Energy-Aware Load Balancing in a Cloudlet Federation

Abstract

With the rapid increase in computation-intensive tasks, the current research task is to minimize energy consumption due to resource constraints and increased cost. For complex computations where multiple computer systems are required to execute a single task such as in a federated cloudlet environment, load balancing is the main challenge. Load balancing means dividing the total workload between all the present nodes to obtain the maximum benefits from the available resources and to minimize energy consumption. A cloudlet is a resourceful computer that is coupled to the Internet and is accessible for mobile devices in their vicinity. A Cloudlet Federation is the concept of a cooperative framework to share resources and load balancing among various cloudlets. Different tasks consume different amounts of energy for their execution, which results in a large amount of heat dissipation. Due to heat, the performance of the systems is decreased. The more heat is present, the more the performance degrades. To address this problem, this paper proposes a novel scheduling strategy that will assign incoming tasks to systems according to their energy consumption level. The proposed methodology is tested in a Cloudlet Federation environment and the results show improved load balancing in terms of energy consumption and heat dissipation.

A lightweight deep learning approach for COVID-19 detection using X-ray images with edge federation

“…If somehow the broker is not working, any edge node can use the local information matrix previously pushed by the broker thus eliminating the chances of failure. 1 Next, the task is offloaded from the connected node to the optimal edge node. The task can be offloaded in the form of workload, code, or virtual machine.…”

“…However, algorithms used in such solutions often require high computational resources and consume a substantial amount of energy. 1 The existing solutions implement a variety of hybrid deep-learning (DL) algorithms to detect COVID-19 using X-ray images. Promising results are reported in several studies but complex DL algorithms require additional computational resources and the time to train such algorithms is also high.…”