Autonomic Microservices For Capital Markets: Policy-Driven Auto-Scaling With Risk-Aware Constraints

Abstract

Traditional cloud auto-scaling responds to infrastructure metrics like CPU utilization and request latency. Capital markets infrastructure requires different considerations. Financial risk exposure matters. Market volatility matters. Regulatory compliance cannot be ignored. Standard orchestration platforms like Kubernetes Horizontal Pod Autoscaler operate without awareness of financial semantics. The platforms ignore market volatility indices. Bid-ask spread dynamics get overlooked. Liquidity depth receives no consideration. Trading volume anomalies remain invisible. Position exposure stays unexamined. The Risk-Aware Autonomic Orchestration Framework addresses these gaps through policy-driven autonomic scaling architecture. The framework integrates computational workload monitoring with real-time market risk indicators. Regulatory constraints receive enforcement through declarative policies. A reinforcement learning control loop performs multi-objective optimization. Trade throughput gets maximized. Infrastructure costs decrease. Systemic risk management improves across diverse volatility regimes and market stress conditions. Proportional-Integral-Derivative control mechanisms maintain system stability. Oscillatory scaling behaviors get prevented. Experimental validation demonstrates significant improvements. Latency reductions occur. Infrastructure cost savings materialize. Regulatory compliance violations decrease substantially compared to baseline Kubernetes HPA. The framework establishes a novel paradigm. AI-driven orchestration mechanisms achieve alignment between computational elasticity decisions and financial risk management principles. A critical gap in cloud-native trading platform architectures gets addressed. Infrastructure management traditionally operates decoupled from domain-specific risk considerations.