  Fog computing is introduced by shifting cloud resources towards the users'
proximity to mitigate the limitations possessed by cloud computing. Fog
environment made its limited resource available to a large number of users to
deploy their serverless applications, composed of several serverless functions.
One of the primary intentions behind introducing the fog environment is to
fulfil the demand of latency and location-sensitive serverless applications
through its limited resources. The recent research mainly focuses on assigning
maximum resources to such applications from the fog node and not taking full
advantage of the cloud environment. This introduces a negative impact in
providing the resources to a maximum number of connected users. To address this
issue, in this paper, we investigated the optimum percentage of a user's
request that should be fulfilled by fog and cloud. As a result, we proposed
DeF-DReL, a Systematic Deployment of Serverless Functions in Fog and Cloud
environments using Deep Reinforcement Learning, using several real-life
parameters, such as distance and latency of the users from nearby fog node,
user's priority, the priority of the serverless applications and their resource
demand, etc. The performance of the DeF-DReL algorithm is further compared with
recent related algorithms. From the simulation and comparison results, its
superiority over other algorithms and its applicability to the real-life
scenario can be clearly observed.
