摘要
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.
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 in-fluencing 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.
基金
supported by the Natural Science Foundation of Tianjin(19JCYBJC15900)
the National Key Research and Development Program of China(2018YFC0823701)
an Open Fund of Tianjin Key Lab for Advanced Signal Processing(2017ASP-TJ04)
a linkage grant of the Australian Research Council(LP160101691)
