Evaluation and Analysis of Bio-Inspired Optimization Techniques for Bill Estimation in Fog Computing

In light of constant developments in the realm of Information Communication and Technologies, large-scale busi-nesses and Internet service providers have realized the limitation of data storage capacity available to them. This led organizations to cloud computing, a concept of sharing of resources among different service providers by renting these resources through service level agreements. Fog computing is an extension to cloud computing architecture in which resources are brought closer to the consumers. Fog computing, being a distinct from cloud computing as it provides storage services along with computing resources. To use these services, the organizations have to pay according to their usage. In this paper, two nature-inspired algorithms, i.e. Pigeon Inspired Optimization (PIO) and Binary Bat Algorithm (BBA) are compared to regulate the effective management of resources so that the cost of resources can be curtailed and billing can be achieved by calculating utilized resources under the service level agreement. PIO and BBA are used to evaluate energy utilization by cloudlets or edge nodes that can be used subsequently for approximating the utilization and bill through a Time of Use pricing scheme. We appraise above-mentioned techniques to evaluate their performance concerning the bill estimation based on the usage of fog servers. With respect to the utilization of resources and reduction in the bill, simulation results have revealed that the BBA gives pointedly better results than PIO.

@article{Arshad2018,
  title     = {Evaluation and Analysis of Bio-Inspired Optimization Techniques for Bill Estimation in Fog Computing},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {9},
  number    = {7},
  year      = {2018},
  publisher = {The Science and Information Organization},
  author    = {Hafsa Arshad and Hasan Ali Khattak and Munam Ali Shah and Assad Abbas and Zoobia Ameer},
  doi       = {10.14569/IJACSA.2018.090727},
  url       = {https://doi.org/10.14569/IJACSA.2018.090727}
}