Response time

Cloud computing has emerged as a pivotal technology for managing and processing data, with a primary objective to offer efficient resource access while minimizing expenses. The allocation of resources is a critical aspect that can significantly reduce costs. This process necessitates the continuous assessment of the current status of each resource to design algorithms that optimize allocation and enhance overall system performance. Numerous algorithms have been developed to address the challenge of resource allocation, yet many fail to satisfy requirements of time efficiency and load balancing in cloud computing environments. This paper introduces a novel approach that classifies tasks according to their resource demands, employs a modified particle swarm optimization (PSO) algorithm, and incorporates load balancing strategies. The proposed method initially clusters tasks based on their resource requirements, subsequently utilizes the PSO algorithm to determine the best task-to-resource assignments, and finally implements a load balancing algorithm to reduce costs through balanced load distribution. The validity of the proposed method is tested and simulated using the Cloudsim tool. The simulation results indicate that the proposed method achieves lower average response time, waiting times, and energy consumption than existing baseline methods.

INTRODUCTION: Disasters, both natural or manmade, have been a serious threat to human life and property for many years. The shortest response time is certainly the most important factor in increasing the survival of victims in disasters. Considering the vital importance of time in relief operations, the aim of this study is to investigate the response time in various incidents and disasters by the aid workers of the RCS by the provinces of the country during the years 2012 to 2020. METHOD: This descriptive-quantitative study is cross-sectional and data was collected by the Rescue and Relief Organization of IRCS during the years 2012 to 2020 in all Emergency Operation Centers (EOC) of the country. The variables contain the year, the province, the time interval between the occurrence of the disasters and the notification by the RCS aid workers, the time interval between their notification and presence at the scene of the disaster. The median (first and third quartile) after removing the outliers was used to report these times by year and province in the three years of 2012, 2016 and 2020. A line chart was applied to examine the trend changes during the study period. FINDINGS: The results show that the maximum minutes between the occurrence of road accidents and the notification of RCS aid worker was not reported in any province in 2012, and in many provinces, this time are equal to 5 minute. The highest mean minutes were seen 7 in Gilan province and 6 in Zanjan, East Azerbaijan, Fars, Khuzestan and Yazd provinces in 2020. Also, this figure has decreased from 5 in 2012 to 4 minute in 2020 in the country. The highest mean of minutes in 2012 and 2020 belongs to Qom province with 11.5 and 13 minute. The median of these minute has increased from 5 in 2012 to 9 in 2020. In urban accidents, the median time of notification and presence of aid workers at the scene has decreased from 5 to 4 and increased from 5 to 7 minute, respectively. On the other hand, in mountain incidents, a decrease of one minute (from 10 to 9) and an increase from 20 to 36 minute can be seen in the mean of the times of notification of the accident and presence at the scene, respectively. CONCLUSION: According to the type of disasters and the climatic and geographical conditions of the provinces, the duration of disaster relief is different and determining the standard criteria for it depends on various factors. It seems that the time between the occurrence of a natural disaster and the notification and the time between the notification and the presence at the scene in road, urban and mountain incidents are at a favorable level in the RCS.

Background: With the continued advance in cyber threats, traditional network security systems offer little returns to organizations. AI has turned out to be a useful technology in improving network security because it proactively identifies and responds to threats in a short time. Objective: This article seeks to discuss the role played by AI self-defending mechanisms in autonomous network security given their effectiveness in threat detection, response time, and the overall harm that can be caused to networks by cyber criminals. Methods: Three separate studies were made, including conventional security systems, and analytically compared them with the AI-driven system across 100 different network environments. Machine learning (ML), deep learning (DL), and other forms of AI were applied to identify and counteract distinct threats like viruses, phishing, and even DDoS attacks. Detecting accuracy, response time and ability to mitigate attacks where among some of the other factors that were examined. Results: Automated threat intelligence systems have a 92% accuracy while legacy systems only have 78%. Mean response time was also decreasing by 65% from 45 seconds to 15 seconds. A significant increase to attack mitigation rates was noted with fifty percent effectiveness of the AI programs averting 85 percent of the threats in the first 30 seconds of identification. Conclusion: Autonomous threat response systems substantiate AI, which function as a radically superior replacement to conventional network security structures, minimizing threat response time and boosting the overall threat neutralization outcome. Incorporation of these types of secure mechanisms into contemporary security landscapes is important as a means of counteraction against new forms of cyber threats.