As a key component in a modern datacenter, the cloud operating system is responsible for managing the physical and virtual infrastructure, orchestrating and commanding service provisioning and deployment, and providing federation capabilities for accessing and deploying virtual resources in remote cloud infrastructures.

Cloud computing will play a major role in the future Internet of Services, enabling on-demand provisioning of applications, platforms, and computing infrastructures. However, the cloud community must address several technology challenges to turn this vision into reality. Specific issues relate to deploying future infrastructure-as-a-service clouds and include efficiently managing such clouds to deliver scalable and elastic service platforms on demand, developing cloud aggregation architectures and technologies that let cloud providers collaborate and interoperate, and improving cloud infrastructures' security, reliability, and energy efficiency.

Automated resource provisioning techniques enable the implementation of elastic services, by adapting the available resources to the service demand. This is essential for reducing power consumption and guaranteeing QoS and SLA fulfillment, especially for those services with strict QoS requirements in terms of latency or response time, such as web servers with high traffic load, data stream processing, or real-time big data analytics. Elasticity is often implemented in cloud platforms and virtualized data-centers by means of auto-scaling mechanisms. These make automated resource provisioning decisions based on the value of specific infrastructure and/or service performance metrics. This paper presents and evaluates a novel predictive auto-scaling mechanism based on machine learning techniques for time series forecasting and queuing theory. The new mechanism aims to accurately predict the processing load of a distributed server and estimate the appropriate number of resources that must be provisioned in order to optimize the service response time and fulfill the SLA contracted by the user, while attenuating resource over-provisioning in order to reduce energy consumption and infrastructure costs. The results show that the proposed model obtains a better forecasting accuracy than other classical models, and makes a resource allocation closer to the optimal case.

In this paper we analyze the deployment of generic clustered services on top of a virtualized infrastructure layer that combines a VM manager (the OpenNebula engine) and a cloud resource provider (Amazon EC2). The use of this virtualization layer between the service and the physical infrastructure extends the classical benefits of VM platforms to distributed infrastructures. Additionally, the integration of the cloud in this layer allows us to give additional capacity to the services using an external provider, thus complementing the local infrastructure without notice from the users or affecting the service workload. This flexible approach, which separates the resource provisioning from the service management, provides important benefits: elastic service capacity to adapt it to its dynamic workload; physical infrastructure partitioning to isolate it from other running services; and support for heterogeneous configurations tailored for each service class. The feasibility of the proposed approach is analyzed for two different clustered services: a classical computing cluster and a web server.