RITA: Reliable and Efficient Task Management in Edge Computing for AIoT Systems

物联网人工智能（AIoT）在推动基于人工智能的创新以支持智能制造、自动驾驶和智慧城市等新兴智能应用方面具有巨大潜力，因此在全球范围内引起了广泛的研究关注。为了处理大量数据并处理计算密集型人工智能算法，边缘计算已成为支持AIoT的关键构建块。AIoT计算任务管理的生命周期包括任务卸载决策、任务传输和任务执行。为了应对这些挑战，该项目旨在创建一套新的任务管理理论和方法，以实现AIoT系统可靠和高效的边缘计算。为此，我将首先开发一种原始的并行性和依赖性感知的卸载决策方案，以寻找具有最大并行性和最小依赖性的细粒度任务，从而以更少的能耗卸载更有效的任务负载。然后，我将提出一种新的任务传输协议，该协议采用基于噪声模式的轻量级交叉访问链路预测方案。最后，设计机会任务分配方案，将每个任务分配给多个机会冗余的边缘服务器，以实现任务执行的最小延迟。一个全面的AIoT测试平台和开放访问软件将用于性能评估和演示拟议的任务管理方法。研究成果将有助于欧洲在有前途的AIoT领域的竞争力和增长。精心策划的培训活动将使我掌握新的技能和能力，增强我的创造和创新潜力，促进我的职业前景。开放科学实践以及有计划的交流、传播和利用活动将面向整个社会，使研究成果对公民可见。

Artificial Intelligence of Things (AIoT) has been attracting significant research attention worldwide owing to its great potential in boosting the promising AI-based innovations to support emerging smart applications, such as intelligent manufacturing, autonomous driving, and smart city. To handle massive volumes of data and process computation-intensive AI algorithms, edge computing has emerged as a key building block to empower AIoT. The lifecycle of computational task management in AIoT consists of task offloading decision, task transmission, and task execution. To tackle these challenges, this project aims to create a suite of novel theories and approaches of task management towards achieving reliable and efficient edge computing for AIoT systems. To this end, I will firstly develop an original parallelism- and dependency- aware offloading decision scheme to find the fine-grained tasks with maximised parallelism and minimised dependency, such that more effective task workloads can be offloaded with less energy consumption. I will then propose a novel task transfer protocol that adopts a lightweight cross-access link prediction scheme based on the noise patterns. Finally, an opportunistic task allocation scheme will be designed, which allocates each task to multiple opportunistic and redundant edge servers for achieving the minimum delay for task execution. A holistic AIoT testbed and open-access software will be implemented for performance evaluation and demonstration of the proposed task management approaches. The research outcomes will contribute to Europe's competitiveness and growth in the promising AIoT area. Well-planned training activities will equip me with new skills and competences to enhance my creative and innovative potential and promote my career prospects. Open science practices as well as planned communication, dissemination, and exploitation activities will reach out to society at large and make the research results visible to citizens.