System Support for Next Generation Cloud Computing

云计算提供了一种强调按使用付费和供应弹性的实用模型。不幸的是，为了充分利用云的潜力，现有的云缺乏广泛应用程序所需的必要的系统级机制。这项提议的研究扩大了可以利用云计算的应用程序和服务的范围。我们的第一个研究重点是帮助用户扩大和缩小其应用程序的足迹，以跟踪需求的瞬时变化。不幸的是，在当前的云中实例化新的虚拟机（VM）是一个缓慢的操作（通常需要“几分钟”），并且新的VM要么是新启动的，要么是模板VM的副本，不知道当前应用程序的状态。类似地，当前的云缺乏在应用程序需求减少时简化缩减过程的机制。拟议的研究将创建新的机制，提供必要的伸缩灵活性，使用户能够充分利用云模型的潜力。我们的第二个研究推力使得在流行数据集上操作的数据密集型计算的有效执行成为可能。虽然虚拟化提供了许多好处（例如，安全性、易于管理以及在程序员配置的自定义环境中运行的灵活性），但它限制了数据重用的机会，因为每个VM必须下载自己的数据副本。我们的研究将使跨虚拟机的有效文件共享成为可能，并将优化虚拟机放置以最大化局部性。

Cloud computing provides a utility model that emphasizes pay-per-use and elasticity of provisioning. Unfortunately, existing clouds lack necessary system level mechanisms required by a broad set of applications in order to make full use of the potential of the cloud. The proposed research broadens the set of applications and services that can take advantage of cloud computing. Our first research thrust helps users scale up and down their application's footprint to track instantaneous variations in demand. Unfortunately, instantiating new virtual machines (VM) in current clouds is a slow operation (typically taking "minutes"), and the new VMs originate either as fresh boots or replicas of a template VM, unaware of the current application state. Similarly, current clouds lack mechanisms that simplify the process of scaling down when the demand for an application recedes. The proposed research will create new mechanism that provide the necessary scale-up and scale-down agility to enable users to take full advantage of potential of the cloud model. Our seconds research thrust makes possible the efficient execution of data intensive computations that operate on popular data sets. While virtualization provides many benefits (e.g, security, ease of management, and the flexibility of running in a custom environment configured by the programmer), it limits opportunities for data reuse as each VM has to download its own copy of the data. Our research will make possible efficient file sharing across VMs, and will optimize VM placement to maximize locality.