Edge-Native Software: Designing Resilient Apps for Low-Latency, Distributed Environments
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V7I3P103Keywords:
Edge-Native Software, Edge Computing, Microservices, Low Latency, Distributed Systems, KubernetesAbstract
The emergence of edge computing has also changed the way contemporary applications are designed, with the focus put on achieving high-resilience, low-latency software programs that are deployed on decentralized and resource-limited systems. Edge-native software is software purposely made to operate at the edge of a network, to allow processing of real-time data, lower bandwidth consumption, and greater fault resiliency. This paradigm is particularly critical to those areas, including autonomous systems, industrial automation, smart cities, and energy infrastructure, that need sub-second responsiveness and system trustworthiness. In this paper, an in-depth design framework of edge-native applications based on microservices architecture is described. Some of the main principles that we discuss are decentralization, data locality, stateless vs. stateful design, redundancy, fault domain isolation and so on. Reference architecture is proposed to support scalable deployments in the event of network partition, and self-healing and management qualities in processing consistent data across heterogeneous edge nodes. We consider powered-up technologies such as lightweight container orchestration (i.e., K3S, Docker), edge communication (i.e., MQTT, gRPC), and Chaos engineering. The performance of the architecture in the latency, fault, and resource stress situations is confirmed by experimental results on the Kubernetes-based edge testbed. The results show that edge-native solutions have the potential to surpass the cloud-based model in measures of latency, throughput, fault recovery, and energy efficiency. We also comment on representative use cases like cars on autopilot and predictive maintenance, and discover such issues as the complexity of orchestration, safety, and managing AI models at the edge
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