  In this paper we describe matching mechanisms for a real-time computational
resource exchange market, Chital, that incentivizes participating clients to
perform computation for their peers in exchange for overall improved
performance. The system is designed to discourage dishonest behavior via a
credit system, while simultaneously minimizing the use of dedicated computing
servers and the number of verifications performed by the administrating
servers. We describe the system in the context of a pre-existing system (under
development), Vedalia \cite{715Project}, for analyzing and visualizing product
reviews, by using machine learning such as topic models. We extend this context
to general computing tasks, describe a list of matching algorithms, and
evaluate their performance in a simulated environment. In addition, we design a
matching algorithm that optimizes the amount of time a participant could save
compared to computing a task on their own, and show empirically that this
algorithm results in a situation in which it is almost always optimal for a
user to join the exchange than do computation alone. Lastly, we use a top-down
approach to derive a theoretically near-optimal matching algorithm under
certain distributional assumptions on query frequency.
