Enterprise cloud data centers consume a tremendous amount of energy due to the large number of physical machines (PMs). These PMs host a huge number of virtual machines (VMs), on which a vast number of applications are deployed. Existing research uses two separate layers to manage data center resources: application assignment to VMs, and VM placement to PMs, each of which is a bin packing problem. While this consecutive two-layer bin packing (Consec2LBP) makes the problems easier to solve, it also limits further improvement in the quality of solution. To address this issue, an integrated any colony optimization approach is proposed in this paper to deal with both layers simultaneously. It formulates the two-layer resource management into an integrated two-layer bin packing (Int2LBP) optimization problem. Then, an integrated first fit-decreasing (FFD) algorithm Int2LBP_FFD is proposed to solve this optimization problem. Using the result of Int2LBP_FFD as an initial solution, an integrated ant colony system (ACS) algorithm Int2LBP_ACS is further developed to improve the quality of solution. Simulation experiments are conducted to demonstrate the effectiveness of our integrated approach.

由于大量的物理机（PM），企业云数据中心消耗大量能源。这些PM托管大量虚拟机（VM），并在其上部署了大量应用程序。现有研究使用两个单独的层来管理数据中心资源：将应用程序分配给VM，将VM放置到PM，每一个都是垃圾箱问题。这种连续的两层装箱（Consec2LBP）使问题更易于解决，但同时也限制了解决方案质量的进一步提高。为了解决这个问题，本文提出了一种集成的任何菌落优化方法来同时处理这两个层次。它将两层资源管理公式化为一个集成的两层bin打包（Int2LBP）优化问题。然后，为了解决该优化问题，提出了一种集成的第一拟合递减算法（Inst2LBP_FFD）。使用Int2LBP_FFD的结果作为初始解决方案，进一步开发了集成蚁群系统（ACS）算法Int2LBP_ACS以提高解决方案的质量。进行仿真实验以证明我们集成方法的有效性。