We present CCM (Cloud Capacity Manager) - a prototype system and its methods for dynamically multiplexing the compute capacity of virtualized datacenters at scales of thousands of machines, for diverse workloads with variable demands. Extending prior studies primarily concerned with accurate capacity allocation and ensuring acceptable application performance, CCM also sheds light on the tradeoffs due to two unavoidable issues in large scale commodity datacenters: (i) maintaining low operational overhead given variable cost of performing management operations necessary to allocate resources, and (ii) coping with the increased incidences of these operations' failures. CCM is implemented in an industry-strength cloud infrastructure built on top of the VMware vSphere virtualization platform and is currently deployed in a 700 physical host datacenter. Its experimental evaluation uses production workload traces and a suite of representative cloud applications to generate dynamic scenarios. Results indicate that the pragmatic cloud-wide nature of CCM provides up to 25% more resources for workloads and improves datacenter utilization by up to 20%, compared to the common alternative approach of multiplexing capacity within multiple independent smaller datacenter partitions.

Workload consolidation is a key technique in reducing costs in virtualized datacenters. When considering storage consolidation, a key problem is the unpredictable performance behavior of consolidated workloads on a given storage system. In practice, this often forces system administrators to grossly overprovision storage to meet application demands. In this paper, we show that existing modeling techniques are inaccurate and ineffective in the face of heterogenous devices. We introduce Romano, a storage performance management system designed to optimize truly heterogeneous virtualized datacenters. At its core, Romano constructs and adapts approximate workload-specific performance models of storage devices automatically, along with prediction intervals. It then applies these models to allow highly efficient IO load balancing.

Virtualized datacenters strive to reduce costs through workload consolidation. Workloads exhibit a diverse set of IO behaviors and varying IO load that makes it difficult to estimate the IO performance on shared storage. As a result, system administrators often resort to gross overprovisioning or static partitioning of storage to meet application demands. In this paper, we introduce Pesto, a unified storage performance management system for heterogeneous virtualized datacenters. Pesto is the first system that completely automates storage performance management for virtualized datacenters, providing IO load balancing with cost-benefit analysis, per-device congestion management, and initial placement of new workloads.

Storage management in virtualized environments is considered as one of the biggest cost factors. According to some estimates, majority of the cost and performance problems are related to storage devices. In this tutorial, we will discuss some of the key storage technologies deployed in virtual datacenters. We will discuss a set of unique challenges faced by administrators and users due to increasing number of layers of abstraction. Next we will discuss some tools and techniques to do workload characterization and monitor devices in order to understand and trouble-shoot IO problems. We will also present some of the recent solutions proposed by industry and academia to handle these problems followed by upcoming technological trends and directions for future research.