A High-Performance Parallel Computing Model for Big Data Stream Processing and Scalable Analytics in Hybrid Cloud Environments
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V5I1P101Keywords:
Hybrid Cloud, Big Data Streams, Parallel Dataflow, Micro-Batch Processing, Topology-Aware Scheduling, RDMA Shuffle, NUMA-Aware Execution, GPU/FPGA Acceleration, Serverless Orchestration, Cost/SLA-Aware Autoscaling, Exactly-Once Semantics, Checkpointing And Lineage, Federated Analytics, Real-Time Inference, Scalable AnalyticsAbstract
This paper presents a high-performance parallel computing model for big data stream processing and scalable analytics in hybrid cloud environments spanning edge, on-prem clusters, and multi-cloud providers. The model unifies dataflow and micro-batch paradigms through a heterogeneous runtime that exploits multi-level parallelism: pipeline and task parallelism across distributed operators, vectorized, NUMA-aware execution within nodes, and accelerator offloading to GPUs/FPGAs for compute-intensive kernels (e.g., joins, aggregations, inference). A topology-aware scheduler leverages RDMA-enabled shuffle and adaptive windowing to minimize tail latency under bursty workloads, while a cost/SLA-aware autoscaler coordinates containerized services and serverless functions across regions. Reliability is ensured via exactly-once processing with lightweight lineage, speculative execution for stragglers, and tiered checkpointing to object stores. Security and governance are embedded through policy-based data localization, encrypted state, and optional federated operators for cross-domain analytics. The model supports both continuous queries and mixed analytical/ML pipelines, enabling online feature generation, drift monitoring, and low-latency inference. We outline the programming abstraction, control-plane algorithms for placement and scaling, and the execution engine’s memory and I/O optimizations. Together, these components deliver predictable sub-second latencies for high-velocity streams while sustaining elastic throughput growth, providing a portable foundation for real-time intelligence, observability, and decision support in modern hybrid clouds
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