BSHOA: Energy Efficient Task Scheduling in Cloud-fog Environment

Q1 Mathematics
Santhosh Kumar Medishetti, Ganesh Reddy
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引用次数: 0

Abstract

Cloud-fog computing frameworks are innovative frameworks that have been designed to improve the present Internet of Things (IoT) infrastructures. The major limitation for IoT applications is the availability of ongoing energy sources for fog computing servers because transmitting the enormous amount of data generated by IoT devices will increase network bandwidth overhead and slow down the responsive time. Therefore, in this paper, the Butterfly Spotted Hyena Optimization algorithm (BSHOA) is proposed to find an alternative energy-aware task scheduling technique for IoT requests in a cloud-fog environment. In this hybrid BSHOA algorithm, the Butterfly optimization algorithm (BOA) is combined with Spotted Hyena Optimization (SHO) to enhance the global and local search behavior of BOA in the process of finding the optimal solution for the problem under consideration. To show the applicability and efficiency of the presented BSHOA approach, experiments will be done on real workloads taken from the Parallel Workload Archive comprising NASA Ames iPSC/860 and HP2CN (High-Performance Computing Center North) workloads. The investigation findings indicate that BSHOA has a strong capacity for dealing with the task scheduling issue and outperforms other approaches in terms of performance parameters including throughput, energy usage, and makespan time.
BSHOA:云雾环境中的高能效任务调度
云雾计算框架是一种创新框架,旨在改善目前的物联网(IoT)基础设施。物联网应用的主要限制因素是雾计算服务器的持续能源供应,因为传输物联网设备产生的海量数据会增加网络带宽开销并减慢响应时间。因此,本文提出了 "蝶斑鬣狗优化算法"(BSHOA),为云-雾环境中的物联网请求寻找另一种能源感知任务调度技术。在这种混合 BSHOA 算法中,蝴蝶优化算法(BOA)与斑点鬣狗优化算法(SHO)相结合,以增强 BOA 在为所考虑的问题寻找最优解的过程中的全局和局部搜索行为。为了证明所提出的 BSHOA 方法的适用性和效率,我们将在真实工作负载上进行实验,这些工作负载来自并行工作负载档案,包括 NASA Ames iPSC/860 和 HP2CN(高性能计算中心北区)工作负载。调查结果表明,BSHOA 在处理任务调度问题方面具有很强的能力,在吞吐量、能源使用和间隔时间等性能参数方面优于其他方法。
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来源期刊
CiteScore
4.10
自引率
0.00%
发文量
33
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