Wang Bin, Liu Fagui
School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China.
Math Biosci Eng. 2021 Mar 22;18(3):2713-2732. doi: 10.3934/mbe.2021138.
With the growth and expansion of cloud data centers, energy consumption has become an urgent issue for smart cities system. However, most of the current resource management approaches focus on the traditional cloud computing scheduling scenarios but fail to consider the feature of workloads from the Internet of Things (IoT) devices. In this paper, we analyze the characteristic of IoT requests and propose an improved Poisson task model with a novel mechanism to predict the arrivals of IoT requests. To achieve the trade-off between energy saving and service level agreement, we introduce an adaptive energy efficiency model to adjust the priority of the optimization objectives. Finally, an energy-efficient virtual machine scheduling algorithm is proposed to maximize the energy efficiency of the data center. The experimental results show that our strategy can achieve the best performance in comparison to other popular schemes.
随着云数据中心的增长和扩展，能源消耗已成为智慧城市系统的一个紧迫问题。然而，当前大多数资源管理方法都集中在传统的云计算调度场景上，却未能考虑物联网（IoT）设备工作负载的特征。在本文中，我们分析了物联网请求的特性，并提出了一种改进的泊松任务模型，该模型具有一种新颖的机制来预测物联网请求的到达。为了在节能和服务水平协议之间实现权衡，我们引入了一种自适应能效模型来调整优化目标的优先级。最后，提出了一种节能虚拟机调度算法，以最大化数据中心的能源效率。实验结果表明，与其他流行方案相比，我们的策略能够实现最佳性能。
