Multi-Agent Fog Computing Model for Healthcare Critical Tasks Management
A.A., Mutlag, . 2020
In healthcare applications, numerous sensors and devices produce massive amounts of
data which are the focus of critical tasks. Their management at the edge of the network can be done
by Fog computing implementation. However, Fog Nodes su er from lake of resources That could
limit the time needed for final outcome/analytics. Fog Nodes could perform just a small number of
tasks. A dicult decision concerns which tasks will perform locally by Fog Nodes. Each node should
select such tasks carefully based on the current contextual information, for example, tasks’ priority,
resource load, and resource availability. We suggest in this paper a Multi-Agent Fog Computing
model for healthcare critical tasks management. The main role of the multi-agent system is mapping
between three decision tables to optimize scheduling the critical tasks by assigning tasks with their
priority, load in the network, and network resource availability. The first step is to decide whether
a critical task can be processed locally; otherwise, the second step involves the sophisticated selection
of the most suitable neighbor Fog Node to allocate it. If no Fog Node is capable of processing the task
throughout the network, it is then sent to the Cloud facing the highest latency. We test the proposed
scheme thoroughly, demonstrating its applicability and optimality at the edge of the network using
iFogSim simulator and UTeM clinic data.
