Performance degradation or system resource exhaustion can be attributed to inadequate computing resources as a result of software aging.In the real world,the workload of a web server varies with time,which will cause ...Performance degradation or system resource exhaustion can be attributed to inadequate computing resources as a result of software aging.In the real world,the workload of a web server varies with time,which will cause a nonlinear aging phenomenon.The nonlinear property often makes analysis and modelling difficult.Workload is one of the important factors influencing the speed of aging.This paper quantitatively analyzes the workload-aging relation and proposes a framework for aging control under varying workloads.In addition,this paper proposes an approach that employs prior information of workloads to accurately forecast incoming system exhaustion.The workload data are used as a threshold to divide the system resource usage data into multiple sections,while in each section the workload data can be treated as a constant.Each section is described by an individual autoregression(AR)model.Compared with other AR models,the proposed approach can forecast the aging process with a higher accuracy.展开更多
基金supported by the Natural Science Foundation of Tianjin(19JCYBJC15900)the National Key Research and Development Program of China(2018YFC0823701)+1 种基金an Open Fund of Tianjin Key Lab for Advanced Signal Processing(2017ASP-TJ04)a linkage grant of the Australian Research Council(LP160101691)
文摘Performance degradation or system resource exhaustion can be attributed to inadequate computing resources as a result of software aging.In the real world,the workload of a web server varies with time,which will cause a nonlinear aging phenomenon.The nonlinear property often makes analysis and modelling difficult.Workload is one of the important factors influencing the speed of aging.This paper quantitatively analyzes the workload-aging relation and proposes a framework for aging control under varying workloads.In addition,this paper proposes an approach that employs prior information of workloads to accurately forecast incoming system exhaustion.The workload data are used as a threshold to divide the system resource usage data into multiple sections,while in each section the workload data can be treated as a constant.Each section is described by an individual autoregression(AR)model.Compared with other AR models,the proposed approach can forecast the aging process with a higher accuracy.
