Feedback control loop design for workload change detection in self-tuning NoSQL wide column stores

•We design a feedback control loop for workload monitoring and analysis in NoSQL database.•The loop describes a design pattern for the self-tuning feature and detects workload changes.•A self-adaptive & dynamic clustering process for workload events is proposed.•An approach for workload change detection using reconfigurable CPN model is proposed.•The results demonstrate the effectiveness and good scalability of the proposed approach. Database management systems are the main part of information systems that the size and complexity of these systems are increased in recent years. Due to the growing complexity of DBMSs, database administrators (DBAs) face increasingly more problems and challenges, and so managing these systems are difficult and laborious. More over the main part of the total cost of ownership includes the cost of expert database administrator who can manage these large and complicated systems. Autonomic database, by providing self-management functionality, leads to a reduction in the total cost of ownership for the database system. Self-management decisions such as automated schema database tuning are dependent on the database workload. Therefore, one of the important issues in realizing the database automated tuning is workload monitoring and analysis for changes detection and schema re-tuning with this changes. In this paper, a feedback control loop is designed for continuous monitoring and light-weight workload analysis in NoSQL wide column stores. This loop describes a design pattern for the self-tuning feature and it is used to detect workload changes that are necessary for the automated schema database re-tuning. Our concept is based on workload model construction using reconfigurable colored petri-net model. The results of the experiments show the effectiveness of the proposed approach in discovering significant workload changes.