一种用于增强边缘计算的多层分布式计算框架
A Multi-Layered Distributed Computing Framework for Enhanced Edge Computing
AI总结：
本文提出一种基于分层网络结构的多层分布式计算框架，将资源共享扩展到一跳邻域之外，并通过精确方法与启发式策略优化任务分配和调度，显著提升边缘计算性能。
摘要：
物联网和边缘计算的兴起使计算资源更靠近终端用户，惠及众多延迟敏感、计算密集型的应用。为了加速计算，分布式计算是一种有前景的技术，它允许任务在多个计算节点上并行执行。然而，当前研究主要围绕主从范式展开，将资源共享限制在一跳邻域内。这一限制在附近资源有限或连接受限/动态的场景下，可能使分布式计算失效。在本文中，我们通过引入一种新的分布式计算框架来解决这一限制，该框架通过探索分层网络结构将资源共享扩展到一跳邻域之外。我们的框架包括将网络图转化为汇聚树，并基于分层树结构为任务分配和调度制定联合优化问题。为了解决该问题，我们提出了两种可找到最优解的精确方法和三种启发式策略，以提高效率和可扩展性。通过理论分析和全面的仿真研究，对这些方法的性能进行了分析和评估。结果表明，与传统的分布式计算和计算卸载策略相比，这些方法表现出有前景的性能。
英文摘要：
The rise of the Internet of Things and edge computing has shifted computing resources closer to end-users, benefiting numerous delay-sensitive, computation-intensive applications. To speed up computation, distributed computing is a promising technique that allows parallel execution of tasks across multiple compute nodes. However, current research predominantly revolves around the master-worker paradigm, limiting resource sharing within one-hop neighborhoods. This limitation can render distributed computing ineffective in scenarios with limited nearby resources or constrained/dynamic connectivity. In this paper, we address this limitation by introducing a new distributed computing framework that extends resource sharing beyond one-hop neighborhoods through exploring layered network structures. Our framework involves transforming the network graph into a sink tree and formulating a joint optimization problem based on the layered tree structure for task allocation and scheduling. To solve this problem, we propose two exact methods that find optimal solutions and three heuristic strategies to improve efficiency and scalability. The performances of these methods are analyzed and evaluated through theoretical analyses and comprehensive simulation studies. The results demonstrate their promising performances over the traditional distributed computing and computation offloading strategies.
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