Revenue Management For Enterprise Users of Cloud Infrastructure

虚拟化、云基础设施的使用已经改变了现代企业计算的性质和范围。如今，大型企业不再投资于专用的计算基础设施，而是通过利用免费和随用随付的云产品有效地租用计算基础设施。从经济角度来看，这创造了一个双赢的局面：它让“买家”为他们使用的东西付费，并以低成本无缝地扩展他们的基础设施，而无需投资购买或维护资源，同时为“卖家”或供应商创造有利可图的业务。成本节约源于将资源需求有效地汇集到云基础设施的多个买家；这些节省的成本原则上由云基础设施的提供者和消费者共享。然而，就目前的情况来看，买家最终只能从一家供应商那里购买资源，这导致了效率低下。这个项目的目标是解决运营方面的挑战，为企业云基础设施提供一个高效的市场。主要调查人员致力于指导来自代表性不足和少数群体的个人。该项目侧重于两个平行的研究重点：买方和卖方问题。这两个问题的一个关键因素是不确定性所扮演的角色，以及采用随机模型（在这个领域往往是高度不稳定的）或对抗模型（往往忽略大量的历史数据）所面临的挑战。因此，项目引入了一个数据驱动的模型，该模型概括了统计文献中具有丰富历史的广泛模型类。这个模型介于随机模型和对抗模型之间。在这个模型的背景下，研究了两类重要的问题。在买方方面，研究了被称为k-秘书问题的自然版本。在卖方方面，研究了需求的网络收益管理模型。在所有这些方面，指导目标是推进开发开箱即用软件所需的科学，这些软件可以由从业者部署，而无需模型拟合或校准。该项目可能会导致一个开源系统，使买家能够利用现货市场大幅降低成本。

The use of virtualized, cloud infrastructure has transformed the nature and scope of modern enterprise computing. Large corporations today, in place of investing in dedicated computing infrastructure, effectively rent compute infrastructure by taking advantage of both free and pay-as-you-go cloud offerings. From an economic perspective this creates a win-win situation: it lets "buyers" pay for what they use and scale their infrastructure seamlessly at low cost without investing to buy or maintain resources while creating profitable businesses for "sellers" or providers. Cost-savings are derived from effectively pooling resource needs across a number of buyers of cloud infrastructure; these cost-savings are in principle shared by providers and consumers of cloud infrastructure. However, as it stands currently buyers end up buying resources from one provider only leading to inefficiency. The goal of this project is to address the operational challenges to enable an efficient market for the enterprise cloud infrastructure. The principle investigators are committed to the mentoring of individuals from underrepresented and minority groups.This project focuses on two parallel research thrusts: buyer-side -and seller-side problems. A key element of both these problems is the role played by uncertainty and the challenge in adopting either stochastic models (which tend to be highly unstable in this domain) or adversarial models (which tend to ignore the copious amount of historical data typically available). As such, project introduces a data-driven model that generalizes a broad class of models with a rich history in the statistics literature. This model treads the line between stochastic and adversarial modeling. In the context of this model, two important classes of problems are studied. On the buyer side, a natural version of what has become known as the k-secretary problem is studied. On the seller side, a Network Revenue Management model of demand is studied. In all of these, the guiding objective is to advance science required to develop out-of-the-box software that can be deployed by practitioners without the need for model fitting or calibration. The project will potentially lead to open-source system that allows buyers to achieve a dramatic reduction in costs taking advantage of spot markets.