Incorporating load factor into the scheduling of soft real-time transactions for main memory databases
Many real-time applications have very tight time constraints which couldn't be met by disk resident databases. For those applications, main memory database where entire database is stored in main memory is the proper choice. It has been shown that coarse-granule locking is better than fins-granule locking for main-memory databases. Coarse-granule locking makes it easy to extract data access patterns correctly from canned transactions of main memory real-time database systems. In this paper, we propose two real-time transaction scheduling algorithms - CCA-ALF (Cost Conscious Approach with Average Load Factor) and EDF-CR-ALF (Earliest Deadline First-Conditional Restart with ALF)- which use both static (e.g., deadline) and dynamic information (e.g., system load) for main memory databases by utilizing data access patterns of transactions. We compare the performance of those algorithms with CCA and EDF-HP which do not use system load information at all. Our simulations on main memory databases indicate that: i) CCA-ALF is better than EDF-HP, CCA, and EDF-CR-ALF in terms of miss percent and mean lateness, and ii) CCA-ALF adapts well to the changes in the system load. (C) 2000 Published by Elsevier Science Ltd.
main memory databases real-time database system soft deadline concurrency control