Modern virtualized data centers often rely on virtual machine (VM) migrations to consolidate workload on a single machine for energy saving. But VM migrations have many drawbacks, including performance degradation, service disruption etc. Hence, many approaches have been proposed to minimize the overhead when migrations occur. In contrast, this work aims to proactively avoid migrations from happening in the first place. We have proposed a novel consolidation aware scheduling algorithm to minimize the number of migrations for batch processing systems by taking advantage of the prior knowledge of consolidation strategy and job information. We show the problem can be formulated as an integer linear programming (ILP) problem, and an effective heuristic solution can be found by a genetic algorithm. Both real and synthetic workload traces were used to evaluate our methods. Experimental results showed that, after comparing with two popular job scheduling algorithms, our approach has reduced the number of migrations by more than 25%.

现代虚拟化数据中心通常依靠虚拟机（VM）迁移将工作负载整合到一台机器上以节省能源。但是VM迁移具有许多缺点，包括性能下降，服务中断等。因此，已经提出了许多方法来最小化发生迁移时的开销。相反，这项工作旨在从一开始就积极避免迁移。我们已经提出了一种新颖的整合意识调度算法，以利用整合策略和工作信息的先验知识来最大程度地减少批处理系统的迁移次数。我们证明了该问题可以表述为整数线性规划（ILP）问题，并且可以通过遗传算法找到有效的启发式解决方案。实际和综合工作量跟踪均用于评估我们的方法。实验结果表明，与两种流行的作业调度算法相比，我们的方法将迁移数量减少了25％以上。