With the fast development of cloud computing methods, exponential growth is faced by
several users. It is complicated for traditional data centers to perform several jobs in real-time because
of inadequate bandwidth resources. Therefore, the method of fog computing is recommended
for supporting and providing fast cloud services. It is not a substitute but is a powerful complement
to cloud computing. The reduction of energy consumption through the notion of fog
computing has certainly been a challenge for current researchers, industries, and communities.
Various industries, including finance and healthcare, require a rich resource-based platform for
processing large amounts of data with cloud computing across fog architecture. The consumption
of energy across fog servers relies on allocating techniques for services (user requests). It facilitates
processing at the edge with the probability of interacting with the cloud. This article proposed
energy-aware scheduling by using Artificial Neural Network (ANN) and Modified Multiobjective
Job Scheduling (MMJS) techniques. The emphasis of the work is on the reduction of energy
consumption rate with less Service Level Agreement (SLA) violation in fog computing for
data centers. The result shows that there is a 3.9% reduction in SLA violation when a multiobjective
function with Artificial Neural Network is applied.
Keywords: Fog computing, service level agreement, energy consumption, artificial neural network, modified multi-objective
job scheduling, bandwidth.
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