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DiCE-M: Distributed Code Generation and Execution for Marine Applications – An Edge-Cloud Approach

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Edge computing has emerged as a transformative technology that reduces application latency, improves cost efficiency, enhances security, and enables large-scale deployment of applications across various domains. In environmental monitoring, systems such as MegaSense[49], use low-cost sensors to gather and process real-time air quality data through edge-cloud collaboration, highlighting the critical role of edge computing in enabling scalable, efficient solutions. Similarly, marine science increasingly requires real-time processing and analysis of marine data from remote, resource-constrained environments. In this paper, we extend the power of edge computing by integrating it with Generative Artificial Intelligence(GenAI),specifically large language models (LLMs), to address challenges in marine science applications. We propose DiCE-M (Distributed Code generation and Execution for Marine applications), a robust system that uses LLM to generate distributed code for marine applications and then utilizes a runtime to efficiently execute it on an edge+cloud computing infrastructure. Specifically, DiCE-M leverages edge computing to execute lightweight AI models locally on unmanned surface vehicles(USVs)while offloading complex tasks to the cloud, thus balancing computational load and enabling realtime monitoring in marine environments. We use marine litter identification as an example application to demonstrate the utility of DiCE-M. Our results show that DiCE-M reduces latency by more than 2X when marine litter is not detected and cuts cloud computing costs by more than half compared to traditional cloud-based approaches. By selectively cropping and transmitting relevant image portions, DiCE-M further improves bandwidth efficiency, making it a reliable and cost-effective solution for deploying AI-driven applications on resource-constrained USVs in dynamic marine environments.

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ECO-LLM: LLM-based Edge Cloud Optimization

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AI/ML techniques have been used to solve systems problems, but their applicability to customize solutions on-the-fly has been limited. Traditionally, any customization required manually changing the AI/ML model or modifying the code, configuration parameters, application settings, etc. This incurs too much time and effort, and is very painful. In this paper, we propose a novel technique using Generative Artificial Intelligence (GenAI) technology, wherein instructions can be provided in natural language and actual code to handle any customization is automatically generated, integrated and applied on-the-fly. Such capability is extremely powerful since it makes customization of application settings or solution techniques super easy. Specifically, we propose ECO-LLM (LLM-based Edge Cloud Optimization), which leverages Large Language Models (LLM) to dynamically adjust placement of application tasks across edge and cloud computing tiers, in response to changes in application workload, such that insights are delivered quickly with low cost of operation (systems problem). Our experiments with real-world video analytics applications i.e. face recognition, human attributes detection and license plate recognition show that ECO-LLM is able to automatically generate code on-the-fly and adapt placement of application tasks across edge and cloud computing tiers. We note that the trigger workload (to switch between edge and cloud) for ECO-LLM is exactly the same as the baseline (manual) and actual placement performed by ECO-LLM is only slightly different i.e. on average (across 2 days) only 1.45% difference in human attributes detection and face recognition, and 1.11% difference in license plate recognition. Although we tackle this specific systems problem in this paper, our proposed GenAI-based technique is applicable to solve other systems problems too.

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