Abstract

Applications of cloud computing are increasing as companies shift from on-premise IT environments to public, private, or hybrid clouds. Consequently, cloud providers use capacity planning to maintain the capacity of computing resources (instances) required to meet the dynamic nature of the demand (queries). However, there is a trade-off between deploying too many costly instances, and deploying too few instances and paying penalties for not being able to process queries on-time. An instance has multiple resource dimensions and executing a query consumes multiple dimensions of an instance’s capacity. This detailed multi-dimensional management of cloud computing resource capacity is known as elasticity management and is an important issue faced by all cloud providers. Determining the optimal number of instances needed in a given planning horizon is challenging, due to the combinatorial nature of the optimization problem involved. We develop an optimization model and related algorithms to capture the trade-off between the resource cost versus the delayed execution penalty in software as a service applications from the cloud provider’s perspective. We develop an exact approach to solve small to medium sized applications and heuristics to solve large applications. We then evaluate their performance via extensive computational analyses with real-world data and current cloud provider approaches. We also develop a stochastic framework and methodology to deal with demand uncertainty, and using two different randomly generated data sets (representing problem instances in practice), we demonstrate that robust solutions can be obtained.

摘要

随着公司从本地IT环境转移到公共，私有或混合云，云计算的应用正在增加。因此，云提供商使用容量规划来维护满足需求（查询）的动态性质所需的计算资源（实例）的容量。但是，在部署太多昂贵的实例与部署太少的实例以及因无法按时处理查询而付出的代价之间需要权衡。实例具有多个资源维度，执行查询会消耗实例容量的多个维度。云计算资源容量的这种详细的多维管理被称为弹性管理，并且是所有云提供商所面临的重要问题。由于所涉及的优化问题的组合性质，在给定的计划范围内确定所需的最佳实例数具有挑战性。我们开发了一个优化模型和相关算法，以从云提供商的角度捕获软件即服务应用程序中资源成本与延迟执行代价之间的权衡。我们开发了一种精确的方法来解决中小型应用程序，并开发了启发式方法来解决大型应用程序。然后，我们通过对真实数据和当前云提供商方法的大量计算分析来评估其性能。我们还开发了一种随机框架和方法来应对需求不确定性，并使用两个不同的随机生成的数据集（在实践中代表问题实例），