Cloud providers face the challenge of efficiently managing their infrastructure through minimizing resource consumption while allocating service requests such that their revenue is maximized. Solutions addressing this challenge should consider the sharing of memory pages among virtual machines (VMs) and the available capacity of each type of requested resources. We provide such solution by designing a greedy approximation algorithm for solving the sharing-aware virtual machine revenue maximization (SAVMRM) problem. The SAVMRM problem requires determining the set of VMs that can be instantiated on a given server such that the revenue derived from hosting the VMs is maximized. In addition, we model the SAVMRM problem as a multilinear binary program and optimally solve it, while accounting for page sharing and multiple resource constraints. We determine and analyze the approximability properties of our proposed greedy algorithm and evaluate it by performing extensive experiments using Google cluster workload traces. The experimental results show that under various scenarios, our proposed algorithm generates higher revenue than other VM allocation algorithms while achieving significant reduction of allocated memory.

中文翻译：

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一种共享感知虚拟机收益最大化的近似算法

云提供商面临着通过最小化资源消耗同时分配服务请求以最大化其收入来有效管理其基础架构的挑战。解决这一挑战的解决方案应考虑在虚拟机 (VM) 之间共享内存页面以及每种类型请求资源的可用容量。我们通过设计一种贪婪逼近算法来解决共享感知虚拟机收入最大化（SAVMRM）问题，从而提供了这样的解决方案。SAVMRM 问题需要确定可以在给定服务器上实例化的 VM 集，从而使托管 VM 的收益最大化。此外，我们将 SAVMRM 问题建模为多线性二进制程序并对其进行优化求解，同时考虑到页面共享和多种资源约束。我们确定并分析了我们提出的贪婪算法的近似性属性，并通过使用 Google 集群工作负载跟踪进行广泛的实验来评估它。实验结果表明，在各种场景下，我们提出的算法比其他VM分配算法产生更高的收益，同时实现了分配内存的显着减少。