Last decade, providers started using Virtual Machines (VMs) in their datacenters to pack users and their applications. This was a good way to consolidate multiple users in fewer physical nodes while isolating them from each other. Later on in 2006, Amazon started offering their Infrastructure as a Service where their users rent computing resources as VMs in a pay-as-you-go manner. However, virtualized providers cannot be managed like traditional ones as they are now confronted with a set of new challenges. First of all, providers must deal efficiently with new management operations such as the dynamic creation of VMs. These operations enable new capabilities that were not there before, such as moving VMs across the nodes, or the ability to checkpoint VMs. We propose a Decentralized virtualization management infrastructure to create VMs on demand, migrate them between nodes, and checkpointing mechanisms. With the introduction of this infrastructure, virtualized providers become decentralized and are able to scale. Secondly, these providers consolidate multiple VMs in a single machine to more efficiently utilize resources. Nevertheless, this is not straightforward and implies the use of more complex resource management techniques. In addition, this requires that both customers and providers can be confident that signed Service Level Agreements (SLAs) are supporting their respective business activities to their best extent. Providers typically offer very simple metrics that hinder an efficient exploitation of their resources. To solve this, we propose mechanisms to dynamically distribute resources among VMs and a resource-level metric, which together allow increasing provider utilization while maintaining Quality of Service. Thirdly, the provider must allocate the VMs evaluating multiple facets such as power consumption and customers' requirements. In addition, it must exploit the new capabilities introduced by virtualization and manage its overhead. Ultimately, this VM placement must minimize the costs associated with the execution of a VM in a provider to maximize the provider's profit. We propose a new scheduling policy that places VMs on provider nodes according to multiple facets and is able to understand and manage the overheads of dealing with virtualization. And fourthly, resource provisioning in these providers is a challenge because of the high load variability over time. Providers can serve most of the requests owning only a restricted amount of resources but this under-provisioning may cause customers to be rejected during peak hours. In the opposite situation, valley hours incur under-utilization of the resources. As this new paradigm makes the access to resources easier, providers can share resources to serve their loads. We leverage a federated scenario where multiple providers share their resources to overcome this load variability. We exploit the federation capabilities to create policies that take the most convenient decision depending on the environment conditions and tackle the load variability. All these challenges mean that providers must manage their virtualized resources in a different way than they have done traditionally. This dissertation identifies and studies the challenges faced by virtualized provider that offers IaaS, and designs and evaluates a solution to manage the provider's resources in the most cost-effective way by exploiting the virtualization capabilities.