Hybrid Model Deployment: Balancing Edge and Cloud Computation

Abstract

During the past couple of years, rapid development of edge computing and cloud technologies has allowed for the implementation of hybrid models that offload computation to edge devices as well as cloud platforms. This article explores the design decisions in the implementation of hybrid models, specifically focusing on offloading processing to the cloud, and addresses the necessary trade-offs between latency and privacy. We begin by contrasting edge and cloud computing, highlighting the advantages of hybrid systems in enhancing scalability, real-time processing, and flexibility. Significant architectural concerns, such as model partitioning and offloading rules, are addressed in the context of the dynamic nature of edge and cloud environments. Latency is a fundamental concern that influences the effectiveness of hybrid systems, especially in applications involving real-time processing. We explore how to minimize latency through edge caching, adaptive algorithms, and local computation for enhanced system performance. Privacy becomes an issue when handling sensitive data on the edge and the cloud. In this paper, we present privacy-preserving mechanisms, such as data anonymization, encryption, and federated learning, to secure user information while leveraging the computational power of the cloud. By performance metric evaluation, such as latency, precision, and scalability, we compare hybrid model deployment with cloud-only and edge-only deployment. We concluded the paper by outlining challenges experienced in hybrid deployment, including network limitations and model complexity, and introduce future work ideas on further enhancing edge to cloud computation balance. This paper offers a thorough examination of deploying hybrid models and offers real-world architectural advice on how to maximize system performance without exacerbating latency and privacy concerns.