While the expert-based approach in buffer management, such as the EEvA algorithms, offers several advantages, there are also potential limitations and drawbacks compared to other machine learning-based strategies.
One limitation of the expert-based approach is the need for manual design and selection of experts. Designing experts that accurately capture the relevant patterns in the data access can be a challenging and time-consuming task. In contrast, machine learning-based strategies can automatically learn patterns and adapt to changing data access behaviors without the need for manual intervention.
Another limitation is the scalability of the expert-based approach. As the complexity and size of the database system increase, managing a large number of experts and updating their weights can become computationally expensive. Machine learning-based strategies, on the other hand, can leverage algorithms that scale well with large datasets and complex systems.
Additionally, the expert-based approach may struggle to adapt to dynamic and evolving data access patterns. If the experts are not designed to capture all possible variations in the workload, the performance of the algorithm may degrade over time. Machine learning-based strategies, especially those based on reinforcement learning, can continuously learn and adapt to changing patterns in the data access.
Despite these limitations, the expert-based approach offers transparency and interpretability in the decision-making process. Experts provide insights into the reasoning behind the algorithm's decisions, making it easier to understand and debug compared to black-box machine learning models.