An adaptive value-based scheduling policy for multiprocessor real-time database systems

Abstract

In a real-time application, a transaction may be assigned a value to reflect the profit of completing the transaction before its deadline. Satisfying both goals of maximizing the totally obtained profit and minimizing the number of missed transactions at the same time is a challenge. In this paper, we present an adaptive realtime scheduling policy named Value-based Processor Allocation (VPA-k) for scheduling value-based transactions in a multiprocessor real-time database system. Using VPA-k policy, the transactions with higher values are given higher priorities to execute first, while at most k percentage of total processors are dynamically allocated to execute the urgent transactions. Through simulation experiments, VPA-k is shown to outpei$orm other scheduling policies substantially in both maximizing the totally obtained profit and minimizing the number of missed transactions under various system environments.

Research on an Adaptive Real-Time Scheduling Method of Dynamic Job-Shop Based on Reinforcement Learning

“…Aldarmi, SA et al [32] proposed value-based scheduling algorithms to improve the performance under normal load in the scheduling system and to reduce the performance degradation of the system under overload to a certain extent. Tseng, SM et al [33] present an adaptive real-time scheduling policy for scheduling value-based transactions in a multiprocessor real-time database system. Prasad, D [34] proposed a valued-based scheduling method to select the optimal service operation autonomously.…”