Cloud computing has drastically reduced the price of computing resources through the use of virtualized resources that are shared among users. However, the established large cloud data centers have a large carbon footprint owing to their excessive power consumption. Inefficiency in resource utilization and power consumption results in the low fiscal gain of service providers. Therefore, data centers should adopt an effective resource‐management approach. In this paper, we present a novel load‐balancing framework with the objective of minimizing the operational cost of data centers through improved resource utilization. The framework utilizes a modified genetic algorithm for realizing the optimal allocation of virtual machines (VMs) over physical machines. The experimental results demonstrate that the proposed framework improves the resource utilization by up to 45.21%, 84.49%, 119.93%, and 113.96% over a recent and three other standard heuristics‐based VM placement approaches.

中文翻译：

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云数据中心网络的资源高效的负载平衡框架

云计算通过使用用户之间共享的虚拟化资源，大大降低了计算资源的价格。但是，已建立的大型云数据中心由于功耗过大而具有较大的碳足迹。资源利用和功耗的低效率导致服务提供商的财政收益较低。因此，数据中心应采用有效的资源管理方法。在本文中，我们提出了一种新颖的负载平衡框架，其​​目标是通过提高资源利用率来最小化数据中心的运营成本。该框架利用改进的遗传算法来实现虚拟机（VM）在物理机上的最佳分配。