  Most existing stochastic models on age of information (AoI) focus on a single
shared server serving status update packets from $N>1$ sources where each
packet update stream is Poisson, i.e., single-hop scenario. In the current
work, we study a two-hop edge computing system for which status updates from
the information sources are still Poisson but they are not immediately
available at the shared edge server, but instead they need to first receive
service from a transmission server dedicated to each source. For exponentially
distributed and heterogeneous service times for both the dedicated servers and
the edge server, and bufferless preemptive resource management, we develop an
analytical model using absorbing Markov chains (AMC) for obtaining the
distribution of AoI for any source in the system. Moreover, for a given tagged
source, the traffic arriving at the shared server from the $N-1$ un-tagged
sources, namely the interference traffic, is not Poisson any more, but is
instead a Markov modulated Poisson process (MMPP) whose state space grows
exponentially with $N$. Therefore, we propose to employ a model reduction
technique that approximates the behavior of the MMPP interference traffic with
two states only, making it possible to approximately obtain the AoI statistics
even for a very large number of sources. Numerical examples are presented to
validate the proposed exact and approximate models.
