An emerging cloud‐Internet of Things (IoT) is a novel paradigm that brings potential benefits over a variety of applications. The pervasive use of IoT devices often struggle to meet resource requirements in cloud environment. The abundant wastage or inappropriate usage of resources leads to consume larger amount of energy and delayed response. Optimization algorithms are more popular for optimal selection, the algorithms as ant colony optimization, particle swarm optimization, genetic algorithm were majorly concentrated for the purpose of load balancing. However workload is balanced in many previous research works, however it failed to mitigate SLA violations and limitations of energy consumption. In this paper, we address both energy‐aware load balancing and satisfaction of SLA constraints. First, IoT devices submit tasks which are segregated into queues using policy‐based SLA by taking in account of SLA constraints. Second, the tasks are allocated in accordance to dynamic threshold predicting fuzzy followed by Analytical Hierarchical Process. Third, the workload is employed for energy minimization which decides whether to perform swap or migrate virtual machines (VMs). Swapping between VMs is held by novel map and consolidates processes. Then combination of fruit fly and bird swarm hybrid optimization algorithm is enabled to select an optimal VM for workload balancing. Also, the idle physical machines (PMs) are supposed to be in OFF state for diminishing unnecessary energy consumption. The outcomes of this cloud‐IoT system is experimentally evaluated and compared in terms of energy consumption, number of migrations, resource utilization, and execution time.

中文翻译：

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能源感知和SLA保证的虚拟物联网中的最佳虚拟机交换和迁移系统

新兴的云—物联网（IoT）是一种新颖的范例，可通过各种应用程序带来潜在的好处。物联网设备的广泛使用通常难以满足云环境中的资源需求。大量浪费或不当使用资源会导致消耗大量能量并延迟响应。优化算法是最流行的最优选择方法，蚁群算法，粒子群算法，遗传算法主要集中在负载均衡上。但是，在以前的许多研究工作中，工作负载是平衡的，但是它未能减轻SLA违规和能耗限制。在本文中，我们既解决了能源感知的负载平衡问题，又满足了SLA约束。第一的，物联网设备通过考虑SLA约束，使用基于策略的SLA提交任务，这些任务被隔离到队列中。其次，根据动态阈值预测模糊分配任务，然后进行层次分析。第三，将工作负载用于能量最小化，该工作量决定执行交换还是迁移虚拟机（VM）。VM之间的交换通过新颖的映射进行，并巩固了流程。然后结合果蝇和鸟群混合优化算法，选择最佳虚拟机进行工作量平衡。此外，空闲的物理机（PM）假定处于OFF状态，以减少不必要的能耗。对该Cloud-IoT系统的结果进行了实验评估，并在能耗，迁移数量，资源利用率，