Serge Bourbonnais

Towards an information infrastructure for the grid

In this paper we present our vision of an information infrastructure for grid computing, which is based on a service-oriented architecture. The infrastructure supports a virtualized view of the computing and data resources, is autonomic (driven by policies) in order to meet application goals for quality of service, and is compatible with the standards being developed in the technical community. We describe how we are implementing this vision in IBM today and how we expect the implementation to evolve in the future.

Capturing global transactions from multiple recovery log files in a partitioned database system

DB2 DataPropagator is one of the IBMs solutions for asynchronous replication of relational data by two separate programs Capture and Apply. The Capture program captures changes made to source data from recovery log files into staging tables, while the Apply program applies the changes from the staging tables to target data. Currently the Capture program only supports capturing changes made by local transactions in a single database log file. With the increasing deployment of partitioned database systems in OLTP environments there is a need to replicate the operational data from the partitioned systems. This paper introduces a system called CaptureEEE which extends the Capture program to capture global transactions executed on partitioned databases supported by DB2 Enterprise-Extended Edition. The architecture and the components of CaptureEEE are presented. The algorithm for merging log entries from multiple recovery log files is discussed in detail.

Inter-Data-Center Large-Scale Database Replication Optimization – A Workload Driven Partitioning Approach

Inter-data-center asynchronous middleware replication between active-active databases has become essential for achieving continuous business availability. Near real-time replication latency is expected despite intermittent peaks in transaction volumes. Database tables are divided for replication across multiple parallel replication consistency groups; each having a maximum throughput capacity, but doing so can break transaction integrity. It is often not known which tables can be updated by a common transaction. Independent replication also requires balancing resource utilization and latency objectives. Our work provides a method to optimize replication latencies, while minimizing transaction splits among a minimum of parallel replication consistency groups. We present a two-staged approach: a log-based workload discovery and analysis and a history-based database partitioning. The experimental results from a real banking batch workload and a benchmark OLTP workload demonstrate the effectiveness of our solution even for partitioning 1000s of database tables for very large workloads.

Optimizing Inter-data-center Large-Scale Database Parallel Replication with Workload-Driven Partitioning

Geographically distributed data centers are deployed for non-stop business operations by many enterprises. In case of disastrous events, ongoing workloads must be failed over from the current data center to another active one within just a few seconds to achieve continuous service availability. Software-based parallel database replication techniques are designed to meet very high throughput with near-real-time latency. Understanding workload characteristics is one of the key factors for improving replication performance. In this paper, we propose a workload-driven method to optimize database replication latency and minimize transaction splits with a minimum of parallel replication consistency groups. Our two-phased approach includes 1 a log-based mechanism for workload pattern discovery; 2 a history-based algorithm on pattern analysis, database partitioning and partition adjustment. The experimental results from a real banking batch workload and a benchmark OLTP workload demonstrate the effectiveness of the solution even for partitioning 1000i¾?s of database tables in very large workloads. Finally, the algorithm to automate the cyclic flow of workload profile capturing and partitioning readjustment is developed and verified.