Maximum CPU Utilization Research Articles

Design and implementation of student management system of integrated programmable device programming system

With the continuous growth and development of society, the reform of higher education is gradually taking shape, and colleges and universities are taking more and more responsibilities in promoting education. The main element of university management is the student management system, which is very important to the development of the university. Under the objective environment of colleges and universities seeking to expand the scale of running schools and rapid economic and social development, the current student management system has been unable to meet the various needs of contemporary students. The integration of programmable device programming systems offers a student management system distinct advantages in terms of reliability, flexibility, and user-friendly operation. This study focuses on developing an effective and affordable student management system by incorporating a programmable device programming system. To evaluate the system's performance, this paper suggests the utilization of a BP neural network, renowned for its high nonlinear approximation capabilities and effectiveness in handling complex nonlinear functional relationships. The experimental findings highlight a significant contribution, demonstrating that the system achieved a throughput of 180 times per second, with a maximum CPU utilization of 99%. Notably, the system's stability exceeded 95%, contrasting sharply with the traditional student management system's stability at a mere 65%. These results underscore the substantial contribution of the proposed system, showcasing its enhanced stability compared to conventional student management systems.

Edge-based individualized anomaly detection in large-scale distributed solar farms

Power output from large-scale solar farms is often plagued by anomalies that can adversely impact grid integration. This paper presents an anomaly detection system that used Siamese-twin neural networks for anomaly detection on edge devices in a solar farm. The model achieved an F1-score of 0.88 and was evaluated using two multi-threading schemes on a Raspberry PI, Nvidia Nano and Google Coral. A single analytics edge device could service 512 solar panels at 1 Hz. The best hardware platform was Nvidia’s Nano using a TensorFlow Lite model consuming about 35 Wh over 12 h, and with maximum CPU utilization not exceeding 60%.

Computational comparison of common event-based differential splicing tools: practical considerations for laboratory researchers

BackgroundComputational tools analyzing RNA-sequencing data have boosted alternative splicing research by identifying and assessing differentially spliced genes. However, common alternative splicing analysis tools differ substantially in their statistical analyses and general performance. This report compares the computational performance (CPU utilization and RAM usage) of three event-level splicing tools; rMATS, MISO, and SUPPA2. Additionally, concordance between tool outputs was investigated.ResultsLog-linear relations were found between job times and dataset size in all splicing tools and all virtual machine (VM) configurations. MISO had the highest job times for all analyses, irrespective of VM size, while MISO analyses also exceeded maximum CPU utilization on all VM sizes. rMATS and SUPPA2 load averages were relatively low in both size and replicate comparisons, not nearing maximum CPU utilization in the VM simulating the lowest computational power (D2 VM). RAM usage in rMATS and SUPPA2 did not exceed 20% of maximum RAM in both size and replicate comparisons while MISO reached maximum RAM usage in D2 VM analyses for input size. Correlation coefficients of differential splicing analyses showed high correlation (β > 80%) between different tool outputs with the exception of comparisons of retained intron (RI) events between rMATS/MISO and rMATS/SUPPA2 (β < 60%).ConclusionsPrior to RNA-seq analyses, users should consider job time, amount of replicates and splice event type of interest to determine the optimal alternative splicing tool. In general, rMATS is superior to both MISO and SUPPA2 in computational performance. Analysis outputs show high concordance between tools, with the exception of RI events.

Quantitative Server Sizing Model for Performance Satisfaction in Secure U2L Migration

There are many challenges in measuring capacity size using units such as transaction per minute (TPM) and operation per second (OPS) for all server hardware, which are increasingly obsolete due to the shortening of hardware life cycle and the advent of microprocessors. Thus, the results of accredited performance measurements are used to measure these standards, which are then used as references in the capacity sizing method employed by industries. Generally, in industries, the capacity sizing of a web application server is defined by OPS, for which no clear transition criterion exists for calculating tpmC using an empirical verification method. In particular, considering secure Unix to Linux (U2L) x86-based servers migration, there are no methods to compare and verify the max-jOPS value of Standard Performance Evaluation Corp., which is an industry-recognized performance measurement standard, to Unix-based benchmark tpmC. In this study, scenario-based U2L migration was empirically verified by analyzing and comparing pre-to-post with the interpretation of Census statistics system log data, which was conducted on approximately 1.7M households over 21 days with 25,288 maximum concurrent users. Thus, we present the correlation through pre-to-post comparison and analysis of Unix-to-Linux for each Census statistics system log data by measuring the maximum CPU utilization as U2L migration between heterogeneous CPUs. These are applied to OPS by TPC-C proportional equation and quantified as derived conversion ratio of OPS to max-jOPS. As a result, we formulated and normalized an arithmetic expression, which is derived the CPU core conversion ratio of 0.165 as facile in U2L migration from a Unix-based legacy platform to a x86-based server. Therefore, we propose a new server sizing model for secure U2L migration between heterogeneous CPUs architecture, which results in an average of 14.3% improvement in data processing time.

Sunflower Whale Optimization Algorithm for Resource Allocation Strategy in Cloud Computing Platform

Cloud computing environment supply the computing resources based on the demand of cloud user requirements. It builds the resource allocation model through distributed computing and virtualization to emphasize the scalability of cloud services. However, to manage the demand of user creates a complex issue in the on-demand resource allocation framework. Therefore, an effective optimization algorithm named Sunflower Whale Optimization Algorithm (SFWOA) is proposed to solve the issues in the resource allocation model. The concept of virtualization helps to execute the tasks based on the availability of resources and reduces the response time. The tasks are allocated to the virtual machine in a distributed manner to balance the workload in cloud. The proposed SFWOA uses the hunting strategy and the foraging behavior of humpback whale along with the peculiar behavior of sunflower to achieve the effective resource allocation. The performance enhancement of the proposed SFWOA is revealed through the performance measures such that the proposed method attained a maximum resource utilization of 0.942 using 20 virtual machines, maximum memory utilization of 0.215, and maximum CPU utilization of 0.269 using 15 virtual machines, and minimum skewness of 0.001 using 25 virtual machines.

Hybridization of firefly and Improved Multi-Objective Particle Swarm Optimization algorithm for energy efficient load balancing in Cloud Computing environments

Load balancing, in Cloud Computing (CC) environment, is defined as the method of splitting workloads and computing properties. It enables the enterprises to manage workload demands or application demands by distributing the resources among computers, networks or servers. In this research article, a new load balancing algorithm is proposed as a hybrid of firefly and Improved Multi-Objective Particle Swarm Optimization (IMPSO) technique, abbreviated as FIMPSO. This technique deploys Firefly (FF) algorithm to minimize the search space where as the IMPSO technique is implemented to identify the enhanced response. The IMPSO algorithm works by selecting the global best (gbest) particle with a small distance of point to a line. With the application of minimum distance from a point to a line, the gbest particle candidates could be elected. The proposed FIMPSO algorithm achieved effective average load for making and enhanced the essential measures like proper resource usage and response time of the tasks. The simulation outcome showed that the proposed FIMPSO model exhibited an effective performance when compared with other methods. From the simulation outcome, it is understood that the FIMPSO algorithm yielded an effective result with the least average response time of 13.58ms, maximum CPU utilization of 98%, memory utilization of 93%, reliability of 67% and throughput of 72% along with a make span of 148, which was superior to all the other compared methods.

RETRACTED ARTICLE: Modified adaptive neuro fuzzy inference system based load balancing for virtual machine with security in cloud computing environment

In a heterogeneous environment, computation over internet is provided by a popular paradigm called cloud computing. In a cloud heterogeneous environment, service providing has various difficulties. Based on service type, difficulties differ. On cloud server, high load is produced by huge amount of request from various users for accessing various applications. Security and balancing of load are major concerns. In cloud environment, NP-hard optimization problem corresponds to load balancing. Dynamic load balancing is handled by various methods. They are designed for enhancing workload distribution process between nodes. Overload avoidance, minimization of average response time, data processing time and optimum utilization of resources are the major aim of those methods. An optimal load balancing technique should improve the turnaround time and maximum CPU utilization. Because of its opaqueness nature of cloud, security is a biggest challenge. According to Forbes, with introduction of General Data Protection Regulation security in cloud continue to be an issue with cloud computing. In the existing system, a fuzzy based hybrid load balancing algorithm is utilized and the results provided are not satisfactory. There are opportunities for improving CPU utilization and turnaround time and in terms of security. In this proposed research work, dynamic load balancing in a heterogeneous environment is handled by Modified Adaptive Neuro Fuzzy Inference System (MANFIS). Parameters of MANFIS are optimized by introducing Fire-fly Algorithm. Security is imposed on user authentication by using the Enhanced Elliptic Curve Cryptography. This is a password-less mechanism to authenticate users. The proposed work attains satisfactory results by proper resource utilization. An experimental result shows that proposed work exhibits better performance by improving the turnaround time and maximizing the CPU utilization and providing secured access to data.

An Adaptive Emergency First Intelligent Scheduling Algorithm for Efficient Task Management and Scheduling in Hybrid of Hard Real-Time and Soft Real-Time Embedded IoT Systems

Industrial revolution is advancing, and the augmented role of autonomous technology and embedded Internet of Things (IoT) systems is at its vanguard. In autonomous technology, real-time systems and real-time computing are of core importance. It is crucial for embedded IoT devices to respond in real-time; along with fulfilling all the constraints. Many combinations for existing approaches have been proposed with different trade-offs between the resources constraints and tasks dropping rate. Hence, it highlights the significance of a task scheduler which not only takes care of complex nature task input; but also maximizes the CPU throughput. A complex nature task input is when combinations of hard real-time tasks and soft real-time tasks, with different priorities and urgency measures, arrive at the scheduler. In this work, we propose a custom tailored adaptive and intelligent scheduling algorithm for the efficient execution and management of hard and soft real time tasks in embedded IoT systems. The proposed scheduling algorithm aims to distribute the CPU resources fairly to the possibly starving, in overloaded cases, soft real-time tasks while focusing on the execution of high priority hard real-time tasks as its primary objective. The proposal is achieved with the help of two intelligent measures; Urgency Measure (UM) and Failure Measure (FM). The proposed mechanism reduces the rate of tasks missed and the rate of tasks starved, by utilizing the free CPU units for maximum CPU utilization and quick response times. We have performed comparisons of our proposed scheme based on performance metrics as percentage of task instances missed, number of tasks with missed instances, and tasks starvation rate to evaluate the CPU utilization. We first compare our proposed approach with multiple traditional and combined scheduling approaches, and then we evaluate the effect of intelligent modules by comparing the intelligent FEF with non-intelligent FEF. We also evaluate the proposed algorithm in contrast to the most commonly-used hybrid scheduling scheme in embedded systems. The results show that the proposed algorithm out performs the other algorithms, by significantly reducing the task starvation rate and increasing the CPU utilization.

Crowdsource Based Vehicle Tracking System

The transport of developing countries faces lots of problems including heavy traffic jams, the uncertainty of waiting time, increased fuel consumption and the risk of road accidents due to unmanaged traffic flow. These problems are being solved by using Intelligent Transportation System (ITS). In this paper, In this paper, we propose the tracking of vehicles which uses crowdsourced positioning data obtained from GPS of smartphones. Traditional transit tracking is costly and resources limited, thus instead installing a tracking device in each vehicle, the location of each vehicle is tracked by user’s smartphone. The positioning data from GPS of smartphone is used as an input. We have developed an algorithm, that will detect and recognize the specific vehicle based on crowdsourced data. Using this the users waiting for their desired vehicle at various routes can track vehicle’s current location and estimated the time the vehicle will take to reach their location. In this research paper, we have analyzed the usability and reliability of crowdsourcing tracking data in terms of usage of battery charge, power consumption, CPU utilization and response time. It can be seen in the results that proposed work consumes power in between the range of 412 mW to 502 mW only. Whereas, maximum CPU utilization goes up to 0.3%. In the end, the battery life of the proposed system is compared with existing work and it shows proposed system have more battery life than existing systems. As a case study, we have applied our algorithm for tracking of university buses.

Optimizing energy consumption for a performance-aware cloud data center in the public sector

In general, cloud environments are based on the pay-per-use model, whereby clients pay for the information resources provided by cloud service providers. Users rent and use resources as needed while avoiding the high costs of large-scale resource acquisitions, and providers maximize their profits by managing information resources at a minimum cost while upholding service-level agreements. As the number of resources gradually increases, power supply shortages may arise. This study focuses on the fact that the CPU utilization rate of the server running in the data center is less than 30% and idle servers running only the OS consume more than half of the power consumed by hosts running with maximum CPU utilization and speed. Therefore, this study proposes an approach to enhance data center efficiency through improved management of energy consumption. We present a method to minimize energy consumption while processing the same workload, i.e., ultimately reducing the energy consumed by operating servers.Energy consumption and SLA violation rate were used as evaluation metrics of the optimization model to meet the minimum performance target.

Energy-Efficient Thermal-Aware Scheduling for RT Tasks Using TCPN

This work leverages TCPNs to design an energy-efficient, thermal-aware real-time scheduler for a multiprocessor system that normally runs in a low state energy at maximum system utilization but its capable of increasing the clock frequency to serve aperiodic tasks, optimizing energy, and honoring temporal and thermal constraints. An off-line stage computes the minimum frequency required to run the periodic tasks at maximum CPU utilization, the proportion of each task’s job to be run on each CPU, the maximum clock frequency that keeps temperature under a limit, and the available cycles (slack) with respect to the system with minimum frequency. Then, a Zero-Laxity online scheduler dispatches the periodic tasks according to the offline calculation. Upon the arrival of aperiodic tasks, it increases clock frequency in such a way that all periodic and aperiodic tasks are properly executed. Thermal and temporal requirements are always guaranteed, and energy consumption is minimized.

Design and simulation of a parallel adaptive arbiter for maximum CPU utilization using multi-core processors

Parallelization of task is considered to be a huge challenge for future extreme-scale computing system. Sophisticated parallel computing system necessitates solving the bus contention in a most efficient manner with high computation rate. The major challenge to deal with is the achievement of high CPU core usage through increased task parallelism by keeping moderate bus bandwidth allocation. In order to tackle the aforesaid problems, a novel arbitration technique, known as Parallel Adaptive Arbitration (PAA) has been proposed for the masters designed according to the traffic behaviour of the data flow. These masters are implemented using a synthetic benchmark program that measures sustainable memory bandwidth and the corresponding computational rate. The proposed arbitration technique is a strong case in favour of fair bandwidth optimization and high CPU utilization, as it consumes the processor cores up to 77% through high degree of task parallelization and also reduces bandwidth fluctuation. Some of the works published so far in this area are reviewed, classified according to their objectives and presented in an organised manner with a conclusion.

An Intelligent Adaptive Arbiter for Maximum CPU Utilization, Fair Bandwidth Allocation and Low Latency

Rapid advancement in the world of microprocessor necessitates solving the bus contention in a most efficient manner. The major challenge is to reduce the latency of the system and to achieve a fair bandwidth allocation using maximum CPU utilization. Combination of Adaptive Arbitration (AA) algorithm and the processors according to their traffic behavior can be one of the feasible options to tackle the aforesaid problems due to its fair bandwidth allotment and low system latency. This article provides a comprehensive picture of the global scenario of AA algorithm for fair bandwidth allocation so as to enable researchers to decide the direction of further investigation. Some of the works published so far in this area are reviewed, classified according to their objectives, and presented in an organized manner with general conclusion.