Memory Utilization Rate Research Articles

Construction of a Database for Vocal Music Teaching in Colleges and Universities Based on the Inheritance and Protection of the Central Plains Culture

Abstract Multi-cultural integration is an inevitable path to enhance the inheritance and protection of the Central Plains culture and to innovate the teaching mode of vocal music in colleges and universities. In this paper, we constructed a university vocal music teaching database system based on distributed database, and used semi-connection algorithm to query and optimize the data related to the integration of Central Plains culture into vocal music teaching resources. In order to make up for the defects of the semi-connection algorithm when facing a large number of tuples, the semi-connection algorithm is improved by introducing the idea of symmetric homology to realize the massive data scheduling of the vocal music teaching resources database. For the college vocal teaching database constructed in this paper, the query optimization test and performance test are carried out, and the teaching practice application is carried out with the vocal teaching database. The results show that the query throughput rate of vocal music teaching resources reaches 3.18*103MB/s under Read-only load when the number of running rounds reaches 16 rounds, and when the number of concurrent users of the vocal music teaching database is 1000, the CPU occupancy rate and memory utilization rate are 1.37% and 1.06%, respectively. By integrating Zhongyuan culture into the vocal music teaching database, more than 70% of the students indicated that it could enhance their interest in learning about Zhongyuan culture. The use of technology to realize the integration of vocal music teaching and the culture of the Central Plains provides a new reference for the inheritance and protection of the culture of the Central Plains.

Exploration and Application of Intelligent Service Mode of College Labor Union under the Spirit of the Third Plenary Session of the 20th CPC Central Committee

Abstract Under the guidance of the spirit of the Third Plenary Session of the Twentieth Central Committee of the Communist Party of China (CPC), colleges and universities have set off a boom in the construction of modernized information service systems. This paper relies on the importance and feasibility of the development of the intelligent service platform of the labor union in colleges and universities and preliminarily constructs an intelligent service platform that can meet the needs of the management of labor union work in colleges and universities. The platform adopts B/S architecture, uses the MVC pattern as a platform design plug-in, and uses the J2EE platform as the main technical platform for the development of the college union management information system. Through the information collaboration network to the union information modeling, with the help of the system’s SQL language function, through the ODBC database-driven service linking method, to ensure the safe and stable operation of the university trade union intelligent service platform. The test results show that under the IO-intensive load verification, the platform memory utilization rate is stable between 56.30~71.05%, the tuning strategy can be applied to the actual college union management scenario application, and the validity and versatility of the parameter tuning scheme have been verified. At the same time, the test results of platform information collaboration association strength show that the maximum information entropy of each associated node is 19.08, and the results can provide a basis for the construction of the information collaboration network of the university smart labor union.

Exploring the Short-Term Memory of Heart Rate Variability through Model-Free Information Measures.

In this work, we perform a comparative analysis of discrete- and continuous-time estimators of information-theoretic measures quantifying the concept of memory utilization in short-term heart rate variability (HRV). Specifically, considering heartbeat intervals in discrete time we compute the measure of information storage (IS) and decompose it into immediate memory utilization (IMU) and longer memory utilization (MU) terms; considering the timings of heartbeats in continuous time we compute the measure of MU rate (MUR). All measures are computed through model-free approaches based on nearest neighbor entropy estimators applied to the HRV series of a group of 15 healthy subjects measured at rest and during postural stress. We find, moving from rest to stress, statistically significant increases of the IS and the IMU, as well as of the MUR. Our results suggest that both discrete-time and continuous-time approaches can detect the higher predictive capacity of HRV occurring with postural stress, and that such increased memory utilization is due to fast mechanisms likely related to sympathetic activation.

Design of Library Archives Information Management Systems Based on Artificial Intelligence and Multimedia Technology

With the rapid development of information technology, the storage and management of library archives and books must meet the needs of readers. In this process, people's demands for various knowledge and information are increasingly diversified, and realizing the personalized service of library archives has become a better solution. At the same time, there are certain connections between various books, which is the basis for providing personalized services for readers. Through the performance test of the library archives information management system, it showed that all the performance indicators of the system met the required functional requirements. When the number of clients was 50, the memory utilization rate was 25.56%; the number of records was 428, and the average response time was 1058ms. Therefore, it is very necessary to use artificial intelligence technology and multimedia technology to design and build archive information management systems.

An efficient and secure data sharing scheme for cloud data using hash based quadraplet wavelet permuted cryptography approach

SummaryEnsuring the reliable and secure data transmission in cloud systems is very essential and demanding task in the recent decades. For that purpose, various security frameworks have been deployed in the existing works, which are mainly focusing the improving the privacy and confidentiality of cloud data against the unauthenticated users. Yet, it facing the major problems of inefficient accessing, increased time consumption, computational complexity, storage complexity, and memory utilization rate. Hence, this research work intends to develop an advanced and efficient cryptography model, named as, hash based quadraplet wavelet permuted cryptography (HQWPC) for the secured cloud data storage and retrieval operations. Here, the bilinear mapping and group hash function generation processes are performed to generate the keys used for cryptographic operations. Also, the zig‐zag scanning, wavelet coefficients extraction, and permutation processes are accomplished for data encryption. Consequently, the inverse of these operations is performed while decrypting the data. In addition to that, an integrated signature control policy authentication mechanism is employed for validating the authenticity of the cloud data users. This kind of signature verification process could efficiently increase the security level of cloud data. For validating the performance of the proposed security framework, various evaluation metrics have been utilized during analysis. Then, the obtained results are compared with the recent state‐of‐the‐art models for proving the efficiency of the proposed technique over the other techniques.

Cloud Computing-Based Medical Health Monitoring IoT System Design

With the continuous improvement of the national medical system, health monitoring combined with cloud computing and Internet of Things has become a concern. This study mainly discusses the design of the medical health monitoring IoT system based on cloud computing. From the user to the health service provider, there are three devices: sensor terminal, gateway terminal, and service platform. The sensor terminal is used to measure physiological indicators, such as blood pressure, electrocardiogram, blood oxygen saturation, heart rate, and other physiological indicators; the gateway terminal is used to link the sensor terminal to receive physiological indicators and forward them to the business platform; the gateway is also used to receive health information and other instructions issued by the server. In the community service mode, users can be divided into groups according to the community and region, and the corresponding service doctors and agent customer service personnel (nurses) can be assigned. Users can collect personal physiological indicators at home or outside through the medical terminal. These indicators and information are transmitted to the background health platform system through the mobile GSM-TD communication network. Users can also view their own historical health records and opinions of health consultants through the web/WAP website. Through the integration ability of the health cloud platform, relying on the interconnection with HIS, LIS, and other information systems of professional medical institutions, we jointly operate special value-added services, such as appointment registration, maternal and child healthcare, and medical communication (doctor-patient interaction), so that users can enjoy the remote service and guidance of professional medical institutions by subscribing to health value-added services. The CPU utilization rate is 40%, the internal utilization rate is 7.44 G, the memory utilization rate is 11.8%, and the network bandwidth is 591.87 M. During the whole test process, the indicators are stable, and there are no restart, crash, and other phenomena, so the system performance meets the design requirements.

Dynamic computation offloading algorithm based on particle swarm optimization with a mutation operator in multi-access edge computing.

The current computation offloading algorithm for the mobile cloud ignores the selection of offloading opportunities and does not consider the uninstall frequency, resource waste, and energy efficiency reduction of the user's offloading success probability. Therefore, in this study, a dynamic computation offloading algorithm based on particle swarm optimization with a mutation operator in a multi-access edge computing environment is proposed (DCO-PSOMO). According to the CPU utilization and the memory utilization rate of the mobile terminal, this method can dynamically obtain the overload time by using a strong, locally weighted regression method. After detecting the overload time, the probability of successful downloading is predicted by the mobile user's dwell time and edge computing communication range, and the offloading is either conducted immediately or delayed. A computation offloading model was established via the use of the response time and energy consumption of the mobile terminal. Additionally, the optimal computing offloading algorithm was designed via the use of a particle swarm with a mutation operator. Finally, the DCO-PSOMO algorithm was compared with the JOCAP, ECOMC and ESRLR algorithms, and the experimental results demonstrated that the DCO-PSOMO offloading method can effectively reduce the offloading cost and terminal energy consumption, and improves the success probability of offloading and the user's QoS.

KCSS: Kubernetes container scheduling strategy

The Kubernetes framework is a well-known open-source container-orchestration system widely used in industrial and academic fields. In this paper, we introduce a new Kubernetes Container Scheduling Strategy called KCSS. The goal of the KCSS is to optimize the scheduling of several containers submitted online by users to improve the performance concerning the user need in terms of makespan and the cloud provider need in terms of power consumption. In the literature, several container scheduling strategies are proposed. Each one uses a dedicated approach to select one node from the node set supplied by the cloud infrastructure. Then, this node welcomes the newly submitted container. The majority of the proposed container scheduling strategies select for each newly submitted container a node based on a single criteria, such as the number of running containers or the amount of available resources in each node. However, the scheduling based on a single criterion downgrades the performance because the scheduler has a limited vision of the state of the cloud infrastructure and the user need. The contribution of our KCSS is to introduce a multi-criteria selection of the node. This approach provides the scheduler with a global vision about the state of the cloud and the user’s need. The idea is to select from each newly submitted container the best node, with a good compromise between hybrid criteria related to the cloud infrastructure and the user need. In the KCSS, we consider six key criteria: (1) the CPUs utilization rate in each node; (2) the memory utilization rate in each node; (3) the disk utilization rate in each node; (4) the power consumption of each node; (5) the number of running containers in each node; and (6) the time of transmitting the image selected by the user for its container. In our context, the KCSS is based on a multi-criteria decision analysis algorithm to aggregate all criteria in a single rank. Then, select the node which has the highest rank to execute the new submitted container. The multi-criteria algorithm used by KCSS is the Technique for the Order of Prioritisation by Similarity to Ideal Solution (TOPSIS) algorithm. The KCSS is implemented in Go language inside the Kubernetes framework with minimal changes to be used easily with the next Kubernetes versions. The experimental results show that the KCSS improves the performance under different scenarios compared to other container scheduling strategies.

Improve Image Decoding in Lightweight Environment Using a Coroutines Based Approach

The JPEG2000 still image compression standard, while providing a remedy for the many shortcomings of its predecessor JPEG, is still slow to establish itself on the Internet. This problem is mainly due to the complexity of the COder-DECoder (CODEC) which implies its non-adoption by large firms and platforms in the field of image acquisition, processing and transmission. Indeed, the encoding and decoding process consumes a lot of CPU, memory and energy resources and takes a lot of computing time. The objective of this paper is to propose a model for decoding jpeg2000 on lightweight devices running on the Android mobile operating system. This implementation uses coroutines, which are a lightweight process model with reduced resource consumption costs compared to conventional AsyncTask threadsets. The model minimizes decoding time while minimizing CPU and memory usage, resulting in a fast and energetically economical decoded image. The results of integrating the coroutines from the main thread into the decoding process instead of the AsyncTask from the main thread produced better performance in terms of computation time, CPU and memory utilization. Indeed, the use of our model has led to a gain of around 23.41% in execution time, 9.8% in CPU utilization rate and 18.56% in memory utilization rate, compared to the model proposed in the literature which uses the threads.

Design and development of exponential lion algorithm for optimal allocation of cluster resources in cloud

Cloud computing is one of the new age technologies which has great prominent factor in the development of the enterprises and markets. The major exertion in the cloud computing is related to the resource being allocated. The optimal resource allocation is one which allocates the best suitable cluster resources for the task to execute with consideration of the different parameters, such as time, cost, and scalability, makespan, reliability, availability, throughput, resource utilization and so on. In this paper, a resource allocation optimization method in the cloud computing based on the exponential lion algorithm is proposed. The exponential lion based resource allocation for cloud computing taken into account saves the execution time, run time, and improves the revenue for the cloud provider. The proposed E-Lion based resource allocation approaches are compared with the PSO, SL-PSO, and Lion using the performance measures profit, CPU utilization rate, and memory utilization rate. The simulations of the experiments show that the algorithm in this paper has improved the algorithm performance efficiently with profit maximal profit of 38.74 and minimal CPU and memory utilization rate of 0.00031, and 0.00036 respectively.

FOG: A Fast Out-of-Core Graph Processing Framework

In this paper we present FOG, an open source graph processing framework designed for out-of-core (external memory) graph processing (https://github.com/mrshawcode/fog). FOG provides a set of programming interfaces that break down update functions of vertices to their incident edges so as to process the functions with edge-centric manner. By these, FOG gives intuitive and productive programming interfaces, and achieves high main memory utilization rate and processing efficiency at the same time. Moreover, FOG proposes an in-place update shuffling mechanism to improve the performance by dramatically reducing disk I/Os during computing. By extensive evaluations on typical graph algorithms and large real-world graphs, we show that FOG outperforms existing out-of-core graph processing systems, including GraphChi, X-Stream and TurboGraph. By comparing the performances of FOG and those of state-of-art distributed graph processing frameworks, we show that only by using just a commodity PC, FOG achieves comparable or even better performance than the best distributed graph processing framework that uses an Amazon EC2 cluster with 128 nodes.

Test and Analysis GPU-Accelerated in Molecular Dynamics Simulation

GPU computing is the use of a graphics processing unit together with a CPU to accelerate large scale scientific and engineering applications, such as molecule simulation. The paper use NVIDIA Tesla C2050NVIDIA GTX580 and NAMD 2.9 simulates three differences molecule systems: Beta2,SET9 and Ubiquitin. We compared and analyzed the results of the simulations experiment, and come to conclusion that the difference molecule systems will get the difference speed accelerated. The computing times of four GPU is nearly half of the time used by one GPU; and this is especially in the case of macromolecules system. Furthermore, from the GPUs memory utilization rate, the larger the protein system is, the higher the memory use of the GPU is. The performance of NVIDIA GTX580 is only half of the NVIDIAC2050. NVIDIA Tesla C2050 is can satisfy an even larger system simulation.

효율적인 MMORPG 분산 게임서버

다수 사용자용 온라인 게임은 온라인 서비스들 중에 중요한 부분을 차지하고 있다. 최근에는 네트워크를 통한 온라인 서비스를 이용하는 사용자들의 증가로 인해 서버에 부하가 가중되고 있다. 본 논문에서는 이와 같은 문제를 해결하는 2Layer-Cell 방식을 이용한 분산 MMORPG(Massively Multi-player Online Role Playing Game)게임 서버를 제안한다. 제안하는 방식은 많은 사용자들을 위한 MMORPG 분산 게임서버에 적합한 해결책을 제공한다. 성능평가는 제안하는 MMORPG 분산게임서버가 기존방법에 비해 메모리 사용량과 처리속도에서 성능이 우수함을 보인다. An important application domain for online services is an interactive, multi-player game. In recent, many increase of users that use on-line services through networks have caused a heavy load to the server. In this paper, we propose a MMORPG(Massively Multi-player Online Role Playing Game) distributed game server using flayer-Cell. Our method provides efficient solution of a MMORPG distributed game server for large numbers of users. It is shown through the experiments that our method outperforms existing methods in terms of memory utilization rate and processing speed.

An Associative Architecture of CMAC for Mobile Robot Motion Control

A new design scheme for the Cerebellar Model Articular Controller (CMAC) is presented in this article. The controller is designed with a content addressable memory (CAM) that replaces the function of the hash-coding method, which is adopted by the traditional CMAC and consumes much computation power in memory space mapping. Therefore, address mapping in the proposed CMAC is different from the hash-coding method. The CAM, which is capable of fast comparison, can immediately determine in parallel if there is any identical data in memory as the input data to be compared. If a match does not occur, the activated address is then stored in a vacant cell of the CAM indexed by a circular incremental pointer. Memory collision can be avoided unless the memory is fully occupied. In this way, the memory utilization rate can be improved to 100%, which is difficult to achieve using the conventional schemes. Meanwhile, control noise can be largely suppressed. In content addressing, the associated mask function, which is triggered when the CAM is full, can decrease the probability of collision and improve control performance. Simulation results of function approximation and truck backer-upper control indicate that the proposed CMAC is clearly superior to the conventional CMACs.