Abstract

The recent growth in intensive services and applications demand has triggered the functional integration of cloud computing with edge computing capabilities. One of the main goals is to allow a fast processing to tasks with strict real time constraints in order to lower the task dropping probability due to expiration of the associated deadlines. This paper deals with the performance evaluation and optimization of a three layers cloud-fog-edge computing infrastructure by resorting to the use of queueing theory results. In particular, a Markov queueing system model with reneging is proposed for the cloud subsystem, in order to consider the premature computation requests departure due to their deadline expiration. Furthermore, a computational resources allocation method is proposed with the aim at maximizing the social welfare metric, constrained to specific quality of service requirements. Finally, the proposed queueing theory analysis as well as of the computational resources allocation approach is validated by comparing the obtained analytical predictions with simulation results.

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