From Trade to Compliance a Cloud-Native AI Architecture for Real-Time Derivatives Pricing
DOI:
https://doi.org/10.15680/IJCTECE.2026.0904006Keywords:
Low Latency Computing, Derivatives Market Infrastructure, Cloud Native Architecture, Microservices Decomposition, Service Mesh Design, Latency Sensitive Workloads, Real Time Trading Systems, Elastic Compute Scaling, Pay Per Use Resource Optimization, Load Balancing Across Availability Zones, Cloud Resilience Engineering, Operational Resilience Risk, Outsourcing Risk Appetite, Intelligent Service Brokerage, High Availability Architectures, Cloud Based Market Systems, Performance Optimized Computing Models, Financial Market Infrastructure Modernization, Scalable Trading Platforms, Resilient Cloud EcosystemsAbstract
The growing demand for low-latency computing in derivatives markets challenges the traditional approach of monolithic applications implemented in dedicated data center clusters. Cloud-native architectures offer a promising solution due to the flexibility, scalability, and resilience of the cloud. Nevertheless, for latency-sensitive workloads, latency is not only a requirement; cloud-native design principles can lead to improvements in the computing model itself. These include breaking down monolithic applications into microservices deployed in a service mesh, taking advantage of on-demand and pay-per-use resources to optimize costs for off-peak traffic, and improving computing efficiency through loading-balancing across cloud availability zones.
Furthermore, the demonstrably increasing risk of operational resilience in the cloud at a suitably architected cloud level requires organisations and business partners to reconsider their outsourcing risk appetite. Even where a loss occurs, insurance can cover events, as long as the incident doesn't lead to the organisation ceasing to trade for an extended period. For many services, intelligent brokerage can ensure continued service. Examined in more depth, these principles may also strengthen the fundamental efficiency and performance of the models beyond a simple scaling gain.
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