  We consider an outsourcing problem where a software agent procures multiple
services from providers with uncertain reliabilities to complete a
computational task before a strict deadline. The service consumer requires a
procurement strategy that achieves the optimal balance between success
probability and invocation cost. However, the service providers are
self-interested and may misrepresent their private cost information if it
benefits them. For such settings, we design a novel procurement auction that
provides the consumer with the highest possible revenue, while giving
sufficient incentives to providers to tell the truth about their costs. This
auction creates a contingent plan for gradual service procurement that suggests
recruiting a new provider only when the success probability of the already
hired providers drops below a time-dependent threshold. To make this auction
incentive compatible, we propose a novel weighted threshold payment scheme
which pays the minimum among all truthful mechanisms. Using the weighted
payment scheme, we also design a low-complexity near-optimal auction that
reduces the computational complexity of the optimal mechanism by 99% with only
marginal performance loss (less than 1%). We demonstrate the effectiveness and
strength of our proposed auctions through both game theoretical and numerical
analysis. The experiment results confirm that the proposed auctions exhibit 59%
improvement in performance over the current state-of-the-art, by increasing
success probability up to 79% and reducing invocation cost by up to 11%.
