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The internet of everything is a network that connects people, data, process, and things, making it easier to understand that many subfields of knowledge are discussable while addressing this subject. This chapter makes a survey on the application of machine learning algorithms to the internet of everything. This survey is particularly focused in computational frameworks for the development of intelligent systems and applications of machine learning algorithms as possible engines of wealth creation. A final example shows how to develop a simple end-to-end system.

Energy consumption and, consequently, the associated costs (e.g., environmental and monetary) concern most individuals, companies, and institutions. Platforms for the monitoring, predicting, and optimizing energy consumption are an important asset that can contribute to the awareness about the ongoing usage levels, but also to an effective reduction of these levels. A solution is to leave the decisions to smart system, supported for instance in machine learning and optimization algorithms. This chapter involves those aspects and the related fields with emphasis in the prediction of energy consumption to optimize its usage policies.

In the present-day generation, cloud computing is getting more and more common. Additionally, every day more and more users are accessing cloud resources. Cloud computing enables end users to take advantage of a collection of distributed hosted resources such as storage, CPU, applications (both software and hardware), and services via the internet on an as-needed basis. There are many power-conscious approaches that attempt to minimize power consumption and host service level agreement (SLA) degradation. In this paper our plan is to study the performance of host machine underloading algorithm in cloud data center.

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