Rapid growth is being witnessed by social life in all countries. It appears in the form of urbanization, establishment of numerous and varied factories, and manufacturing of products such as cars and devices emitting toxic emissions and radiations. In this context, cloud computing technology has come to reduce the energy consumed by electronic devices as well as the toxic gases emitted by them and has increased recycling of electronic waste.

Green (eco-friendly) cloud computing centers, which rely on renewable energy, are gaining much attention worldwide for their contribution to sustainable economic development. These hubs also attract an increasing number of companies to publish their applications and provide services to global users. With the rise in application tasks, energy consumption and costs have skyrocketed to up to 40% of the operating expenses of global distribution centers.

In this context, Dr. Ahmed Al-Ammari of the College of Engineering conducted a study to manage spatiotemporal renewable energy, schedule green cloud computing systems to promote green computing, and examine the proposed system in a manner that includes spatiotemporal variability of multiple factors. It also focused on low-latency application tasks, potential reduction of costs, maximization of renewable energy utilization, and improving system throughput without violating any delay constraints for all tasks.

Based on the observations in the above study, this researcher designed an architecture for distributed green cloud computing centers, so as to support cost scheduling and energy efficiency for limited-delay tasks across multiple heterogeneous applications. He also developed mathematical modelling for the same. He also relied on the G/D/1 queue system proposed by David Kendall, a numbering method to describe the characteristics of the queuing model for mathematical relationship modelling, in order to define accurate scheduling strategies and formulate a constrained, multi-objective optimization problem.

Further, this researcher developed multiple, innovative intelligent optimization algorithms to obtain near-perfect, quick, real-time solutions for the tasks. He collected data from various geographical sources (real task rates, network rate in distributed locations, solar radiation, wind speed, and stability of smart network power), while carrying out experimental and performance comparisons of the proposed algorithms. The optimal solutions thus obtained increased the use of renewable energy sources from varied geographical locations and reduced energy costs as well as negative environmental impacts, by considering spatiotemporal differences across multiple factors, such as the price and stability of smart grid energy, solar radiation, and wind speed.

This researcher pointed out that the completion of the above project would help create a digital economy in the Sultanate of Oman and enhance its information technology sector. The importance of information technology is increasing with the world moving towards the era of cloud computing, big data, IoT, smart systems, smart manufacturing, smart homes, and smart cities. Therefore, the development of green computing technologies is a crucial step for the Sultanate to position itself as a leading country in this field.