Evaluating Bio-Inspired Optimization Techniques for Utility Price Estimation in Fog Computing

With continuous advancements in the domain of information and communication technologies, large-scale enterprises and the Internet service providers have realized about the limited capacity of data storage available to them. This leads to the concept of sharing of resources among the enterprises by renting these services through the Service Level Agreements (SLAs). Fog Computing is proposed to be an extension of cloud computing architecture where the resources are brought near the end users. Fog computing, in contrast to cloud computing provides services along with the computing resources. To borrow the services, the enterprises have to pay according to their usage of the services or resources. In this paper, two nature inspired algorithms are compared to determine the efficient management of resources so that the cost of resources can be minimized and the billing can be achieved through calculation of the utilized resources. Pigeon Inspired Optimization (PIO) and Enhanced Differential Evolution (EDE) are used to determine the energy consumed by the cloudlets or edge nodes that in turn can be used for estimating the bill through the Time of Use pricing variable. We evaluate both of the aforementioned technique to determine their performance regarding the bill calculation on the basis of usage of fog servers. Simulation results demonstrate that PIO gives significantly better results than EDE in terms of resource utilization whereas for bill reduction EDE outperforms PIO based technique.