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Capacity Expansions with Bundled Supplies of Attributes: An Application to Server Procurement in Cloud Computing

机译：具有捆绑物资源的容量扩展：云计算中的服务器采购应用程序

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Problem definition: The recent surge in demand for cloud services has posed a significant capacity-expansion problem for cloud infrastructure providers. Although the growth of demand for capacity attributes-for example, CPU and RAM-is disproportionate, these attributes are often provided in preconfigured packages (cluster-types), and the fixed ratio of attributes in a package does not match with the time-varying ratio of demand. We analyze a class of expansion policies to determine the timing andmagnitude of expansions, using preconfigured cluster-types, and we examine the optimal configurations of the cluster-types. Academic/practical relevance: Cloud computing is a major technological advance that is influencing businesses significantly, giving rise to an emerging industry but also posing the above-noted capacity-expansion problem. To our knowledge, this is a new issue that has not been studied in the literature. Methodology: We consider growing demand for two attributes and analyze a class of policies that consist of capacity expansion cycles (CECs), whereby capacities are added through sequential or simultaneous replenishments of two configured cluster-types. Results: We first derive the optimal timing and magnitude of expansions for every CEC, and then we devise two algorithms, the dynamic-programming-based (DP) algorithm and the forward-looking (FL) heuristic, to determine the optimal cycle lengths. We also propose a cluster-selection heuristic for choosing the optimal configurations of the cluster-types. Managerial implications: The FL-heuristic is effective, easy to communicate, and can be used as an excellent starting point for the search of the DP-algorithm. Moreover, because there is a desire in practice to reduce the variety of cluster-types, we find conditions under which the employment of only two cluster-types is as efficient as the employment of many cluster-types. Finally, we provide useful guidelines for the optimal configurations of these two cluster-types.
机译：问题定义：最近对云服务需求的激增为云基础设施提供商带来了显着的容量扩张问题。虽然对容量属性的需求的增长 - 例如，CPU和RAM - 是不成比例的，但这些属性通常以预配置的包（群集类型）提供，并且包中的属性的固定比率与时变不匹配需求比例。我们分析一类扩展策略，以确定使用预配置的群集类型来确定扩展的时序和模拟，我们检查群集类型的最佳配置。学术/实际相关性：云计算是一种主要的技术进步，积极影响企业，引起新兴的行业，但也造成了上述能力扩张问题。为了我们的知识，这是一个尚未在文献中进行过研究的新问题。方法论：我们考虑对两个属性的需求不断增长，并分析一类由容量扩展周期（CEC）组成的策略，由此通过两个配置的群集类型的顺序或同时补充添加容量。结果：我们首先为每个CEC推导出最佳的时序和扩展幅度，然后我们设计了两个算法，动态编程（基于DP）算法和前瞻性（FL）启发式，以确定最佳循环长度。我们还提出了一种用于选择群集类型的最佳配置的集群选择启发式。管理含义：FL-heuristic是有效的，易于通信，并且可以用作搜索DP算法的出色起点。此外，由于在实践中存在渴望减少各种集群类型，我们发现在其中仅使用两个群集类型的条件是与许多群集类型的就业有效。最后，我们为这两个群集类型的最佳配置提供了有用的指导。

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