We propose an Edge-write architecture which performs eager update propagation for update requests for the corresponding secondary server, whereas it lazily propagates updates from other secondary servers. Our architecture resolves consistency problems caused by read/update decoupling in the conventional lazy update propagation-based system. It also improves overall scalability by alleviating the performance bottleneck at the primary server in compensation for increased but bounded response time. Such relaxed consistency management enables a read request to choose whether to read the replicated data immediately or to refresh it. We use the age of a local data copy as the freshness factor so that a secondary server can make a decision for freshness control independently. As a result, our freshness-controlled edge-write architecture benefits by adjusting a tradeoff between the response time and the correctness of data.

To speed up on-line analytical processing (OLAP), data warehouse, which is usually derived from operational databases, is introduced. When the operational databases happen to change, the data warehouse gets stale. To maintain the freshness of data warehouse, operational database changes need to be frequently and concurrently propagated into the data warehouse. However, if several update transactions are allowed to execute concurrently without an appropriate concurrency control, data inconsistency between data warehouse and operational databases could arise due to incorrect propagation of changes on the operational databases into the data warehouse. In this paper, we propose a new concurrency control scheme, which could execute a number of update transactions in a consistent way. Whenever an update transaction tries to update a data that is being used by OLAP transactions, our scheme allows the update transaction to create a new version of the data. To investigate the applicable areas of our scheme, its performance is evaluated by means of simulation approach. Our experimental results show that the proposed scheme enables OLAP transactions to continuously read a very fresh data without wasting a lot of time to find out an appropriate version of the data from the version pool.