Abstract
Fog computing is a new computing architecture, composed of a set of near-user edge devices called fog nodes, which collaborate together in order to perform computational services such as running applications, storing a signicant amount of data, and transmitting messages. Fog computing extends cloud computing by deploying digital resources at the premise of mobile devices and smart objects. In this new paradigm, management and operating functions, such as job scheduling aim at providing high-performance, cost-effective services requested by mobile devices and smart objects and executed by fog nodes. I propose a new bio-inspired optimization approach called Task Distribution Algorithm aimed at addressing the job scheduling problem in the fog computing environment. Our proposed approach is based on the optimized distribution of a set of tasks among all the fog computing nodes. The objective is to find an optimal tradeoff between CPU execution time and allocated memory required by fog computing services established by mobile devices and smart objects.
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