Date of Award

Fall 2021

Document Type

Open Access Dissertation


Computer Science and Engineering

First Advisor

Csilla Farkas


Concurrent database transactions within a web service environment can cause a variety of problems without the proper concurrency control mechanisms in place. A few of these problems involve data integrity issues, deadlock, and efficiency issues. Even with today’s industry standard solutions to these problems, they have taken a reactive approach rather than proactively preventing these problems from happening. We deliver a solution, based on prediction-based scheduling to ensure consistency while keeping execution time the same or faster than current industry solutions. The first part of this solution involves prototyping and formally proving a prediction-based scheduler.

The prediction-based scheduler leverages a prediction-based metric that promotes transactions with a high performance metric. This performance metric is based on the transaction’s likelihood to commit and its efficiency within the system. We can then predict the outcome of the transaction based on the metric and apply customized lock behaviors to address consistency issues in current web service environments. We have formally proven that the solution will increase consistency among web service transactions without a performance degradation. The simulation was developed using a multi-threaded approach to simulate concurrent transactions. Our empirical results show that the solution performs similarly to industry solutions with the added benefit of ensured consistency. This work has been published in IEEE Transactions on Services Computing.

The second part of the solution involves building the prediction-based metric mentioned previously. In the initial solution we assumed that the categorization of transactions is provided in advance. To incorporate the ability to dynamically adjust transaction reputations we extended the four category solution to a dynamic reputation score. The attributes used in the reputation score are system abort ranking, user abort ranking, efficiency ranking, and commit ranking. With these four attributes we were able to establish a dynamic dominance structure that allowed for a transaction to promote or demote itself based on its performance within the system. This work has been submitted to ACM Transactions on Database Systems and awaiting review.

Both phases provide a complete solution of prediction-based transaction schedul-ing that provides dynamic categorization no matter the transactional environment.

Future work of this system would involve extending the prediction-based solution to a multi-level secure database with an added dimension. Our goal is to increase concurrency of multi-level secure transactions without creating a covert channel. The dimension provides a security classification in addition to attributes for dynamic reputation that allows for transactions to establish dominance. Our reputation score would provide a cover story for timing differences of transactions of different security levels to allow for a more robust scheduling algorithm. This would allow for high security transactions to gain priority over low security transactions without creating a covert timing channel.