  In multi-source status update systems, sources need to be scheduled
appropriately to maintain timely communication between each of the sources and
the monitor. A cyclic schedule is an age-agnostic schedule in which the sources
are served according to a fixed finite transmission pattern, which upon
completion, repeats itself. Such a scheme has a low $O(1)$ runtime complexity,
which is desirable in large networks. This paper's focus is on designing
transmission patterns so as to be used in massive scale networking scenarios
involving a very large number of sources, e.g., up to thousands of IoT sources,
with service time requirements and weights being heterogeneous in nature. The
goal is to minimize the weighted sum age of information (AoI), called weighted
AoI, when transmitting users' packets over a channel susceptible to
heterogeneous packet errors. The main tool we use is a stochastic modeling
framework using either Markov chains (MC) or moment generating functions (MGF),
by which we obtain the weighted AoI for a given transmission pattern, which is
not straightforward in the presence of packet drops. Using this framework, we
provide a lower bound on the weighted AoI for the particular case of two
sources, and also an algorithm to attain this lower bound. Then, by using the
same framework, we design a cyclic scheduler for general number of sources with
reasonable complexity using convex optimization and well-established packet
spreading algorithms, and comparatively evaluate the proposed algorithm and
existing age-agnostic scheduling schemes for general number of sources
(resp.~two sources) when the lower bound is not available (resp.~when it is
available). We present extensive numerical results to validate the
effectiveness of the proposed approach.
