Cost optimization of omnidirectional offloading in two-tier cloud–edge federated systems. (June 2023) - Kar, Binayak; Lin, Ying-Dar; Lai, Yuan-Cheng
Abstract: The use of the federation can exploit the advantages of cloud and edge computing technologies as the federation provides the facilities by which both can complement each other. However, certain services are needed to offload from clouds to edges, termed reverse offloading, and between edges, termed horizontal offloading. By considering the scenarios discussed above, in this paper, we propose a generic omnidirectional (OMNI) architecture of cloud–edge computing systems intending to provide vertical (edge to cloud offloading, and vice versa), and horizontal (between edges) offloading. To investigate the effectiveness of the proposed architecture in different operational scenarios, we formulate the cost optimization problem with different latency (loose, low, ultra-low) constraints. We develop an offloading algorithm using simulated annealing named the two-tier simulated annealing (TTSA) algorithm. We set two criteria for offloading: (1) offloading based on the job's size and (2) offloading based on the job's priority, irrespective of its size. The experimental results show that our proposed OMNI architecture can reduce the total cost by 7%–10%, compared to other existing architectures, and our proposed TTSA algorithm can reduce the cost by 45%–55%, compared to other existing algorithms. The average latency in OMNI architecture is relatively very less compared to other architectures.