Collaborative Research: Resource Allocation in Clouds: A Stochastic Modeling and Control Perspective

云计算服务(如Amazon EC2 System、Google AppEngine和Microsoft Azure)正变得无处不在，并开始成为企业和个人计算应用程序的主要计算能力来源。云计算平台(或简称为云)可以按需方式向用户提供各种资源，包括基础设施、软件和服务。与传统的自有自用方式相比，云计算服务消除了云用户购买和维护基础设施的成本，允许用户根据自己的需求实时动态扩大和缩小计算资源。云由多台机器(计算机)组成，每台机器都有一定的资源(CPU、RAM、硬盘空间等)。每台计算机都可以细分为多个虚拟计算机，其中每个虚拟计算机(VM)的行为就像一台拥有一定专用资源的小型计算机。当用户向云提交作业时，他或她从云请求一定量的资源，云通过在机器中创建具有所需资源的VM来响应。资源分配问题是找出如何将作业分配给机器。此外，当多个作业正在等待服务时，云还必须决定下一步选择哪个作业进行服务。该项目的目标是为云的高效运行设计资源分配算法，并设计定价机制以最大化云服务提供商的收入，同时为竞争用户提供良好的服务质量。智能优点：该领域的现有技术将该问题视为如下一系列静态问题：考虑当前等待服务的作业，并通过求解组合优化问题将其分配给机器。忽视系统动态性质的静态方法会导致不稳定。这里我们的观点是根本不同的：我们将资源分配问题视为一个动态随机网络控制问题。我们将使用等待作业的队列长度信息作为反馈信号，以做出资源分配决策，如将作业路由到机器上，以及在机器上调度作业。为此，我们将回答一些基本问题：云的稳定区域是什么？计算复杂性和稳定性之间是否存在权衡？除了稳定性之外，我们如何描述资源分配算法的性能？云提供商应该如何为其资源定价，以最大化社会福利或利润？从理论上看，该方法的新颖之处在于设计了控制和性能分析算法，同时考虑了计算复杂性。广泛的影响：PI教授跨越网络、游戏、控制理论和优化的研究生级课程。我们是首批将网络应用程序纳入控制课程和将控制应用程序纳入网络课程的公司之一。拟议的项目为控制理论方法论开辟了一个新的应用领域，即云计算，从而为这种相互作用提供了新的机会。私人投资机构在为来自代表性不足群体的本科生和研究生提供建议方面有很好的记录。我们也将继续从这样的学生团体中为这个项目进行招募工作。我们还将在适用的情况下利用特定的机会，例如NSF联盟研究生教育和教授计划(AGEP)，这是爱荷华州大学为招收少数族裔学生所做的协调努力，以及研究生少数族裔援助计划(GMAP)，它为招募少数族裔学生提供资金，获得研究援助奖学金。

Cloud computing services (such as Amazon EC2 system, Google AppEngine, and Microsoft Azure) are becoming ubiquitous and are starting to serve as the primary source of computing power for both enterprises and personal computing applications. A cloud computing platform (or simply, a cloud) can provide a variety of resources, including infrastructure, software, and services, to users in an on-demand fashion. Compared to traditional own-and-use approaches, cloud computing services eliminate the costs of purchasing and maintaining the infrastructures for cloud users, and allow the users to dynamically scale up and down computing resources in real time based on their needs.A cloud consists of a number of machines (computers), each with a certain amount of resources (CPU, RAM, hard disk space, etc.). Each machine can be subdivided into virtual machines, where each virtual machine (VM) behaves like a small machine with a certain amount of dedicated resources. When a user submits a job to the cloud, he or she requests a certain amount of resources from the cloud and the cloud responds by creating a VM with the required resources in a machine. The resource allocation problem is to figure out how to allocate jobs to machines. Further, when several jobs are waiting for service, the cloud must also decide which job to select for service next. The goal of this project is to design resource allocation algorithms for efficient operation of the cloud, and to design pricing mechanisms to maximize the cloud service provider revenue while providing good quality of service to competing users.Intellectual Merit: The prior art in this area is to view the problem as a sequence of static problems as follows: consider the jobs that are currently waiting for service and allocate them to machines by solving a combinatorial optimization problem. Static approaches which ignore the dynamic nature of the system lead to instability. Our viewpoint here is fundamentally different: we consider the resource allocation problem as a dynamic stochastic network control problem. We will use queue length information about waiting jobs as the feedback signal to take resource allocation decisions such as routing jobs to machines and scheduling jobs on machines. To this end, we will answer a number of fundamental questions: what is the stability region of a cloud? ; is there a tradeoff between computational complexity and stability? ; how can we characterize the performance of resource allocation algorithms beyond stability? ; and how should a cloud provider price its resources for maximizing social welfare or profit? From a theoretical perspective, the novelty in the proposed approach lies in the design of control and performance analysis algorithms while taking computational complexity considerations in account.Broader Impact: The PIs teach graduate-level courses spanning networks, games, control theory, and optimization. We were among the first to incorporate network applications in control courses and control applications in networking classes. The proposed project provides new opportunities for such cross-fertilization by opening up a new application area, namely cloud computing, for control-theoretic methodologies. The PIs have a strong record of advising undergraduate students and graduate students from underrepresented groups. We will continue our recruitment efforts from such student groups for this project also. We will also use specific opportunities for this purpose as applicable, such as the NSF Alliance Graduate Education and the Professoriate (AGEP) program, which is a coordinated effort by Iowa universities to recruit minorities, and the Graduate Minority Assistantship Program (GMAP), which provides funds for recruiting minority students on research assistantships.