Topology-aware virtual machine placement for improving cloud servers resource utilization

As cloud computing offers increasingly sophisticated services, the optimal decisions of virtual machine (VM) placement become rather complicated, which may significantly influence the efficiency and profitability of cloud data centers (CDCs). In this paper, a realistic and holistic resource allocation model is proposed for the VM placement problem. Both the multi-layer topology of CDCs with complex topology-related user requests and the impact of multi-NUMA structures within servers are incorporated. Moreover, a novel objective function is developed to maximize overall resource utilization over an extended time horizon. The remaining resources of a server are characterized through the provision of three types of value: the value of hosting the current VM request, the potential value of accommodating future VM requests, and the topological value. A sophisticated value function is designed to integrate these components and quantify the overall benefit of placing VMs on a server, accounting for both the present and future values. As the resulting integer programming (IP) formulation is essentially NP-hard, a value-driven online algorithm is customized and developed to make online placement decisions following the proposed value function. By sequentially assigning VMs to feasible servers that maximize the evaluated placement value, our algorithm achieves a desirable trade-off between solution quality and computational efficiency. Numerical experiments on a practical cloud computing dataset demonstrate the effectiveness, efficiency, and scalability of the proposed VM placement method. Our test results indicate that the online placement decisions achieve over 80% of the global optimum (i.e., obtained from offline optimization), which outperforms other popular online methods, e.g., Fit-class heuristics and Deep Q-Network (DQN) based learning method. Besides, even with a challenging scale of 30,000 servers, our algorithm can make efficient placement decisions for 100 VMs within 1 s.