Point Estimator Log Tracker for Cloud Monitoring

A cloud management system depends mainly on monitoring systems to perform the right management actions, especially in a high change configuration parameters environment. Monitoring system must provide needed information to cloud manager to describe cloud dynamic state by reading cloud-generated logs and sending them to cloud manager. Log updates should be accurate, instantaneous and sent with minimum time delay. Data sources vary from low to high level of cloud infrastructure resources, or it can be generated from workload demands. Logs are used to discover cloud system status, which is input for future actions in cloud management resources orchestration. A good monitoring system must reduce number of communication transactions with cloud manager and keep a fresh and consistent log update. This work introduces a new method of logs tracking and sampling that can achieve lower logging transactions and cloud system reconfiguration actions, under several types of workload and log data sources. The proposed method, Point Estimator (PE) log tracker, can dynamically adapt to the type of workload providing accurate fresh logs values to cloud manager with minimum number of data transactions.