scispace - formally typeset
Search or ask a question

How much RAM do you need for a Fivem server? 

Data security
Scalability
Server
Cache pollution
Cache

Answers from top 9 papers

More filters
Papers (9)Insight
Journal Article•DOI
A Hybrid PRAM and STT-RAM Cache Architecture for Extending the Lifetime of PRAM Caches
Yongsoo Joo, Sangsoo Park 
01 Jul 2013-IEEE Computer Architecture Letters
17 Citations
To extend the lifetime of phase change RAM (PRAM) caches, we propose a hybrid cache architecture that integrates a relatively small capacity of spin transfer torque RAM (STT-RAM) write buffer with a PRAM cache.
Proceedings Article•DOI
Application level ballooning for efficient server consolidation
Tudor-Ioan Salomie, Gustavo Alonso, Timothy Roscoe, Kevin Elphinstone 
15 Apr 2013
81 Citations
In virtualization scenarios (cloud computing, server consolidation), however, static peak provisioning of RAM to applications dramatically reduces the efficiency and cost-saving benefits of virtualization.
Book Chapter•DOI
The server array: a scalable video server architecture
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.
Journal Article•DOI
A vortex transitional memory cell for 1-Mbit/cm/sup 2/ density Josephson RAMs
Hideaki Numata, Shuichi Nagasawa, Shuichi Tahara 
01 Jun 1997-IEEE Transactions on Applied Superconductivity
13 Citations
These results are promising for developing a 1-Mbit/cm/sup 2/ density Josephson RAM.
Open access•DOI
A survey of synchronous RAM architectures
Matthias Gries
01 Jan 1999
5 Citations
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.
Journal Article•DOI
Servers and data centers energy performance metrics
Abdlmonem H. Beitelmal, Drazen Fabris 
01 Sep 2014-Energy and Buildings
27 Citations
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.
Proceedings Article•DOI
A server-to-server view of the internet
Balakrishnan Chandrasekaran, Georgios Smaragdakis, Arthur W. Berger, Matthew Luckie, Keung-Chi Ng 
01 Dec 2015
18 Citations
We highlight how dual-stack servers can be utilized to reduce server-to-server latencies by up to 50 ms. Our results indicate that significant daily oscillations in end-to-end RTTs of server-to-server paths is not the norm, but does occur, and, in most cases, contributes about a 20 ms increase in server-to-server path latencies.
Journal Article•DOI
STT-RAM Cache Hierarchy With Multiretention MTJ Designs
Zhenyu Sun, Xiuyuan Bi, Hai Li, Weng-Fai Wong, Xiaochun Zhu 
01 Jun 2014-IEEE Transactions on Very Large Scale Integration Systems
38 Citations
The experiments show that on the average, our proposed multiretention level STT-RAM cache reduces total energy by as much as 30%-74.2% compared to previous single retention level STT-RAM caches, while improving instruction per cycle performance for both two-level and three-level cache hierarchies.
Open access•Book Chapter•DOI
The More the Merrier - Federated Learning from Local Sphere Recommendations
Bernd Malle, Nicola Giuliani, Peter Kieseberg, Andreas Holzinger 
29 Aug 2017
19 Citations
Without the need for powerful server infrastructures, even small companies could be scalable to millions of users easily and cost-efficiently; 2.

Related Questions

How much RAM do I need for Plex server?8 answers
How much RAM do you need to run a Fivem server?8 answers
How much RAM is needed for Plex server?5 answers
How much RAM is needed for a Terraria server?8 answers
How much RAM is needed to run a Fivem server?8 answers
How to start a Fivem server on a VPS?8 answers

See what other people are reading

How does the choice of clustering algorithm impact the accuracy of the OD matrix in trip analysis?
10 answers
The choice of clustering algorithm significantly impacts the accuracy of the Origin-Destination (OD) matrix in trip analysis by affecting the identification of travel patterns, the scalability of route choice models, and the efficiency of OD estimation methods. Clustering algorithms, by grouping similar data points, help in simplifying the complex, high-dimensional OD matrices into more manageable sub-groups, which can be crucial for accurately capturing and analyzing trip patterns. For instance, the use of geographical window-based structural similarity index (GSSI) as a structural proximity measure in the DBSCAN algorithm has shown effectiveness in clustering high-dimensional OD matrices. This approach addresses the challenge of multi-density data points by proposing clustering on individual subspaces, which aids in identifying typical travel patterns and estimating corresponding OD matrices. Similarly, the application of clustering analysis to obtain a small set of representative OD pairs and routes has been demonstrated to improve the predictive accuracy of route choice models, by scaling up observations on traveler behavior from these representative sets to the entire network. Moreover, the integration of clustering methods in the optimization process, as seen in the sensitivity-based OD estimation (SBODE) using the Levenberg-Marquardt (LM) algorithm, leverages information from the Jacobian matrix to define clusters of OD-demand variables. This approach not only enhances the efficiency and efficacy of the algorithm but also reduces the risk of being trapped in local optima by breaking the OD estimation problem into smaller, more manageable sub-problems. In summary, the choice of clustering algorithm plays a pivotal role in enhancing the accuracy of OD matrices by facilitating the identification of distinct travel patterns, enabling scalable and adaptable route choice modeling, and improving the methodological and computational efficiency of OD estimation processes. This underscores the importance of selecting appropriate clustering algorithms tailored to the specific characteristics and challenges of the OD matrix data being analyzed.How has the use of FIFOs (named pipes) impacted modern operating systems?
5 answers
The use of FIFOs, also known as named pipes, has significantly impacted modern operating systems by facilitating efficient data transfer and communication between different devices and processes. FIFOs play a crucial role in real-time digital system design, especially for data streaming applications like multimedia devices, and are integral to Unix and Linux interprocess communication architectures. They provide a powerful model for transferring data between devices, utilizing memory controllers, CPUs, and data controllers for seamless data delivery. Additionally, FIFOs with integrated error management ensure reliable data transfer by reacting to errors and maintaining known operational states, preventing data gaps or overlaps during communication. The efficient integration of FIFOs in modern operating systems enhances performance, resource utilization, and overall system design.What is vector search?
5 answers
Vector search involves searching for similarities between vectors in large-scale information retrieval and machine learning systems. It plays a crucial role in search engines like Google and Bing, processing numerous queries per second on massive document datasets by evaluating vector similarities between encoded query texts and web documents. Various approaches to vector search include utilizing hardware accelerators like FPGAs to enhance performance, implementing specialized algorithms for efficient searching, and developing fast-search algorithms to reduce computational demands in methods like vector quantization. These methods aim to improve search efficiency, reduce execution time, and enhance the scalability of vector search systems, paving the way for future integration of advanced technologies in data centers and AI supercomputers.What factors contribute to user trust or lack thereof in the telemedicine platform's data protection measures?
5 answers
User trust in telemedicine platform's data protection measures is influenced by several factors. Firstly, the misuse of personal data due to inadequate data management knowledge can lead to breaches and cybercriminal attacks. Secondly, the need for stringent data security measures, including informed patient consent and high data security standards, is crucial for maintaining trust. Additionally, the use of blockchain technology in telemedicine can enhance data protection by providing secure data interaction and real-time accuracy of identity information, thus fostering trust among users. Overall, factors such as data management practices, regulatory compliance, and innovative technologies like blockchain play significant roles in shaping user trust in the data protection measures of telemedicine platforms.What are the current challenges in energy management in microgrids with distributed generation (DGs)?
4 answers
Current challenges in energy management in microgrids with distributed generation (DGs) include addressing uncertainties in modeling renewable-based DGs and battery storage, selecting appropriate models for accurate planning and operation. Furthermore, the shift towards decentralized energy management methods raises concerns about privacy and scalability, necessitating solutions to promote trusted collaboration and prevent collusion. Incorporating artificial intelligence (AI) in energy management systems (EMS) can enhance performance efficiency and reliability, but challenges like data privacy, security, scalability, and explainability need to be overcome. Additionally, ensuring robustness and energy efficiency in grid integration, selecting suitable EMS, and managing renewable resources and storage systems pose significant challenges in maintaining stability and security of microgrids.How optimal power flow (OPF) solution can find equivalent networks optimal parameters in electrical power systems?
10 answers
Optimal Power Flow (OPF) solutions are crucial for determining equivalent networks' optimal parameters in electrical power systems, ensuring efficient, reliable, and cost-effective operation. The Hybrid Flying Squirrel Search Algorithm (HFSSA) demonstrates significant potential in solving the OPF problem by optimizing generation fuel cost, emission, and transmission losses, thus providing a novel approach to finding optimal values for control parameters in complex power systems. Similarly, distributed optimization methods, particularly those leveraging digital twins for simulation within optimization, offer scalable solutions for grid-edge coordination in three-phase unbalanced distribution systems, validating the robustness of distributed algorithms under stressed communication conditions. Convex relaxation and linear approximation models, such as second-order cone programming (SOCP), semi-definite programming (SDP), and linear programming (LP), have been explored for their feasibility, optimality, and scalability in addressing the non-convex and non-linear nature of D-OPF problems. These methods provide insights into the relative performance of different formulations, highlighting the importance of selecting appropriate models based on the problem type and the potential for AC-infeasible solutions. Graph signal processing and machine learning, particularly through the use of graph neural networks (GNNs) and message passing graph convolution (MPGCN) models, present innovative data-driven approaches. These methods learn the nonlinear parametrization between power demanded and allocation, minimizing costs while considering electrical constraints, thus offering computationally efficient solutions that avoid constraint violations. Recursive programming and convex models for DC networks also contribute to the OPF solution space, ensuring global optimum finding through convexity and efficiently solving classical power flow problems with reduced convergence error. Collectively, these advancements across various methodologies underscore the multifaceted approach required to optimize equivalent networks' parameters in electrical power systems, highlighting the importance of algorithm selection, computational efficiency, and the ability to handle complex, non-linear problems.What are the different definitions of architect sensitivity in design process?
4 answers
Architect sensitivity in the design process can be defined in various ways. Firstly, it involves creating architectures that consider the sounds of the immediate environment to enhance comfort and sensory perception. Secondly, it pertains to the robustness of architecture in accommodating changes and updates during the design process and product life-cycle, measured as design sensitivity through performance reserve and slack in meeting timing requirements. Additionally, architect sensitivity encompasses the ability to analyze the impact of uncertainties in property estimates on process design, identifying critical properties and their relationships with design variables. Lastly, architect sensitivity is honed through international exposure, broadening personal experiences through travel to enhance affinity to local nuances over global standards in architectural practice.What's direct memory access?
4 answers
Direct Memory Access (DMA) is a method that allows peripherals to access the system's memory directly without involving the CPU. DMA controllers facilitate this process by managing data transfers between peripherals and memory, enhancing system performance by reducing CPU involvement in data transfer operations. DMA operations involve transferring data in blocks, optimizing memory access efficiency and reducing latency. DMA controllers can execute microprograms for data reorganization in shared local memory, enhancing processing capabilities. Additionally, DMA management includes cache memory address handling and TCE cache flag utilization for efficient data transfer operations. Overall, DMA streamlines data movement, enhances system performance, and minimizes CPU workload in managing memory operations.What are the best practices for implementing backups in an OpenNebula environment?
4 answers
Best practices for implementing backups in an OpenNebula environment involve utilizing methods like creating snapshots and backups of application output in virtual machines. Additionally, employing a system backup solution can prevent data loss by duplicating and storing server data periodically. Implementing hardware-based mirroring technology can ensure direct mirroring of NVRAM data between servers at an atomic level, enhancing data redundancy and protection. For backing up encrypted data in backend storage without exposing it, a frontend client system with client-controlled keys and minimal server-side processes is recommended. Furthermore, a computer-implemented method for backups may involve identifying data volumes, locating data objects, and backing up references to data objects in archival data stores, optimizing backup efficiency. These practices collectively enhance data protection and recovery capabilities in an OpenNebula environment.What is the current state of research on hierarchical address event representation in neuromorphic computing?
5 answers
The current state of research on hierarchical address event representation (AER) in neuromorphic computing is focused on enhancing scalability and efficiency in large-scale neural processing. Novel techniques like Time-to-Event Margin Propagation (TEMP) leverage causal temporal primitives within the AER protocol for scalable computing, demonstrating high accuracy in tasks like image recognition. Additionally, advancements in multicast network-on-chip (NoC) architectures, such as Multicast-HiAER, enable high-bandwidth inter-core spike event transmission, supporting brain-scale neural network connectivity across processing cores. Furthermore, the introduction of hierarchical token-rings (HTR) encoding schemes improves latency, throughput, and power efficiency in scientific imaging applications, addressing performance issues with existing encoding methods in large sensor arrays. These research directions signify a growing emphasis on optimizing communication protocols and network architectures for efficient and scalable neuromorphic computing systems.What is A/B Split Testing?
5 answers
A/B Split Testing, also known as A/B Testing, is a method commonly used in industry to enhance innovation by introducing new features or modifying existing software. This approach involves conducting experiments on different segments of a population to determine the effectiveness of various strategies. By automating A/B testing pipelines and splitting the population based on user criteria, A/B Split Testing accelerates the identification of statistically significant results compared to traditional sequential testing methods. It aims to optimize marketing activities, such as email campaigns or website design, by comparing different versions to improve user experience and profitability. A/B Split Testing is a valuable tool for businesses of all sizes, offering a cost-effective and efficient way to iterate products and enhance user engagement.