Multi-Objective Adaptive Manta-Ray Foraging Optimization for Workflow Scheduling with Selected Virtual Machines Using Time-Series-Based Prediction

Sweta Singh, R. Kumar, U. P. Rao
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Abstract

Optimization problems are challenging, but the larger challenge is to deal with the energy issue of the cloud, with continuous dynamic load and fluctuating VM performance. The optimization technique aids in efficient task-resource mapping ensuring optimal resource utilization with minimum active hosts and energy consumption. Existing works focused on time-invariant and bounded VM performance with major concentration on minimizing the execution cost and time. A multi-objective adaptive manta-ray foraging optimization (MAMFO) has been proposed in the paper for efficient scheduling with optimum resource utilization and energy consumption. The paper contributes by considering the time-varying VM performance and performance prediction using a dynamic time-series based ARIMA model, filtering out the VMs with larger fluctuating possibility, and employing only the selected VMs to be scheduled using MAMFO to meet the optimization goal with minimum SLA violations. The experimental analysis improves the work efficiency (e.g., energy consumption attained to be 0.405 kWh, and 5.97% of SLA violations).
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基于时间序列预测的选择虚拟机工作流调度多目标自适应蝠鲼觅食优化
优化问题是具有挑战性的,但更大的挑战是处理云的能源问题,持续的动态负载和波动的VM性能。优化技术有助于有效的任务-资源映射,以最小的活动主机和能量消耗确保最佳的资源利用。现有的工作主要集中在定常和有界VM性能上,主要集中在最小化执行成本和时间上。提出了一种多目标自适应蝠鲼觅食优化算法,以实现资源利用和能量消耗最优的高效调度。本文通过考虑时变的虚拟机性能和基于动态时间序列的ARIMA模型的性能预测,过滤掉波动可能性较大的虚拟机,使用MAMFO只使用被选中的要调度的虚拟机,以最小的SLA违规来满足优化目标。实验分析提高了工作效率(如能耗0.405 kWh,违反SLA率5.97%)。
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