  In status update scenarios, the freshness of information is measured in terms
of age-of-information (AoI), which essentially reflects the timeliness for
real-time applications to transmit status update messages to a remote
controller. For some applications, computational expensive and time consuming
data processing is inevitable for status information of messages to be
displayed. Mobile edge servers are equipped with adequate computation resources
and they are placed close to users. Thus, mobile edge computing (MEC) can be a
promising technology to reduce AoI for computation-intensive messages. In this
paper, we study the AoI for computation-intensive messages with MEC, and
consider three computing schemes: local computing, remote computing at the MEC
server, and partial computing, i.e., some part of computing tasks are performed
locally, and the rest is executed at the MEC server. Zero-wait policy is
adopted in all three schemes. Specifically, in local computing, a new message
is generated immediately after the previous one is revealed by computing. While
in remote computing and partial computing, a new message is generated once the
previous one is received by the remote MEC server. With infinite queue size and
exponentially distributed transmission time, closed-form average AoI for
exponentially distributed computing time is derived for the three computing
schemes. For deterministic computing time, the average AoI is analyzed
numerically. Simulation results show that by carefully partitioning the
computing tasks, the average AoI in partial computing is the smallest compared
to local computing and remote computing. The results also indicate numerically
the conditions on which remote computing attains smaller average AoI compared
with local computing.
