  As a cost-effective option for Cloud consumers, spot service has been
considered to be a significant supplement for building a full-fledged market
economy for the Cloud ecosystem. However, unlike the static and straightforward
way of trading on-demand and reserved Cloud services, the market-driven
regulations of employing spot service could be too complicated for Cloud
consumers to comprehensively understand. In particular, it would be both
difficult and tedious for potential consumers to determine suitable bids from
time to time. To reduce the complexity in applying spot resources, we propose
to use a feedback control to help make bidding decisions. Based on an
arccotangent-function-type system model, our novel bidding mechanism imitates
fuzzy and intuitive human activities to refine and issue new bids according to
previous errors. The validation is conducted by using Amazon's historical spot
price trace to perform a set of simulations and comparisons. The result shows
that the feedback control-based mechanism obtains a better trade-off between
bidding rationality and success rate than the other five comparable strategies.
Although this mechanism is only for black-box bidding (price prediction) at
this current stage, it can be conveniently and gradually upgraded to take into
account external constraints in the future.
