Managing Physical Folios of Objects Between Nodes

The limitation of physical memory (RAM) is a primary constraint hindering optimal performance in modern operating systems, especially when running large applications or performing intensive multitasking, often resulting in crashes and high latency. This research aims to quantitatively analyze the effectiveness of Virtual Memory (VM) implementation as a solution to this RAM constraint on the Windows 10 operating system, focusing on VM’s impact on CPU performance, GPU performance, and multitasking response. The methodology employed is a controlled experiment using industry-standard benchmarks: Cinebench R20 (CPU), Unigine Heaven (GPU), and response time measurements in intensive multitasking scenarios. Experimental results demonstrate that VM activation improves CPU/GPU performance by up to 5% and accelerates multitasking response time by up to 15%, confirming VM's effectiveness in mitigating memory bottlenecks. Nevertheless, this study also identifies potential performance overhead stemming from excessive paging and swapping processes, which trigger the phenomenon of Thrashing. Therefore, the research recommends a dual optimization strategy to achieve maximum and stable performance: software optimization via the Least Recently Used (LRU) algorithm to suppress page faults, supported by hardware optimization including the use of an SSD for the swap file and increased RAM capacity.

Virtual memory was developed to automate the movement of program code and data between main memory and secondary storage to give the appearance of a single. This technique greatly simplified the programmer’s job, particularly when program code and data exceeded the main memory’s size. In modern operating system, secure file system can protect confidential data from unauthorized access. However, once an authorized process has accessed data from a cryptographic file system, the data can appear as plaintext in the unprotected virtual memory backing store, even after system shutdown. This paper proposes new approach for adding multilevel security capability to virtual memory management. It uses partial swap encryption based on process’s security policy. Volatile encryption keys are chosen randomly, and remain valid only for short time periods. Performance comparison shows the total overhead of proposed method.

This study aims to explore the influence of decision-making, financial data analysis, and human resource training on company performance, as well as the role of artificial intelligence (AI) moderation in these relationships. Using the path analysis method with AMOS software, data was collected from 100 employees in a Telecommunications company in Indonesia. The results show that decision-making and analysis of financial data have a positive and significant influence on company performance, while human resource training does not show a significant influence. Additionally, AI serves as a significant moderator in the relationship between decision-making and company performance, but not in the relationship between financial data analysis and performance. These findings emphasize the importance of good decision-making and financial data analysis in improving organizational performance, and demonstrate the potential of AI in amplifying the positive impact of decision-making. This study has limitations in sample size and context, which need to be considered for further research.

The purpose of this research is to evaluate tax compliance based on the behavior of taxpayers' financial and non-financial data. This research is expected to be useful as material for evaluating the level of tax compliance of taxpayers. This study uses a case study method (single case) and uses a mixed method. Descriptive analysis is used to analyze data from the research object in the form of a single case unit analysis. The results of this study indicate the perceived behavior control of taxpayers that shows low tax compliance behavior. The combination of analysis of financial and non-financial data from taxpayers produces a useful evaluation in determining the level of tax compliance. Based on this, the low achievement of tax revenue realization and high tax audit performance is directly influenced by the low tax compliance behavior of taxpayers.
 Keywords: Tax Compliance; Behavior; Financial Data; Non-Financial Data.

Decision support systems use statistical and computational techniques. These statistical and computational techniques are used to predict the outcomes when it is applied on financial data. This chapter discusses the time series forecasting and predictive analytics within the financial domain. This work will cover the ARIMA method and deep learning methods. These methods will facilitate forecasting future through the analysis of financial time series data. The outcomes generated by these methods will help the financial managers and other professionals in the financial domain to improve their results. This work investigates predictive analytic techniques in the finance domain and computational algorithms to forecast the future trends.

We present a novel approach for the measurement of one-way delays and throughput between network nodes in heterogeneous environment over Wide Area Network (WAN). This research investigates performance evaluation of remote data transfers on heterogeneous services and technologies environment. We propose an enhanced equation to evaluate the performance of network traffic management via Little Law and Queuing theory to improve the evaluation algorithm. To get accuracy results on the performance of simulation model, we measure (verify and validate) data from lab experiment. We use network management tool to capture those data and Ping Plotter application (network analyzer) to generate traffic. As a result, this simulation model can provide a good approximation of the real traffic (one-way delays and throughput) observed in the real network environment (Wide Area Network, WAN). Through laboratory and field experiments, it shows that the model via simulation is capable of approximating the performance of remote data transfers over heterogeneous services and techniques within a minimum error range.

Many next-generation scientific and business applications feature large-scale data-intensive workflows, which require massive computing resources for execution on high-performance clusters in cloud environments. Such computing resources (e.g., VCores and virtual memory) requested through parameter setting in big data systems, if not fully utilized by workloads, are simply wasted due to the nature of exclusive access made possible by containerization. This necessitates accurate modeling and prediction of workflow performance to make an effective recommendation of appropriate parameter settings to end users. However, it is challenging to determine optimal workflow and system configurations due to the large parameter space and the interaction between various technology layers of big data systems. Towards this goal, we propose a machine learning-based feature selection method to identify influential parameters based on historical performance measurements of Spark-based computing workloads executed in big data systems with YARN. We first identify a comprehensive set of parameters across multiple layers in the big data technology stack including workflow input structure, Spark computing engine, and YARN resource management. We then conduct an in-depth exploratory analysis of their individual and coupled impact on workflow performance, and develop a performance-influence model using random forest for prediction. Experimental results show that the proposed approach identifies important features for performance modeling and achieves high accuracy in performance prediction.

In wide-area networks (WANs), efficiently allocating distributed resources presents a significant challenge due to increasing node numbers and complex network conditions. Traditional algorithms, which heavily depend on global information, encounter scalability limitations. This study proposes an adaptive distributed optimal resource allocation consistency algorithm designed for WANs. The approach dynamically adjusts resource allocation based on real-time network conditions and user requirements, reducing dependence on global data. A time window distribution model and an information coding model are employed to optimize the resource allocation process. By integrating a Q-learning algorithm and extending the Paxos algorithm to WANs, global consistency among network nodes is ensured. The experimental results demonstrate the algorithm’s superior performance, particularly in low-bandwidth WAN scenarios (below 2 Mb/s), where it significantly enhances throughput. An average resource utilization rate exceeding 97% is achieved, along with rapid convergence and high parallelism. This research underscores the algorithm’s effectiveness and scalability, providing a robust solution to distributed resource allocation challenges in complex WAN environments.

A critical challenge of performing data transfers or remote reads is to be as fast and efficient as possible while, at the same time, keeping the usage of system resources as low as possible. Ideally, the software that manages these data transfers should be able to organize them so that one can have them run up to the hardware limits. Significant portions of LHC analysis use the same datasets, running over each file or dataset multiple times. By utilizing "ondemand" based regional caches, we can improve CPU Efficiency and reduce the wide area network usage. Speeding up user analysis and reducing network usage (and hiding latency from jobs by caching most essential files on demand) are significant challenges for HL-LHC, where the data volume increases to an exabyte level. In this paper, we will describe our journey and tests with the CMS XCache project (SoCal Cache), which will compare job performance and CPU efficiency using different storage solutions (Hadoop, Ceph, Local Disk, Named Data Networking). It will also provide insights into our tests over a wide area network and possible storage and network usage savings.

In this paper we propose NCRS, a Network Computational Resource Sharing grid based on network RAM. In NCRS, the computing node regards the free memory of other nodes in networks as a complement of local memory and uses it to store the large amount of intermediate data during computation. When the remote data is required, rather than blocks and gets it from remote nodes (or local disks), the computing node sends the related instructions to the remote nodes where the data locates. We refer to the memory on the remote nodes in networks as Network Intelligent Memory (NIM). NIM carries out the received instructions, and by this means the computing node reduces the page-swaps with local disks and the instructions locally carried out.

Virtual memory techniques are available in modern operating system will permits the execution of a program even if it is partially available in memory thus providing an illusion of very large memory to the user and freeing the user from the concern of large program size. The primary storage size has increased by multiple order of magnitude. With several gigabytes of primary memory, algorithms that require a periodic check of each and every memory frame are becoming less and least practical. Some basic assumptions used by the traditional page replacement algorithms were invalidated, resulting in a revival of research area. Currently the usage of the object oriented programming is much higher than the structured programming. Hence the locality of reference of user software has weakened. Due to the above problem for the page replacement methods also we have to think about new methods. In this paper mainly focused on the improvement of the processor performance. In order to achieve this, the number of page faults are tried to reduce, by using the page replacement method in the correct way. In modern operating systems mainly make use of the Least Recently Used (LRU) page replacement method, in which only the arrival time of the pages to the page frame are make used. Here we made an attempt to consider both the arrival time and the number of reference done on the page is considered. By consider these two parameter we developed a new algorithm Least Recently Used and Least Frequently Used (LRU-LFU) which will give a better performance than LRU page replacement method.

The article explores data mining methods that allow us to get helpful information from the data. The possibility of using these methods in practice in the financial sector was considered. Since financial activity is closely related to our social life, the use of data mining methods plays an essential role in the analysis and forecasting of the financial market in the modern era of big data. However, due to differences in the experience of researchers in different disciplines, it is not easy to use data mining methods when analyzing financial data. Therefore, creating a methodological basis for the practical application of data mining methods in the analysis of financial data is an urgent issue. The purpose of this article is to create a methodological basis for using data mining methods for efficient trading. When processing product data, a priori methods and visualization methods were used, and their implementation in practice was described. As a result, scenarios of computer applications were created as a sample of the practical implementation of the algorithms of these methods. Building a quantitative trading strategy requires first statistical analysis of the information in the market and then testing the quantitative model on the collected data. This study developed a quantitative trading system based on data mining methods. The primary development tool used is the Jupyter web platform, and three cores have been developed: quantitative data selection, strategy testing on data, time series analysis, and visualization. The developed system supports modules for making simple trading decisions.

High Energy Physics data processing and analysis applications typically deal with the problem of accessing and processing data at high speed. Recent studies, development and test work have shown that the latencies due to data access can often be hidden by parallelizing them with the data processing, thus giving the ability to have applications which process remote data with a high level of efficiency.Techniques and algorithms able to reach this result have been implemented in the client side of the Scalla/xrootd system, and in this contribution we describe the results of some tests done in order to compare their performance and characteristics. These techniques, if used together with multiple streams data access, can also be effective in allowing to efficiently and transparently deal with data repositories accessible via a Wide Area Network.

This paper describes a simple distributed mechanism for caching files among a networked collection of workstations. We have implemented it as part of Sprite, a new operating system being implemented at the University of California at Berkeley. A preliminary version of Sprite is currently running on Sun-2 and Sun-3 workstations, which have about 1-2 MIPS processing power and 4-16 Mbytes of main memory. The system is targeted for workstations like these and newer models likely to become available in the near future; we expect the future machines to have at least five to ten times the processing power and main memory of our current machines, as well as small degrees of multiprocessing. We hope that Sprite will be suitable for networks of up to a few hundred of these workstations. Because of economic and environmental factors, most workstations will not have local disks; instead, large fast disks will be concentrated on a few server machines.

Supply chain finance, a key tool for optimizing capital allocation across the industrial chain, faces core risks centered on disruptions and blockages in capital flows. This study focuses on capital flow risk in the context of supply chain finance, constructing a multi-level risk identification and quantitative assessment model and conducting empirical testing based on enterprise financial data. The study first systematically examines the formation mechanisms and transmission pathways of supply chain capital flow risk, identifying key dimensions such as core enterprise credit risk, trade background authenticity risk, operational risk, and external environmental risk. Furthermore, combined with enterprise financial statement data, a comprehensive capital flow risk assessment model integrating logistic regression with an indicator-based early warning system is constructed. The empirical analysis utilizes the financial data of 500 sample enterprises across the upstream, midstream, and downstream supply chains in my country's manufacturing and wholesale and retail sectors over the past five years, along with selected supply chain finance business data, which has been rigorously cleansed and processed. The results demonstrate that the quick ratio and cash flow gap volatility of core enterprises significantly explain the overall supply chain capital flow risk; abnormally prolonged accounts receivable turnover days for small and medium-sized enterprises is an important early warning signal of risk; the model achieves an 82.3% accuracy rate in identifying high-risk enterprises, with an average early warning time of three months. This study provides financial institutions with operational model tools and data support for accurately identifying, quantifying and managing supply chain finance cash flow risks, and has practical value for improving the robustness and efficiency of supply chain financial services.