ABSTRACT

With the recent advancements in distributed computing technologies, the fog computing model has emerged to provide resource capabilities at the edge of the network for executing IoT applications. However, due to the rapid growth of IoT applications and variability their workload over time, achieving an efficient resource provisioning solution to deal with time-varying workloads as one of the challenging tasks in resource management scope to be considered. In this work, we propose a learning-based resource provisioning approach for managing time-varying workloads of IoT applications in the fog network. Our proposed approach utilises the nonlinear autoregressive (NAR) neural network as prediction method and hidden Markov model (HMM) as a decision-maker to identify scaling decisions to provision the fog resources for serving of workloads of IoT applications. The effectiveness of our proposed solution is evaluated using extension experiments under real-world datasets, and the obtained results from iFogSim toolkit demonstrated that it yields a reduction of the delay and cost and improves resource energy consumption compared with existing baseline mechanisms.

摘要

随着分布式计算技术的最新进展，雾计算模型已经出现，可以在网络边缘提供资源能力来执行物联网应用程序。然而，由于物联网应用程序的快速增长及其工作负载随时间的变化，实现高效的资源供应解决方案来处理随时间变化的工作负载，这是需要考虑的资源管理范围中的一项具有挑战性的任务。在这项工作中，我们提出了一种基于学习的资源配置方法，用于管理雾网络中物联网应用程序的时变工作负载。我们提出的方法利用非线性自回归 (NAR) 神经网络作为预测方法，利用隐马尔可夫模型 (HMM) 作为决策者来确定扩展决策，从而为物联网应用程序的工作负载提供雾资源。我们提出的解决方案的有效性使用真实世界数据集下的扩展实验进行评估，从 iFogSim 工具包获得的结果表明，与现有的基线机制相比，它减少了延迟和成本，并改善了资源能源消耗。