International Journal of Science, Engineering and TechnologyAuthors: Vishnu K, Nithin Babu,, Parvathy S, Roshni Kumar
Abstract: Genomic computing systems represent one of the most demanding domains in high-performance computing (HPC), characterized by large-scale data processing, long-running workflows, and the need for high availability. Platforms handling genomic data must manage the execution of complex pipelines such as read alignment, variant calling, and annotation on terabytes of data including FASTQ, BAM, and VCF files. In environments based on Solaris, the combination of robust system engineering and advanced fault-management tools such as ZFS, SMF (Service Management Facility), and FMA (Fault Management Architecture) provides a stable foundation for these bioinformatics workloads. However, the increasing computational and I/O demands also amplify the risks of system degradation, daemon failure, or hardware faults that may interrupt critical research operations. Predictive alerting presents a forward-looking approach to infrastructure management, using statistical modeling, system telemetry, and machine learning to identify and respond to early signs of system stress. In Solaris-based genomic infrastructures, predictive alerting combines native tools such as kstat, fmadm, iostat, prstat, and log analysis from /var/adm/messages to build a rich dataset for analysis. This data can be used to trigger threshold-based alerts, perform trend detection, or even feed anomaly detection models for real-time decision-making. When integrated with job schedulers or bioinformatics tools like Snakemake or BWA, predictive alerting ensures that computational pipelines are safeguarded from failure before it occurs. This review explores the architecture, methodology, and operational benefits of implementing predictive alerting within Solaris genomic infrastructures, providing a blueprint for enhancing data reliability, uptime, and scientific productivity.
DOI: https://doi.org/10.5281/zenodo.16155601
