A Resilient Cloud Computing Architecture for Fault-Tolerant Data Processing Using AI-Based Error Recovery
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V1I4P101Keywords:
Resilient Cloud Computing, Fault Tolerance, AI-Based Error Recovery, Anomaly Detection, Reinforcement Learning, Checkpointing, Erasure Coding, Speculative Execution, Microservices, Kubernetes, Stream And Batch Processing, Service-Level Objectives (Slos), Multi-Cloud; Autoscaling, Chaos EngineeringAbstract
Modern data-driven services demand uninterrupted processing despite hardware faults, software bugs, and transient network failures. This paper presents a resilient cloud computing architecture for fault-tolerant data processing that blends proven reliability techniques with AI-based error recovery. The design layers microservices on container orchestration (e.g., Kubernetes) across hybrid/multi-cloud zones and couples streaming and batch pipelines with adaptive checkpointing, erasure coding, and speculative re-execution. A learning-enabled resilience controller combines online anomaly detection (sequence models over telemetry and logs) with reinforcement-learning policies that decide when to retry, roll back to checkpoints, switch execution paths, or proactively migrate workloads. The controller optimizes a multi-objective reward that balances SLO adherence (latency/throughput), cost, and recovery risk. A dependency-aware graph tracks inter-service health to enable localized circuit breaking and state reconciliation, while a policy layer enforces blast-radius limits via canary rollouts and automated runbooks. We prototype the architecture on commodity clusters with synthetic and production-like workloads, injecting realistic faults (node crashes, pod evictions, degraded disks, and tail-latency spikes). Results show consistent SLO protection under diverse failure modes, rapid recovery without human intervention, and cost-aware scaling during incident bursts. We discuss engineering trade-offs, including checkpoint granularity, model drift, and governance for AI-driven actions, and outline a roadmap for verifiable resilience using chaos testing and formalized recovery invariants
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