Load balancing is vital for the efficient and long-term operation of cloud data centers. With virtualization, post (reactive) migration of virtual machines (VMs) after allocation is the traditional way for load balancing and consolidation. However, it is not easy for reactive migration to obtain predefined load balance objectives and it 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 start time, end time and capacity demands, and treats each sub-request as a regular VM request. In this way, it can proactively set a bound for each VM request on each physical machine and makes the scheduler get ready before VM migration to obtain the predefined load balancing goal, which supports the resource allocation in a fine-grained manner. Simulations with real-world trace and synthetic data show that our proposed approach with offline version (PrepartitionOff) scheduling has 10%–20% better performance than the existing load balancing baselines under several metrics, including average utilization, imbalance degree, makespan and Capacity_makespan. We also extend Prepartition to online load balancing. Evaluation results show that our proposed approach also outperforms state-of-the-art online algorithms.

负载均衡对于云数据中心的高效、长期运行至关重要。对于虚拟化，分配后虚拟机 (VM) 的后（反应式）迁移是负载平衡和整合的传统方式。然而，反应式迁移不容易获得预定义的负载均衡目标，并且可能会中断服务并带来不稳定。因此，我们提供了一种称为预分区的新方法来进行负载平衡。它将VM请求按开始时间、结束时间和容量需求按顺序划分为几个子请求，并将每个子请求视为常规VM请求。这样，它可以主动为每台物理机上的每个VM请求设置一个界限，并使调度器在VM迁移之前做好准备，以获得预定义的负载均衡目标，从而支持细粒度的资源分配。对真实世界跟踪和综合数据的模拟表明，我们提出的离线版本 (PrepartitionOff) 调度方法在多个指标（包括平均利用率、不平衡程度、makespan 和Capacity_makespan）下的性能比现有负载均衡基线提高了 10%–20%。我们还将 Prepartition 扩展到在线负载平衡。评估结果表明，我们提出的方法也优于最先进的在线算法。