A data engineering team schedules an AWS Glue ETL job to run every 15 minutes to process incoming files. During heavy traffic periods, upstream events sometimes trigger the job while a previous run is still executing. The team notices that some job runs fail immediately with a 'ConcurrentRunsExceededException' error, and downstream data is occasionally incomplete. They want to allow overlapping runs when needed but prevent runaway parallelism, without rewriting the pipeline logic. What is the MOST appropriate action?