Adaptive Co-Scheduling for Periodic Update and User Transactions in Real-Time Database Systems

In this paper, we study the co-scheduling problems of periodic user transactions and update transactions in real-time database systems for surveillance of critical events. To perform the surveillance functions effectively, it is important to meet the deadlines of all the periodic user transactions and at the same time to maintain the quality of the real-time data objects accessed by them. Unfortunately, these two goals are conflicting and difficult to be achieved at the same time. To address the co-scheduling problem, we propose a real-time co-scheduling algorithm, called Adaptive Earliest Deadline First Co-Scheduling (AEDF-Co) in which we adopt a dynamic scheduling to adaptively schedule the update and user jobs based on their deadlines. The performance goal of AEDF-Co is that for given sets of periodic user and update transactions, we determine a schedule such that the deadlines of all the user transactions can be satisfied and at the same time the quality of the real-time data objects are maximized. Extensive simulation experiments are performed to evaluate the performance of AEDF-Co. The results show that adaptively adjusting the release times of update jobs and schedule the update and user jobs using a dynamic scheduling algorithm, AEDF-Co can effectively in achieving the performance goal and maximize the system performance.