Multi-criteria optimal task allocation at the edge

In Internet of Things (IoT), numerous nodes produce huge volumes of data that are the subject of various
processing tasks. Tasks execution on top of the collected data can be realized either at the edge of the
network or at the Fog/Cloud. Their management at the network edge may limit the required time for
concluding responses and return the final outcome/analytics to end-users or applications. IoT nodes, due
to their limited computational and resource capabilities, can execute a limited number of tasks over the
collected contextual data. A challenging decision is related to which tasks the IoT nodes should execute
locally. Each node should carefully select such tasks to maximize the performance based on the current
contextual information, e.g., tasks’ characteristics, nodes’ load and energy capacity. In this paper, we
propose an intelligent decision making scheme for selecting the tasks that will be locally executed. The
remaining tasks will be transferred to peer nodes in the network or the Fog/Cloud. Our focus is to limit the
time required for initiating the execution of each task by introducing a two-step decision process. The first
step is to decide whether a task can be executed locally; if not, the second step involves the sophisticated
selection of the most appropriate peer to allocate it. When, in the entire network, no node is capable of
executing the task, it is, then, sent to the Fog/Cloud facing the maximum latency. We comprehensively
evaluate the proposed scheme demonstrating its applicability and optimality at the network edge.