Workload-Aware Agent Orchestration for Large-Scale Vulnerability Remediation Pipelines
DOI:
https://doi.org/10.66238/smsrj.v4i2.83Keywords:
Vulnerability Remediation, Agent Orchestration, Workload Management, Distributed Systems, Cybersecurity AutomationAbstract
The rapid expansion of cloud-native computing and microservice architectures has significantly accelerated the software development lifecycle, concurrently expanding the attack surface for potential security exploits. As large-scale organizational infrastructures ingest thousands of vulnerability disclosures daily, conventional static vulnerability remediation pipelines suffer from severe resource bottlenecks, high latency, and inefficient utilization of computational resources. To address these critical challenges, this paper proposes a novel workload-aware agent orchestration framework designed specifically for large-scale vulnerability remediation pipelines. The proposed framework introduces a dynamic scheduling methodology that continuously monitors queue depths, agent health, and processing complexity to intelligently distribute remediation workloads across a decentralized fleet of autonomous agents. By integrating advanced queuing theory with predictive workload estimation models, the orchestrator dynamically scales and reallocates resources in real-time, minimizing end-to-end remediation latency while maximizing systemic throughput. Comprehensive empirical evaluations conducted in a simulated enterprise-scale cloud environment demonstrate that the workload-aware orchestration framework reduces average remediation latency by a significant margin compared to traditional round-robin and static allocation strategies. Furthermore, the system achieves superior resource utilization efficiency and maintains robust fault tolerance under volatile load conditions. The findings presented in this study offer a scalable, resilient, and highly efficient solution for automating vulnerability management in complex computational environments, paving the way for next-generation automated cybersecurity defense mechanisms.
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