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Load balancing is vital for the efficient and long-term operation of clouddata centers. With virtualization, post (reactive) migration of virtualmachines after allocation is the traditional way for load balancing andconsolidation. However, reactive migration is not easy to obtain predefinedload balance objectives and may interrupt services and bring instability.Therefore, we provide a new approach, called Prepartition, for load balancing.It partitions a VM request into a few sub-requests sequentially with starttime, end time and capacity demands, and treats each sub-request as a regularVM request. In this way, it can proactively set a bound for each VM request oneach physical machine and makes the scheduler get ready before VM migration toobtain the predefined load balancing goal, which supports the resourceallocation in a fine-grained manner. Simulations with real-world trace andsynthetic data show that Prepartition for offline (PrepartitionOff) schedulinghas 10%-20% better performance than the existing load balancing algorithmsunder several metrics, including average utilization, imbalance degree,makespan and Capacity_makespan. We also extend Prepartition to online loadbalancing. Evaluation results show that our proposed approach also outperformsexisting online algorithms.

Prepartition: Load Balancing Approach for Virtual Machine Reservations in a Cloud Data Center