Sustainable Image Processing for Digital News Platforms: Evaluating Go Concurrency Models for Efficient Media Workloads

The rapid expansion of visual content in digital news media has introduced significant computational challenges for image processing systems. This study evaluates the effectiveness of Go's concurrency mechanisms to address these bottlenecks, specifically through the implementation of a Worker Pool architecture. To validate its capability, the proposed model was benchmarked against a standard sequential process and a naive unbounded concurrency approach using the IMAGINE dataset under tiered load scenarios. The analysis focuses on execution time, memory stability, and CPU efficiency. The results demonstrate that Go's concurrency implementation using the Worker Pool pattern successfully maximizes system throughput, achieving a 3.74x speedup over the sequential baseline. Furthermore, unlike the naive approach which suffered from critical Out-of-Memory (OOM) failures, the Worker Pool maintained higher stability (CV 0.34%) and controlled resource usage. This study confirms that Go's concurrency, when implemented with a bounded strategy, is a highly effective solution for high-performance news portal systems.