Reducing the Upfront Cost of Private Clouds with Clairvoyant Virtual Machine Placement

TL;DR

This paper introduces a new model and algorithm for virtual machine placement in private clouds, aiming to reduce upfront costs by leveraging additional scheduling information and efficient optimization techniques.

Contribution

It proposes a heterogeneous, multidimensional clairvoyant bin packing model and a novel divide-and-conquer branch-and-bound algorithm tailored for private cloud VM placement.

Findings

DCBB achieves near-optimal solutions faster than existing algorithms.

DCBB finds the optimal solution for real-world workloads significantly quicker.

Experimental results validate the efficiency and accuracy of the proposed methods.

Abstract

Although public clouds still occupy the largest portion of the total cloud infrastructure, private clouds are attracting increasing interest from both industry and academia because of their better security and privacy control. According to the existing studies, the high upfront cost is among the most critical challenges associated with private clouds. To reduce cost and improve performance, virtual machine placement (VMP) methods have been extensively investigated, however, few of these methods have focused on private clouds. This paper proposes a heterogeneous and multidimensional clairvoyant dynamic bin packing (CDBP) model, in which the scheduler can conduct more efficient VMP processes using additional information on the arrival time and duration of virtual machines to reduce the datacenter scale and thereby decrease the upfront cost of private clouds. In addition, a novel branch-and-bound algorithm with a divide-and-conquer strategy (DCBB) is proposed to effectively and efficiently handle the derived problem. One state-of-the-art and several classic VMP methods are also modified to adapt to the proposed model to observe their performance and compare with our proposed algorithm. Extensive experiments are conducted on both real-world and synthetic workloads to evaluate the accuracy and efficiency of the algorithms. The experimental results demonstrate that DCBB delivers near-optimal solutions with a convergence rate that is much faster than those of the other search-based algorithms evaluated. In particular, DCBB yields the optimal solution for a real-world workload with an execution time that is an order of magnitude shorter than that required by the original branch-and-bound (BB) algorithm.