In this paper, a mathematical model is framed to optimize resource consumption using game-theory.
The adaptation of energy production to demand has been traditionally very important for utilities in order to optimize resource consumption.
Contemporary cloud computing research has seldom focused on solutions that provide both high availability and real-time assurance to these applications in a way that also optimizes resource consumption in data centers, which is a key consideration for cloud providers.
The program's goals include optimizing resource use while significantly improving worker productivity, safety, turnover and overhaul of compensation packages.
Realizing high-quality digital image transmission via a satellite link, while optimizing resource distribution and minimizing battery consumption, is a challenging task.
This control system simultaneously optimizes resource energy consumption by limiting energy wastage through the real-time control of resource states, and by dynamically controlling the overall power consumption taking the limited availability of energy into consideration.
As a unique approach designed for off-the-cloud use and by service users, OCSO is capable of achieving distinguished effectiveness in optimizing service resource consumption from a new perspective.
In this article, we propose a cross-layer video adaptation solution, which may be used for optimizing network resource consumption and user experienced quality of video streaming in wireless networks; thus improving the availability of video services to mobile users.
The objective of this paper is to optimize resource allocation using an improved clonal selection algorithm (ICSA) based on makespan optimization and energy consumption models in cloud computing environment.
Smart cities have the capability of reducing resource consumption, using IoT devices to optimize electricity and water usage, reducing traffic congestion and air pollution through traffic light monitoring, and much more.
