论文标题
雾计算中资源管理的审查：机器学习观点
A Review of Resource Management in Fog Computing: Machine Learning Perspective
论文作者
论文摘要
雾计算成为一项有前途的技术，可以处理用户在用户接近度附近的请求，以减少延迟敏感性请求的响应时间。尽管具有优势，但资源异质性和局限性等属性以及其动态和不可预测的性质会大大降低了雾计算的效率。因此，预测雾的动态行为和相应地管理资源至关重要。在这项工作中，我们对雾环境中基于机器学习的预测资源管理方法进行了审查。资源管理分为六个子区域：资源提供，应用程序放置，调度，资源分配，任务卸载和负载平衡。根据目标指标，工具，数据集和利用技术，对审查的资源管理方法进行了分析。
Fog computing becomes a promising technology to process user's requests near the proximity of users to reduce response time for latency-sensitive requests. Despite its advantages, the properties such as resource heterogeneity and limitations, and its dynamic and unpredictable nature greatly reduce the efficiency of fog computing. Therefore, predicting the dynamic behavior of the fog and managing resources accordingly is of utmost importance. In this work, we provide a review of machine learning-based predictive resource management approaches in a fog environment. Resource management is classified into six sub-areas: resource provisioning, application placement, scheduling, resource allocation, task offloading, and load balancing. Reviewed resource management approaches are analyzed based on the objective metrics, tools, datasets, and utilized techniques.
