  This paper is motivated by emerging edge computing systems which consist of
sensor nodes that acquire and process information and then transmit status
updates to an edge receiver for possible further processing. As power is a
scarce resource at the sensor nodes, the system is modeled as a tandem
computation-transmission queue with power-efficient computing. Jobs arrive at
the computation server with rate $\lambda$ as a Poisson process with no
available data buffer. The computation server can be in one of three states:
(i) OFF: the server is turned off and no jobs are observed or processed, (ii)
ON-Idle: the server is turned on but there is no job in the server, (iii)
ON-Busy: the server is turned on and a job is processed in the server. These
states cost zero, one and $p_c$ units of power, respectively. Under a long-term
power constraint, the computation server switches from one state to another in
sequence: first a deterministic $T_o$ time units in OFF state, then waiting for
a job arrival in ON-Idle state and then in ON-Busy state for an independent
identically distributed compute time duration. The transmission server has a
single unit data buffer to save incoming packets and applies last come first
serve with discarding as well as a packet deadline to discard a sitting packet
for maintaining information freshness, which is measured by the Age of
Information (AoI). Additionally, there is a monotonic functional relation
between the mean time spent in ON-Busy state and the mean transmission time. We
obtain closed-form expressions for average AoI and average peak AoI. Our
numerical results illustrate various regimes of operation for best AoI
performances optimized over packet deadlines with relation to power efficiency.
