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.
展开▼
