Average System Response Time Research Articles

Optimizing storage on fog computing edge servers: A recent algorithm design with minimal interference.

The burgeoning field of fog computing introduces a transformative computing paradigm with extensive applications across diverse sectors. At the heart of this paradigm lies the pivotal role of edge servers, which are entrusted with critical computing and storage functions. The optimization of these servers' storage capacities emerges as a crucial factor in augmenting the efficacy of fog computing infrastructures. This paper presents a novel storage optimization algorithm, dubbed LIRU (Low Interference Recently Used), which synthesizes the strengths of the LIRS (Low Interference Recency Set) and LRU (Least Recently Used) replacement algorithms. Set against the backdrop of constrained storage resources, this research endeavours to formulate an algorithm that optimizes storage space utilization, elevates data access efficiency, and diminishes access latencies. The investigation initiates a comprehensive analysis of the storage resources available on edge servers, pinpointing the essential considerations for optimization algorithms: storage resource utilization and data access frequency. The study then constructs an optimization model that harmonizes data frequency with cache capacity, employing optimization theory to discern the optimal solution for storage maximization. Subsequent experimental validations of the LIRU algorithm underscore its superiority over conventional replacement algorithms, showcasing significant improvements in storage utilization, data access efficiency, and reduced access delays. Notably, the LIRU algorithm registers a 5% increment in one-hop hit ratio relative to the LFU algorithm, a 66% enhancement over the LRU algorithm, and a 14% elevation in system hit ratio against the LRU algorithm. Moreover, it curtails the average system response time by 2.4% and 16.5% compared to the LRU and LFU algorithms, respectively, particularly in scenarios involving large cache sizes. This research not only sheds light on the intricacies of edge server storage optimization but also significantly propels the performance and efficiency of the broader fog computing ecosystem. Through these insights, the study contributes a valuable framework for enhancing data management strategies within fog computing architectures, marking a noteworthy advancement in the field.

Design and experimental evaluation of a variable pesticide application control system for the air-assisted rubber tree powder sprayer.

In order to address the issues of uneven pesticide deposition and low pesticide utilization in rubber gardens caused by the traditional diffuse plant protection spraying method, this study focuses on the air-assisted powder sprayer and proposes a variable pesticide application control system. A variable pesticide application decision-making model integrating the leaf area index (LAI) was designed based on powdery mildew control standards and individual rubber tree information. According to the target powder spraying accuracy requirements, a control model of the air velocity adjustment device was established and a fuzzy proportional-integral-differential (PID) air velocity control system was developed. The simulation results indicate that the wind speed control system exhibits a maximum overshoot of 2.18% and an average response time of 1.48 s. The field experiment conducted in a rubber plantation revealed that when the air-assisted powder sprayer operates in the variable powder spraying mode, the average response time of the control system is 2.5 s. The control accuracy of each executive mechanism exceeded 95.9%. The deposition coefficient of variation (CV) at different canopy heights was relatively consistent, with values of 35.38%, 36.26% and 36.90%. In comparison to the quantitative mode, the variable mode showed a significant 20.03% increase in the effective utilization rate of sulfur powder. These research findings provide valuable technical support for the advancement of mechanized variable powder spraying equipment in rubber tree cultivation. © 2024 Society of Chemical Industry.

Research on human-robot interaction for robotic spatial 3D printing based on real-time hand gesture control

With the rapid advancements in three-dimensional (3D) printing, researchers have shifted their focus towards the mechanical systems and methods used in this field. While Fused Deposition Modelling (FDM) remains the dominant method, alternative printing methods such as Spatial 3DP (S-3DP) have emerged. However, the majority of existing research on 3D printing technology has been emphasizing offline control, which lacks the capability to dynamically adjust the printing path in real time. Such an limitation has resulted in a decrease in printing efficiency. Therefore, this paper proposes a human-robot interaction (HRI) method based on real-time gesture control for Robotic Spatial 3DP (RS-3DP). This method incorporates utilization of YOLOv5 and Mediapipe algorithms to recognize gestures and convert the gesture information into real-time robot operations. Results show that this approach offers a feasible solution to address the issue of discontinuous S-3DP nodes because it achieves a gesture-controlled robot movement accuracy of 91 % and an average system response time of approximately 0.54 s. The proposed HRI method represents a pioneering advancement in real-time control for RS-3DP, thereby paving the way for further exploration and development in this field.

Design and optimization of the Teaching evaluation system based on artificial intelligence

Abstract In today’s highly developed information society, the construction work of educational informatization has become the focus of the future development of major universities. This paper combines artificial neural networks and UML software modeling technologies to design a teaching evaluation system using artificial neural network technology. The system requirements are analyzed from three aspects: system feasibility, functional requirements, and neural network evaluation function. The proposed system performance requirements are based on these three aspects. The neural network approximation problem can be solved using the gradient descent method based on the BP neural network model. After a series of operations, such as data preprocessing, the Design of the teaching evaluation system is completed, realizing the joint role of teaching and neural network evaluation module, and at the same time, proposing a system optimization decision for the teaching quality aspect. The teaching evaluation system designed in this study passed the test as the average response time of the whole system was 94.366 seconds when the number of users was 100, and there was no phenomenon such as transaction unresponsiveness. After the use of the teaching system, the feedback from the students’ use shows that the number of computer science students who mastered more than 80% of the system concepts is 42, and the rest of the knowledge is concentrated in the number of people who mastered 40% to 80%.

Performance Modeling of Hyperledger Fabric 2.0: A Queuing Theory-Based Approach

Hyperledger Fabric (shortened to Fabric) is an open-source, enterprise-level, permissioned distributed ledger technology platform with a highly modular, configurable architecture. It supports writing smart contracts in general-purpose programing languages and has become the preferred choice for enterprise-level blockchain applications. However, the transaction throughput of the Fabric system remains a critical factor that restricts the further application of this technology in various fields. Therefore, it is necessary to evaluate and optimize the performance of the Fabric blockchain platform. Existing performance modeling methods need to be improved in terms of compatibility and effectiveness. To address this, we propose a performance-compatible modeling method for Fabric using queuing theory, which considers the limited transaction pool and the situation where node groups are attacked. Using the Fabric 2.0 version as an example, we have established a model of the transaction process in the Fabric network. By analyzing the model’s continuous 3D time Markov process, we solved the system stationary equation and obtained analytical expressions for performance indicators such as system throughput, system steady-state queue length, and system average response time. We conducted extensive analyses and simulations to verify the models’ and formulations’ accuracy and validity. We believe this approach can be extended to various scenarios in other blockchain systems.

Design and application of an intelligent communication platform for aesthetics education for internationalization

Abstract With the rapid development of information technology, aesthetics education is changing, and the use of computer technology for teaching has become the new norm in the teaching field. In recent years, many intelligent platforms for aesthetics education have emerged at home and abroad, but most of them are only simple video courses, which can hardly meet the needs of aesthetics education. In order to design an international-oriented intelligent communication platform for aesthetics education, this paper builds an intelligent platform serving aesthetics education based on the relevant technologies of intelligent learning communication and completes performance tests after implementation. On the user side, the average memory consumption of the intelligent platform is 15.6 MB, the average memory occupation rate is only 15.34%, and the average network traffic consumed per operation step is only 7.56 MB. The CPU consumption of the platform is also relatively low. The highest occupation rate is 3.76%, the lowest is 0.81%, and the average is only 2.58%. For the server-side Web test, the average system response time of the intelligent platform is 0.72 seconds, which is a very fast response time. In the concurrency test, the platform still runs smoothly under the full concurrency of 100 concurrency numbers and can respond to most users’ requests within 1 second. The intelligent platform is fully capable of meeting the needs of teachers and students in aesthetics education. The international-oriented intelligent communication platform for aesthetics education designed in this paper can help cultivate students’ aesthetic perception and thinking ability, thus improving their aesthetic literacy in an all-around way.

A BCI framework for smart home automation using EEG signal

Since the first recording in 1924, modern developments in technology have enabled human electroencephalogram (EEG) acquisition as a non-invasive process, enabling a multitude of opportunities to learn about human brain dynamics. With the capability to tap into localized brain dynamics, it is possible to correlate event-related potentials such as meditation, concentration, and motor control with localized brain activities, opening up a broad spectrum for exploration and implementation in prosthetic, control, and brain computer interfaces. In this work, an attempt has been made to explore human emotions and other intelligent states that can be interpreted to automate and control electrical appliances for differently abled people. A smart home automation model has been designed and implemented using a Think Gear Application-specific integrated circuit (ASIC) Module (TGAM) EEG sensor module integrated with a Bluetooth module, which serves as the core for control applications. A combination of external triggers and brain states are interpreted and forwarded to gain control of the connected appliances via a local server over the internet. Equipped with internet connectivity and Internet of Things (IoT), the system also facilitates long-distance communication and control, which can be easily translated to industrial control and automation applications. Based on a single-channel analog EEG signal acquisition module, this study details the development of a Brain Computer Interface (BCI) control and monitoring system for smart home automation with blink and attention features. The designed BCI system has a large bandwidth of 400 Hz, an easy setup, Morphological Component Analysis (MCA) based blink detection, monitoring, control of devices, and high accuracy at a low cost. Each subject completed three trials separated by one minute. The smart devices underwent testing in two states, namely on and off. The response time and accuracy of the system were recorded for each trial. The average system response time for the devices was determined to be 17.13 sec for switching ON and 20.66 sec for switching OFF, with an accuracy of 98.73% and 95.75% for ON and OFF states respectively.

Dynamic Monitoring System of Oil Pipeline Leakage for Oil and Gas Safety

The conventional oil pipeline leakage dynamic monitoring system has the problem of imperfect risk assessment link, which leads to the long alarm response time of the system. An oil and gas safety oriented oil pipeline leakage dynamic monitoring system is designed. Hardware part: select MLLD sensor as voltage acquisition device, and connect RS-485 serial standard bus and other circuit devices. Software part: identify the leakage hazard source of oil pipeline, capture the corresponding characteristic points of leakage negative pressure wave, determine the location of leakage point, build the risk assessment model based on oil and gas safety theory, design the safety management function of dynamic monitoring system software, and clarify the potential safety hazards of oil and gas pipeline. Experimental results: the average alarm response time of the oil pipeline leakage dynamic monitoring system based on data mining in this paper and the other two systems are 95.746s, 122.026s and 122.426s respectively, which proves that the oil pipeline leakage dynamic monitoring system integrating oil and gas safety theory has a higher value.

COGNITIVE RADIO SYSTEM FOR DYNAMIC ALLOCATION OF UHF BAND CHANNELS BASED ON 802.11AF AND USING THE ENERGY DETECTION ALGORITHM

This paper describes the development of a cognitive radio system through a software-defined radio approach, using universal software radio peripheral (USRP) devices and digital television equipment for communication in the UHF band. The algorithm was developed in LabVIEW and is based on energy detection, where the average power of the video carrier of the tv signal is calculated and compared with the established threshold value. As a result of detection, free channels are determined and assigned for the transmission of an Secondary User (SU), which will synchronize with the receiver the same working frequency. For the evaluation of the performance tests, a DTT transmitter and receiver were used, which simulate a Primary User (PU), and when the system detects it, it releases the frequency in use of the SU and assigns another free frequency to it to guarantee continuity of trasmission service. Tests were carried out to determine the efficiency of the system, taking into account the response time of the system and its efficiency to allow coexistence of systems within the same spectrum. System response times were measured, being 1.07 s and 41.47 s the shortest and longest response time respectively, for frequency change in order to avoid interference with the PU. Using sampling techniques, the average system response time was 21.33 s, proving that the system is efficient in environments with high interference for services that are not sensitive to latency.

Deadline-Constrained RSU-to-Vehicle Task Offloading Scheme For Vehicular Fog Networks

In Vehicular Fog Computing (VFC), the RSU-to-Vehicle (R2V) task offloading process is highly affected by undesirable yet sometimes inevitable events ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , buffer exhaustion, task HoL blocking and deadline expiry), the occurrence of which will notably alter the RSU's performance in terms of crucial Quality-of-Service (QoS) metrics such as the average system response time, the blocking and deadline expiry probabilities. This paper proposes a novel R2V Deadline-Constrained Task Offload (R2V-DCTO) scheduling scheme with the objective of improving the RSU's performance in terms of the above-mentioned metrics; hence, filling an important literature gap. For this purpose, a stochastic modelling framework is established to capture the RSU's functional dynamics and assess its performance as it operates under R2V-DCTO. Extensive simulations are conducted to confirm the model's validity and accuracy and then compare R2V-DCTO's performance to that of the often adopted FIFO scheme. Results indicate that, on average, R2V-DCTO outperforms FIFO by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$33.4\%$</tex-math></inline-formula> in terms of the probability of deadline mismatch and by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$83.3\%$</tex-math></inline-formula> in terms of mean system response time; those being critical QoS performance metrics.

Multimodal Optimization of Edge Server Placement Considering System Response Time

Mobile edge computing (MEC)deploys computing and storage resources close to mobile devices, enabling resource demanding applications to run on mobile devices with short network latency. In the past few years, large numbers of research works focused on the research hotspots in MEC, such as computation offloading and energy efficiency. However, few researchers have investigated the deployment of edge servers. On the one hand, blindly deploying numerous edge servers will result in a large amount of capital expenditure. On the other hand, the deployment of edge servers is a multimodal problem that should provide decision makers with multiple deployment options to deal with the impact of unmeasured real-world factors. Considering these factors, we study themultimodal optimization problem of edge server placement (MESP)with the goal of minimizing the average system response time in this work. Regarding the difficulty of the MESP problem, we propose a heuristic algorithm that combines particle swarm optimization and niching technology to obtain a set of competitive placement solutions. Extensive experiments over a real-world dataset show that the proposed algorithm can significantly reduce the system response time.

Estimating the response time of a data-intensive computing environment

Introduction: The amount of digital data is constantly growing as well as the need for its storage and processing for various purposes. To conduct data analysis, high-performance computing environments associated with parallelization methods, and, accordingly, data-intensive applications are used. The lack of quality tools for evaluating the effectiveness of the process of parallel data processing or tasks leads to excessive allocation of resources. Purpose: To develop mathematical models of data-intensive computing environments and methods for their performance analysis, i.e., for estimating the average system response time based on the data on system performance at the level of subtask solving. Results: We present a mathematical model of a parallel computing system in the form of a queueing system with parallel query processing on various architectures, including non-Poisson input flow and non-exponential service times. As a method for analyzing the average response time, we use a combination of simulation modeling with one of the machine learning methods (artificial neural networks). The effectiveness of the method is confirmed by numerical experiments and depends neither on the type of input flow, nor on the type of distribution of query service times, nor on the number of servers in the nodes of the system. The approximation error of the average response time does not exceed 10%, which makes it possible to optimize the generally accepted resource allocation, significantly reducing the amount of the resources. Practical relevance: The presented models and the method of their analysis can be used for efficient planning and allocation of resources for data-intensive systems.

Resource Cache Sharing System of Education Information Center Network Based on Internet of Things

The development of the internet of things has spawned new information concepts such as educational information sharing. Due to the openness of the internet of things, all kinds of mobile terminal devices can rely on the internet of things for data communication, information interaction, and resource sharing. Although the development of the internet of things has brought convenience to people’s lives, the internet of things is facing severe challenges in the fields of internal data sharing and cache sharing of educational information. The existing resource sharing systems do not fully consider the problems of privacy, shared data security, and data access control. This paper mainly studies the network integrated resource cache sharing system of the education information center based on internet of things. The data management system of the network education information center is developed on the basis of the school teaching management system and lifelong education public service platform, combined with the actual situation of student information. This paper combines the enrollment and information workflow of the student information management website, analyzes the problems existing in the current workflow, and then uses computer technology to standardize and transform the workflow, realize the information of the built-in resources in the data management, and solve the problems of data cache sharing and data statistics in the management of the education information center. The simulation results show that with the increase in the number of concurrent threads, the average response time of the edge cloud system deployed with two servers is smaller than that of the edge cloud system deployed with a single server, and the cloud system has good scalability.

Implementasi Metode Load Balancing Sebagai Upaya Meningkatkan Kinerja Server

The use of the internet turns out to cause problems that must be found a solution. One of them is the service provider's server load which continues to increase. This condition is triggered by the increasing number of clients from time to time. Some sites even reported receiving hundreds of thousands of connection requests simultaneously from a number of clients. From these problems, research is needed that focuses on how to design and build a server system that is able to handle the increase in incoming requests so that the server load can be unraveled. The purpose of this research is that the service provider's server can improve its service to the client. There are several approaches to overcome these problems, one of which is by applying the load balancing method. So that by implementing load balancing, the incoming large load will be distributed to each of the service provider's servers. In the tests that have been carried out, the scalability of the system has increased. This is proven when a system with load balancing is given 10000 connections, the test results have an average response time of 44.42 ms. As for the system without load balancing, the test results have an average response time value of 185.88 ms. From the test results, it can be seen that the average response time of the server system with load balancing is smaller than without load balancing, so the service performance of the system can be continuously improved by implementing load balancing.

Design and Optimization of University Management Information System Based on Internet of Things and Intelligent Computing Model

After the preliminary design of university management information system is completed, the related systems are optimized through IOT and intelligent computing. The data of the system is monitored and trained through IOT. Through the training data, it can be seen that the maximum difference between the actual output and the predicted output data of the Internet of Things is about 4. By calculating the error percentage of the predicted and actual output values, it is found that the maximum error percentage is about 0.0632, and less than 0.1 is a normal error. The IIA model is compared with MQT model, HAMT model, and SAR model in four different function states. Through the analysis of iteration times, it is found that the best value of IIA model is 12 and the average value is 14. Compared with the other three models, the convergence degree is higher, the calculation time is shorter, and the calculation results are more accurate. In order to explore whether the IIA optimization model proposed in this paper can run normally, the performance of the optimization system is tested by dividing the number of users into three groups: 100, 400, and 1000, and testing and analyzing the average response time, peak traffic response time, and test conclusion. According to the data, the average response time of IIA optimization model system is 2.031, 3.211, and 4.421. The average response time of related systems in traditional universities is 3.075, 4.563, and 5.097, respectively, and the test conclusions are all passed. According to the data analysis, it is known that the average response time of IIA optimization system is greatly reduced compared with the traditional system, which shows that this model has a good optimization effect for management information system. Finally, the success rate of IIA optimization model and traditional system is analyzed, which shows that performance is greatly improved after IIA optimization model is optimized. At present, the management information system is of great help to the entry of college students’ information. The management information system is used to enter, classify, and manage student information. The design and optimization of the system through IOT technology and intelligent computing-related models are helpful in improving the average response time of the system and improving the data processing capability of the system, which is convenient for register student information.

Improving Lagos State emergency medical services by analysing road traffic accident data, response time, and efficient allocation of ambulances

BackgroundEmergency medical services (EMS) play a critical role in the response to road traffic accidents (RTAs) by providing critical prehospital care to RTA victims, and thus helping to reduce the severity of injuries and casualties from RTAs. Analysing the efficiency of such systems and optimising the service (often with insufficient resources) would be highly beneficial to inform operating guidelines. In this study, we aimed to assess the ambulance utilisation rate for RTAs and provide insights about the system's efficiency and utilisation metrics in Lagos State, Nigeria. MethodsWe received hospital capacity information (type of hospital [eg, tertiary] and number of beds, nurses, operating rooms, and so on) from hospital survey data in Lagos. Additionally, we received RTA-specific intervention forms from the Lagos State Ambulance Service. With the limited amount of historical data, we highlighted methods to estimate the system parameters (eg, arrival rate, service rate, and so on) of RTA victims at various hospitals in Lagos. We also developed a parametric model and applied approximate dynamic programming approach for ambulance dispatch and location optimisation with a fixed number of base stations. Furthermore, our routing approach was accompanied by directions from OpenRouteService and queue analysis to determine the most appropriate hospital near an RTA for the EMS systems in Lagos. FindingsUsing patient-queue based wait-time analysis, we found that in over 80% of cases, one of the nearest three hospitals was the most appropriate recommendation for patient delivery. This exponentially decays with the next closest hospitals. Additionally, we found that optimising the ambulance relocation method, where the ambulance returns to a base selected by our model as opposed to its dedicated base or a random base, can reduce the average response time of the EMS system by 8%, or 3·5 min (from 41 min to 38 min). InterpretationAppropriate placement of mobile resources (ie, ambulances, health-care booths, and so on) can help to lower the response time of EMS systems in addressing RTAs. Similarly, optimal routing of patients to the most well prepared and nearby hospitals would significantly increase the system's efficacy and improve health outcomes. Our findings can provide valuable insight into this and further inform resource allocation and guidelines for health systems, by identifying those hospitals that have the highest burden of RTA victims and those that provide the most efficient care in a resource-limited setting. FundingNational Institutes of Health (1R21TW010991–01A1).

Design and Analysis of Chinese-Korean Translation System Based on Deep Transfer Learning

In order to better establish the Chinese-Korean translation system model, the deep transfer learning and the model system are tested and analyzed, and the following analysis results are obtained. By discussing the different adjustment mechanisms of deep transfer learning under MMD metric and Wasserstein metric, we can see that, in the MMD metric model, through analyzing datasets 1 and 2, the highest accuracy rate is 83.1% under multisource weight adjustment mechanism under MMD metric and the lower accuracy rate is 62.7% under no weight adjustment mechanism, and the accuracy rates of datasets 1 and 2 are higher than the average. Under Wasserstein metric, the accuracy of dataset 1 is 82.5% under multisource weight and 68.5% under no source weight, both of which are higher than the average. Three EEGNet models, EEGNet_0, EEGNet_1, and EEGNet_2, were established for comparative testing; according to the test results, it can be seen that EEGNet_1 has high accuracy and can be preferred for system establishment. By comparing the Chinese-Korean translation model with the blockchain model and the traditional translation model, it can be seen that when the translation sentences are 100 sentences, the average response time and peak traffic response time of the Chinese-Korean translation model are lower than those of the traditional translation model and the test conclusion is passed. When the test sentences are 1000 sentences, the average response time and peak traffic corresponding time of the Chinese-Korean translation model are still lower than those of the traditional method. Therefore, it can be seen that the efficiency and winning rate of the Chinese-Korean translation model are higher than those of the traditional translation system and meet the needs. According to the analysis of the performance test results of the translation system, it can be seen that the average response time and success rate of the Chinese and Korean translation system under different data are higher than those of the traditional translation system. When the test data are 500, the average response time of the translation system is 13 ms and the accuracy rate is 100%. When the test data are 3000, the average response time is 99 ms and the success rate is 99.6%. Therefore, the success rate of the translation system is basically above 99.6%, which is higher than that of the traditional translation system. In contrast, the Chinese-Korean translation system can improve the translation efficiency and accuracy and can be preferred.

ShareOn: Shared Resource Dynamic Container Migration Framework for Real-Time Support in Mobile Edge Clouds

Mobile Edge Cloud (MEC) technology is envisioned to play a key role in next generation mobile networks by supporting low-latency applications using geographically distributed local cloud clusters. However, MEC faces challenges of resource assignment and load balancing to support user mobility and latency-sensitive applications. Virtualized resource reallocation techniques including dynamic service migration are evolving to achieve load balance, fault tolerance and system maintenance objectives for resource constrained edge nodes. In this work, a compute and network-aware lightweight resource sharing framework with dynamic container migration, ShareOn, is proposed. The migration framework is validated using a set of heterogeneous edge cloud nodes distributed in San Francisco city, serving mobile taxicab users across that region. The end-to-end system is implemented using a container hypervisor called LXD (Linux Container Hypervisor) executing a real-time application to detect license number plates in automobiles. The system is evaluated based on key metrics associated with application quality-of-service (QoS) and network efficiency such as the average system response time and the migration cost for different combinations of load, compute resources, inter-edge cloud bandwidth, network and user latency. A detailed migration cost analysis enables evaluation of migration strategies to improve ShareOn&#x2019;s performance in comparison to alternative migration techniques, achieving a gain of 15&#x2013;22&#x0025; in system response time for highly loaded edge cloud nodes.

EddySuperblock: Improving NAND Flash Efficiency and Lifetime by Endurance-Driven Dynamic Superblock Management

Solid-state drives (SSDs) are widely used in current storage systems. SSD manufacturers usually aggregate physical blocks across planes into one superblock to improve SSD performance. However, due to the wear endurance variations among blocks and pages, the standard superblock scheme leads to lifetime and space waste of SSDs. Superblock granularity also causes higher and more uncertain garbage collection overhead. To address these problems, this work proposes endurance-driven dynamic superblock management called EddySuperblock to provide a longer lifetime and higher space utilization with a faster and deterministic response time, which designs a dynamic superblock organization strategy that links free strong blocks into one superblock according to the real-time wear index of each block and introduces page-level wear management to further unlock the lifetime potential of pages. To reduce the high garbage collection overhead caused by superblocks, a nonblocking parallel garbage collection policy is proposed, which can reclaim space and serve requests simultaneously with low overhead. We conduct a set of experiments on a real hardware experiment platform. Both the proposed EddySuperblock and representative superblock schemes have been implemented on the experiment platform. The experimental results show that our scheme greatly reduces the average system response time by 47.6% and prolongs the lifetime by 75.6% compared with the state-of-the-art superblock scheme, and the final-state flash utilization reaches 95.4%. In the end, this article presents the application status of our work in real-world scenarios.

The solution of the main types of tasks in the design of a distributed corporate information system of the sanatorium

This paper presents the solution of the problem of optimal allocation of collective use resources by the criterion of minimum average system response time to the requests of all users and the solution of the problem of achieving the required fault tolerance index with a speed limit for the distributed corporate information system of the sanatorium “Rossiyanka” of Anapa.