Cloud-Native Frameworks for Low-Latency Applications: Optimizing Real-Time Data Processing in Multi-Cloud Environments

Anant Kumar¹ & Dr Shantanu Bindewari²

Manipal University
Madhav Nagar, Manipal, Karnataka 576104, India

IILM University

Greater Noida

Abstract

The optimization of low-latency data processing in multi-cloud environments is a significant challenge in the era of cloud-native architectures, where real-time applications are increasingly critical. This review explores various frameworks and strategies developed between 2015 and 2024, aiming to reduce latency in distributed cloud systems. Cloud-native frameworks such as Apache Kafka and Apache Flink are widely used for real-time data streaming, but their performance can be hindered by network latency and the complexity of multi-cloud configurations. Recent advancements have introduced serverless computing and edge computing as promising solutions to address latency, offering scalability and proximity to data sources for faster processing. Moreover, machine learning and predictive analytics have been integrated into load balancing and auto-scaling mechanisms to proactively manage resources, ensuring efficient handling of varying workloads. The integration of 5G networks further enhances the low-latency capabilities of cloud-native systems, enabling real-time processing for high-demand applications. Compression techniques and blockchain integration have also been explored to reduce network bottlenecks and ensure data integrity without sacrificing performance. This paper synthesizes key findings from these studies, highlighting the importance of intelligent orchestration, edge-cloud collaboration, and advanced technologies like 5G and blockchain in optimizing low-latency data processing. Future research should continue to refine these techniques, focusing on seamless multi-cloud orchestration, machine learning-based latency prediction, and the integration of emerging technologies to further improve real-time application performance in cloud-native environments.

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