The DOPPLER project: modelling and optimizing workloads for the cloud

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As organizations grow, their data centres inevitably become larger and more complex. These centres usually provide numerous services to the internal and external users; such services are commonly deployed on virtual machines (VMs) across a cluster of computing devices, which can either be the property of the organization (i.e., a "private cloud") or be rented from a public cloud provider such as Amazon EC2. In both cases, private or public, managing "the cloud" requires appropriate virtualization and cost-effective provisioning of the infrastructure with respect to the type and size of the service requests (i.e., the workload). The implementation of this virtualization and provisioning needs to be carefully considered. Over-estimating the required infrastructure for a set of requested services in a specified period of time leads to waste, under-utilization, and increased costs. At the same time, under-estimation of the future workload is also unacceptable, since it eventually degrades the quality of the services and may lead to violations of in the Service-Level Agreements (SLAs) with the clients. Minimizing the infrastructure costs, while at the same time ensuring that SLAs are satisfied, requires not only state-of-the-art techniques for resource assignment and load balancing but also a detailed understanding of the cloud workload itself.