Mobile Edge Computing(MEC)is proposed to solve the needs of Inter-net of Things(IoT)users for high resource utilization,high reliability and low latency of service requests.However,the backup virtual machine is idle w...Mobile Edge Computing(MEC)is proposed to solve the needs of Inter-net of Things(IoT)users for high resource utilization,high reliability and low latency of service requests.However,the backup virtual machine is idle when its primary virtual machine is running normally,which will waste resources.Overbooking the backup virtual machine under the above circumstances can effectively improve resource utilization.First,these virtual machines are deployed into slots randomly,and then some tasks with cooperative relationship are off-loaded to virtual machines for processing.Different deployment locations have different resource utilization and average service response time.We want tofind a balanced solution that minimizes the average service response time of the IoT application while maximizing resource utilization.In this paper,we propose a task scheduler and exploit a Task Deployment Algorithm(TDA)to obtain an optimal virtual machine deployment scheme.Finally,the simulation results show that the TDA can significantly increase the resource utilization of the system,while redu-cing the average service response time of the application by comparing TDA with the other two classical methods.The experimental results confirm that the perfor-mance of TDA is better than that of other two methods.展开更多
While emergency medical service (EMS) response time (ERT) is a major factor associated with the survival of patients with cardiovascular disease (CVD), relatively few studies have explored the factors associated with ...While emergency medical service (EMS) response time (ERT) is a major factor associated with the survival of patients with cardiovascular disease (CVD), relatively few studies have explored the factors associated with ERT. This study aimed to assess the current status of ERT and to identify the factors affecting ERT in patients with CVD in China. Between January 1, 2011 and December 31, 2015, EMS responses to CVD incidents in Guangzhou, China, were examined. The primary outcome was ERT, defined as the time from receipt of an emergency call to the arrival of paramedics on the scene. Factors associated with ERT were evaluated by multivariable logistic regression. A total of 44 383 CVD incidents were analysed. The median ERT was 12.58 min (interquartile range=9.98-15.67). Among the risk factors, distance (OR=13.73, 95% CI=11.76- 16.04), level of hospital (OR=1.57, 95% CI=1.40-1.75), and site of the incident (OR=1.53, 95% CI=1.38-1.69) were the top three significant factors affecting the ERT. Our results suggest that greater attention should be given to factors affecting the ERT. It is essential to make continuous efforts to promote the development of effective interventions to reduce the response time.展开更多
文摘较低的网络服务响应时间对提升用户体验至关重要.以搜索引擎这一典型的网络服务场景为例,服务提供商应确保网络服务(搜索)响应时间在1 s以内.在实践中,服务响应时间会受到用户浏览器、运营商、页面加载方式等诸多服务属性的影响.为了进行针对性的优化,服务提供商需要找出使服务响应时间过长的规则,即一些属性的组合.然而现有研究工作遇到了3方面挑战:1)搜索日志数据量大;2)搜索日志数据分布不平衡;3)要求泛化度高的规则.因此设计了Miner(multi-dimensional extraction of rules),一种新型服务响应时间异常诊断框架.Miner使用自步采样机制应对第1个挑战和第2个挑战.针对第3个挑战,Miner使用Corels算法挖掘出泛化率高且召回率高的规则.使用2家国内顶级搜索引擎服务提供商的响应时间日志数据评估了Miner性能,结果显示Miner的泛化率和召回率均高于现有方法,并证明了Miner挖掘出的规则可被运维人员采纳并做针对性的优化.
基金supported by the National Natural Science Foundation of China under Grant No.62173126the National Natural Science Joint Fund project under Grant No.U1804162+2 种基金the Key Science and Technology Research Project of Henan Province under Grant No.222102210047,222102210200 and 222102320349the Key Scientific Research Project Plan of Henan Province Colleges and Universities under Grant No.22A520011 and 23A510018the Key Science and Technology Research Project of Anyang City under Grant No.2021C01GX017.
文摘Mobile Edge Computing(MEC)is proposed to solve the needs of Inter-net of Things(IoT)users for high resource utilization,high reliability and low latency of service requests.However,the backup virtual machine is idle when its primary virtual machine is running normally,which will waste resources.Overbooking the backup virtual machine under the above circumstances can effectively improve resource utilization.First,these virtual machines are deployed into slots randomly,and then some tasks with cooperative relationship are off-loaded to virtual machines for processing.Different deployment locations have different resource utilization and average service response time.We want tofind a balanced solution that minimizes the average service response time of the IoT application while maximizing resource utilization.In this paper,we propose a task scheduler and exploit a Task Deployment Algorithm(TDA)to obtain an optimal virtual machine deployment scheme.Finally,the simulation results show that the TDA can significantly increase the resource utilization of the system,while redu-cing the average service response time of the application by comparing TDA with the other two classical methods.The experimental results confirm that the perfor-mance of TDA is better than that of other two methods.
文摘While emergency medical service (EMS) response time (ERT) is a major factor associated with the survival of patients with cardiovascular disease (CVD), relatively few studies have explored the factors associated with ERT. This study aimed to assess the current status of ERT and to identify the factors affecting ERT in patients with CVD in China. Between January 1, 2011 and December 31, 2015, EMS responses to CVD incidents in Guangzhou, China, were examined. The primary outcome was ERT, defined as the time from receipt of an emergency call to the arrival of paramedics on the scene. Factors associated with ERT were evaluated by multivariable logistic regression. A total of 44 383 CVD incidents were analysed. The median ERT was 12.58 min (interquartile range=9.98-15.67). Among the risk factors, distance (OR=13.73, 95% CI=11.76- 16.04), level of hospital (OR=1.57, 95% CI=1.40-1.75), and site of the incident (OR=1.53, 95% CI=1.38-1.69) were the top three significant factors affecting the ERT. Our results suggest that greater attention should be given to factors affecting the ERT. It is essential to make continuous efforts to promote the development of effective interventions to reduce the response time.
