efficiency increases with increasing CPU utilization and is higher for a multi-processor server than for a single-processor server.
Through a series of experiments, it is verified that, with appropriate settings of cache sizes, the proposed management scheme provides comparable performance results to prior arts with much lower requirements on the on-device RAM.
DNA RAM can eliminate the computational overhead of sequence design because the same RAM can be used for various computations once it is made.
These combined advantages of RAM and flash memory make it a potential choice for SOC.
Proceedings ArticleDOI
Liviu Iftode, Kai Li, Karin Petersen 
01 Jan 1993
65 Citations
The memory server model extends the memory hierarchy of multicomputers by introducing a remote memory server layer.
Performance measurements from our Route Server implementation reveal that the storage requirements of Route Servers can be much larger than that of a typical border router.
Optical RAM has emerged as a promising solution for overcoming the “Memory Wall” of electronics, indicating the use of light in RAM architectures as the approach towards enabling ps-regime memory access times.
Book ChapterDOI
Christoph Bernhardt, Ernst W. Biersack 
19 Jun 1995
49 Citations
The server array is a novel video server architecture based on partitioning each video over multiple server nodes, thereby achieving perfect load balancing for any demand distribution.
These results are promising for developing a 1-Mbit/cm/sup 2/ density Josephson RAM.
Different from the past work, this paper proposes to consider RAM as a storage medium together with flash memory to take advantage of the characteristics of both RAM and flash memory.
This paper also demonstrates how the efficiency of servers can be optimized for specific workloads as well as tailored to server configuration for optimum performance and efficiency.
By providing a better understanding of the limits of current RAM designs, this report supports the decision for a particular RAM in an individual application.
This is then utilized for a one-by-one system level comparison between the two RAM cell layouts in terms of readout extinction ratio, maximum speed, footprint, and power consumption concluding that the SOA-XGM-based RAM cell offers certain advantages when operational speeds not higher than 10 Gb/s are targeted, and the SOA-XPM-based RAM cell setup dominating when higher RAM serial speeds even up to 40 GHz are targeted.
Open accessProceedings ArticleDOI
Guangdeng Liao, Xia Znu, Laxmi Bnuyan 
12 Feb 2011
56 Citations
Experimental results demonstrate that the new server I/O architecture improves the network processing efficiency by 47% and web server throughput by 14%, while substantially reducing the NIC hardware complexity.
Proceedings ArticleDOI
Seokin Hong, Jongmin Lee, Soontae Kim 
04 Dec 2014
33 Citations
Evaluation results show that Ternary cache achieves the data density benefit of MLC STT-RAM and the reliability benefit of SLC STT-RAM.

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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.
What are the most recent statistics about resilience?
5 answers
Recent statistics on resilience show a growing interest in the concept within various fields. Studies from 2014 to 2018 highlight the multifaceted nature of resilience in sustainability literature, with a focus on frameworks, indicators, and models. Additionally, research in robust statistics has expanded the understanding of resilience by considering perturbations under different distances, leading to the development of robust estimators based on minimum distance functionals. Moreover, in the context of Software-Defined Networking, the performance of the Least Recently Used (LRU) mechanism under DoS attacks has been evaluated, emphasizing the importance of utilizing rule statistics information to enhance cache attack resistance. These diverse studies collectively contribute to the evolving landscape of resilience research across various disciplines.
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.
How to mitigate DDoS signalling attack?
5 answers
To mitigate DDoS signalling attacks effectively, various strategies have been proposed in recent research. One approach involves leveraging the virtualized environment of 5G to implement intelligent resource scaling strategies like REPEL, which uses game theory to build a defense front line against attacks while preserving legitimate traffic. Another method includes a resource sharing technique combined with IP address tracking and memory management systems to filter out unwanted IP packet requests, showcasing the potential to mitigate a significant portion of attacks with a limited number of VMs. Additionally, a defense mechanism based on randomization techniques has been shown to significantly reduce signaling data volume in 5G systems, particularly under malicious conditions, by up to 70% while avoiding unnecessary resource consumption. These innovative approaches demonstrate promising avenues for combating DDoS signalling attacks in modern network environments.
Why is the browser essential to Google chrome?
4 answers
The browser is essential to Google Chrome because it serves as the primary gateway for internet users to engage in various online activities. Digital forensic practitioners rely on analyzing browser artifacts, such as cache files, to investigate cybercrimes related to internet usage. Additionally, the comparison between Google Chrome and Brave browsers reveals that both share similar data structures, emphasizing the importance of understanding browser artifacts for forensic analysis. Furthermore, the development of browser extensions, like the one for Google Chromium, highlights the need to address security threats like cross-site scripting attacks, which can exploit vulnerabilities in browsers. Overall, browsers like Google Chrome play a crucial role in providing users with access to online content while also posing security challenges that need to be addressed.
Because is hot?
5 answers
Fever is a physiological response that results from increased prostaglandin synthesis in the hypothalamus due to pyrogenic cytokines, signaling immune system activation. Fever can be caused by various factors such as infections, immune disorders, malignancy, and drug side effects, among others. It is a common reason for seeking pediatric care, with most cases being due to viral illnesses that require supportive care and parental education. Despite fever being a beneficial response aiding in clearing infections, concerns about fever, termed fever phobia, persist among parents and healthcare professionals, leading to unnecessary treatments and anxiety. The management of fever in children involves educating parents, preventing complications, and providing supportive care, emphasizing the importance of understanding the underlying causes and appropriate management strategies.