Real-Time Transaction Scheduling: A Framework for Synthesizing Static and Dynamic Factors

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Real-time databases are poised to be an important component of complex embedded real-time systems. In real-time databases (as opposed to real-time systems), transactions must satisfy the ACID properties in addition to satisfying the timing constraints specified for each transaction (or task). Although several approaches have been proposed to combine real-time scheduling and database concurrency control methods, to the best of our knowledge, none of them provide a framework for taking into account the dynamic cost associated with aborts, rollbacks, and restarts of transactions.

In this paper, we propose a framework in which both static and dynamic costs of transactions can be taken into account. Specifically, we present: i) a method for pre-analyzing transactions based on the notion of branch-points for data accessed up to a branch point and predicting expected data access to be incurred for completing the transaction, ii) a formulation of cost that includes static and dynamic factors for prioritizing transactions, iii) a scheduling algorithm which uses the above two, and iv) simulation of the algorithm for several operating conditions and workload.

Our dynamic priority assignment policy (termed the cost conscious approach or CCA) adapts well to fluctuations in the system load without causing excessive numbers of transaction restarts. Our simulations indicate that i) CCA performs better than the EDF-HP algorithm for both soft and firm deadlines, ii) CCA is more fair than EDF-HP, iii) CCA is better than EDF-CR for soft deadline, even though CCA requires and uses less information, and iv) CCA is especially good for disk-resident data.

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