Performance Tuning of IBM Sterling B2B Integrator for Large File Transfers: JVM, Thread Pool, and Database Optimization – Volume 9 Issue 4

International Journal of Computer Techniques
ISSN 2394-2231
Volume 9, Issue 4 | Published: July – August 2022
Author
Raghavendar Akuthota
Abstract
Large file transfers are a defining workload for enterprise data exchange systems, requiring both reliability and throughput. IBM Sterling B2B Integrator (SB2BI) is widely deployed in industries such as finance, healthcare, and logistics, but without deliberate tuning, large payloads strain its JVM environment, thread pools, and database schema. This paper examines performance optimization strategies within Sterling B2B Integrator for large file transfers, drawing on IBM documentation and independent analyses published between 2019 and 2022. The discussion covers JVM configuration, thread pool adjustments, and database-level optimizations, along with diagnostic and monitoring practices. The findings consolidate practical tuning methods with architectural insight, resulting in a structured framework that enterprises can adopt to maintain throughput and resilience.
Keywords
Sterling B2B Integrator, Performance Tuning, JVM, Thread Pool, Database Optimization, Large File TransfersConclusion
Large file transfers represent one of the most demanding workloads for IBM Sterling B2B Integrator. Without tuning, JVM garbage collection pauses, saturated thread pools, and overloaded databases reduce throughput and compromise service reliability. This study identified four main challenges: JVM memory and garbage collection bottlenecks, thread pool saturation, database connection and schema constraints, and limited diagnostic integration. For each challenge, it proposed actionable solutions, including JVM tuning with G1GC, thread pool reallocation, JDBC and schema optimizations, and integrated diagnostics. Beyond these solutions, organizations should adopt continuous practices: establishing benchmarks, embedding monitoring into governance frameworks, and investing in training. Together, these strategies form a structured framework for sustaining performance in SB2BI deployments. Future work could investigate automation through AI-assisted monitoring and adaptive tuning mechanisms, where JVM and thread configurations adjust dynamically based on workload predictions.
References
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