Efficient Algorithms for Selecting Optimal Data Collection Locations in Business Process Management

The flexibility and dynamism of service-oriented architecture (SOA) makes it critical to monitor and manage services behaviors at runtime for performance assurance. In this paper, two efficient evidence channel selection (ECS) algorithms are designed to select service run-time data collection locations for business process management. The design of ESC algorithms are based on the classic facilities location problems: k-median and Set-Covering. The performance study shows that these ECS algorithms significantly reduce data collection cost and achieve better diagnosis correctness compared to random ECS selection.