With the rapid advances in networks and the world move towards global society, modern networks like cloud and fog computing, Internet of Things (IoT) becomes very popular. As a result, new types of potential problems are introduced. One of the key challenging problems that are emerging with the evolution of modern networks is how to effectively utilize energy at different levels, e.g., data centers hosting cloud applications. Energy utilization imposes a new level of complexity to new generations of networks.
Energy utilization means the ability to minimize energy consumption, such that the quantity of energy consumed is at an adequate level. Significant research has been devoted to providing solutions to this vital problem. The majority of the studies, however, attempt to solve the problem in terms of an optimized scheduling for the resources involved in communication networks and hence, the energy is saved.
Methods that make use of the IoT have sustainably proved to be effective as the former is able to provide high performance across heterogeneous systems.
However, difficulties posed in IoT like security, privacy and reliability are always expended. On the other hand, Cloud Computing has unlimited capabilities in terms of storage, processing power, and reliability. In the context that it was developed, it involves several computing ideas ranging from services to the underlying structure of networks. Recently, research combines the advantages of both IoT and cloud computing in what is called CloudIoT, while attempting to minimize their drawbacks. CloudIoT is flexible enough to support different types of services and data among heterogeneous networks and it gains increasing popularity in the recent years.
As a result to this advanced technology new critical problems are imposed. Examples include, but are not limited to, protocol support, energy efficiency, resource allocation, and location of data storage.
Since estimating minimum energy consumption in CloudIoT is a relatively complex problem, it is often to attempt to solve the dilemma in terms of optimization techniques. From that prospective, the problem is defined as an objective function, for which we seek its feasible and optimal solution. Genetic Algorithms (GA) are ones of the proposed solutions for handling energy consumption problem in particular and optimization problems in general. The underlying assumption behind GA is that combining exceptional characteristics from different ancestors would likely generate better and optimized offspring, which in turn could have an improved fitness than the original ancestors. So, if the technique is iteratively implemented, offspring would be more optimized, and thus resulting into higher sustainability in the environment they are operated.
In this paper, we proposed a new method for energy efficiency in CloudIoT. The method is built on the top of the GA. Our method is computing execution time and energy consumption to aims reducing the energy consumption of application requests.