  The demand for real-time cloud applications has seen an unprecedented growth
over the past decade. These applications require rapidly data transfer and fast
computations. This paper considers a scenario where multiple IoT devices update
information on the cloud, and request a computation from the cloud at certain
times. The time required to complete the request for computation includes the
time to wait for computation to start on busy virtual machines, performing the
computation, waiting and service in the networking stage for delivering the
output to the end user. In this context, the freshness of the information is an
important concern and is different from the completion time. This paper
proposes novel scheduling strategies for both computation and networking
stages. Based on these strategies, the age-of-information (AoI) metric and the
completion time are characterized. A convex combination of the two metrics is
optimized over the scheduling parameters. The problem is shown to be convex and
thus can be solved optimally. Moreover, based on the offline policy, an online
algorithm for job scheduling is developed. Numerical results demonstrate
significant improvement as compared to the considered baselines.
