A Practice Guide of Software Aging Prediction in a Web Server Based on Machine Learning

In the past two decades, software aging has been studied by both academic and industry communities. Many scholars focused on analytical methods or time series to model software aging process. While machine learning has been shown as a very promising technique in application to forecast software state: normal or aging. In this paper, we proposed a method which can give practice guide to forecast software aging using machine learning algorithm. Firstly, we collected data from a running commercial web server and preprocessed these data. Secondly, feature selection algorithm was applied to find a subset of model parameters set. Thirdly, time series model was used to predict values of selected parameters in advance. Fourthly, some machine learning algorithms were used to model software aging process and to predict software aging. Fifthly, we used sensitivity analysis to analyze how heavily outcomes changed following input variables change. In the last, we applied our method to an IIS web server. Through analysis of the experiment results, we find that our proposed method can predict software aging in the early stage of system development life cycle.

Newsoftware rejuvenation strategy based on measurement and time

Operational software systems often experience an “aging” phenomenon, characterized by progressive performance degradation and a sudden hang/crash failure. Software rejuvenation is a proactive fault-tolerance strategy aimed to prevent unexpected outages due to aging. A new rejuvenation strategy based on measurement and time, and constructs a SRN model to evaluate this strategy. Numerical results show that this strategy outweighs either the purely measurementbased or the purely time-based strategy and can effectively improve system performance.

Predicting resource consumption in a web server using ARIMA model

Software aging is a phenomenon observed in a software application executing continuous- ly for a long period of time, where the state of software degrades and leads to performance degrada- tion, hang/crash failures or both. A technique named rejuvenation was proposed to counteract this problem. Rejuvenation in period is not a good idea, because the speed of software aging is not constant, but variable. The key to find an optimal timing to resist aging problem is how to analyze/fore- cast the resource consumption of aging system. An ARIMA model is applied to forecast resource con- sumption due to software aging in a running web server. First, order and parameters of ARIMA model need to be identified. Second, it needs to be checked whether the model satisfies stationarity and reversibility. Finally, ARIMA model is used to predict resource consumption. The experiment results indicate that ARIMA model can do better than ANN model and SVM model in the forecasts of available memory and heap memory.

A workload-based nonlinear approach for predicting available computing resources

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.

Reliability analysis of web server cluster systems based on proportional hazards model

An approach for web server cluster(WSC)reliability and degradation process analysis is proposed.The reliability process is modeled as a non-homogeneous Markov process(NHMH)composed of several non-homogeneous Poisson processes(NHPPs).The arrival rate of each NHPP corresponds to the system software failure rate which is expressed using Cox s proportional hazards model(PHM)in terms of the cumulative and instantaneous load of the software.The cumulative load refers to software cumulative execution time,and the instantaneous load denotes the rate that the users requests arrive at a server.The result of reliability analysis is a time-varying reliability and degradation process over the WSC lifetime.Finally,the evaluation experiment shows the effectiveness of the proposed approach.

Availability Analysis of Application Servers Using Software Rejuvenation and Virtualization

Demands on software reliability and availability have increased tremendously due to the nature of present day applications. We focus on the aspect of software for the high availability of application servers since the unavailability of servers more often originates from software faults rather than hardware faults. The software rejuvenation technique has been widely used to avoid the occurrence of unplanned failures, mainly due to the phenomena of software aging or caused by transient failures. In this paper, first we present a new way of using the virtual machine based software rejuvenation named VMSR to offer high availability for application server systems. Second we model a single physical server which is used to host multiple virtual machines （VMs） with the VMSR framework using stochastic modeling and evaluate it through both numerical analysis and SHARPE （Symbolic Hierarchical Automated Reliability and Performance Evaluator） tool simulation. This VMSR model is very general and can capture application server characteristics, failure behavior, and performability measures. Our results demonstrate that VMSR approach is a practical way to ensure uninterrupted availability and to optimize performance for aging applications.