Abstract—Workload in Database Management System (DBMS) consists of huge amount of data and number of concurrent users who are executing different requests that require some resources. To manage these types of activities, organizations hire different database experts. There is versatility in workload due to the huge data size and different types of requests (workload). These factors contribute to some new challenges in the workload management. These challenges are identification of the workload and decision about the problem queries, identification of resource oriented and contention queries, accurate workload classification, optimal plan selection, prediction and adoption. In DBMS, where workload management and tuning is performed through if-then approach, unforeseen behavior of the workload cannot be handled and sometime leads to unpredictable state. In this research a prediction framework has been proposed called as workload queries performance Predictor. The predictor will predict the performance metrics (workload size, elapsed time, record accessed, record used, disk I/Os, memory required, message count and bytes) for queries in a given workload. We are improving efficiency and reducing search time when projection of query feature vector is performed over performance feature vector. The predictor will take help from the optimizer and store the information in database which saves the information as history for the future.
Index Terms—Workload management, prediction, performance metrics
Basit Raza, Abdul Mateen are with the Computer Science scholar at International Islamic University, Islamabad, Pakistan (e-mail: basitrazasaeed@gmail.com, abdulmateen@fuuastisb.edu.pk).
Mian Muhammad Awais is with the Department of Computer Science, Lahore University of Management Sciences, Lahore, Pakistan (e-mail: awais@lums.edu.pk).
Muhammad Sher is with the Department of Computer Science and Software Engineering, International Islamic University, Islamabad, Pakistan (e-mail: m.sher@iiu.edu.pk).
Cite: Basit Raza, Abdul Mateen, Muhammad Sher, and Mian Muhammad Awais, "Self-Prediction of Performance Metrics for the Database Management System Workload," International Journal of Computer Theory and Engineering vol. 4, no. 2, pp. 198-201, 2012.
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