  In the Internet of Things (IoT) environment, edge computing can be initiated
at anytime and anywhere. However, in an IoT, edge computing sessions are often
ephemeral, i.e., they last for a short period of time and can often be
discontinued once the current application usage is completed or the edge
devices leave the system due to factors such as mobility. Therefore, in this
paper, the problem of ephemeral edge computing in an IoT is studied by
considering scenarios in which edge computing operates within a limited time
period. To this end, a novel online framework is proposed in which a source
edge node offloads its computing tasks from sensors within an area to
neighboring edge nodes for distributed task computing, within the limited
period of time of an ephemeral edge computing system. The online nature of the
framework allows the edge nodes to optimize their task allocation and decide on
which neighbors to use for task processing, even when the tasks are revealed to
the source edge node in an online manner, and the information on future task
arrivals is unknown. The proposed framework essentially maximizes the number of
computed tasks by jointly considering the communication and computation
latency. To solve the problem, an online greedy algorithm is proposed and
solved by using the primal-dual approach. Since the primal problem provides an
upper bound of the original dual problem, the competitive ratio of the online
approach is analytically derived as a function of the task sizes and the data
rates of the edge nodes. Simulation results show that the proposed online
algorithm can achieve a near-optimal task allocation with an optimality gap
that is no higher than 7.1% compared to the offline, optimal solution with
complete knowledge of all tasks.
