  This paper presents a systematic approach for implementing a class of
nonlinear signal processing systems as a distributed web service, which in turn
is used to solve optimization problems in a distributed, asynchronous fashion.
As opposed to requiring a specialized server, the presented approach requires
only the use of a commodity database back-end as a central resource, as might
typically be used to serve data for websites having large numbers of concurrent
users. In this sense the presented approach leverages not only the scalability
and robustness of various database systems in sharing variables asynchronously
between workers, but also critically it leverages the tools of signal
processing in determining how the optimization algorithm might be organized and
distributed among various heterogeneous workers. A publicly-accessible
implementation is also presented, utilizing Firebase as a back-end server, and
illustrating the use of the presented approach in solving various optimization
problems commonly arising in the context of signal processing.
