Each proxy maintains a cache of previously fetched or predicted data values for each sensor. Since storage is plentiful at the proxy--microdrives or hard-drives can be used to hold the cache--the cache is assumed to be infinite and all previously predicted or fetched values are assumed to be stored at the proxy. The cache is used to handle queries on historical data--if requested values have already been fetched or if the error bounds of cached predictions are smaller than the query error tolerance, then the query can be handled locally, otherwise the requested data is pulled from the archive at the sensor. After responding to the query, the newly fetched values are inserted into the cache for future use.
A newly fetched value, upon insertion, is also used to improve the accuracy of the neighboring predictions using interpolation. The intuition for using interpolation is as follows. Upon receiving a new value from the sensor, suppose that the proxy finds a certain prediction error. Then it is very likely that the predictions immediately preceding and following that value incurred a similar error, and interpolation can be used to scale those cached values by the prediction error, thereby improving their estimates. PRESTO proxies currently use two types of interpolation heuristics: forward and backward.
Forward interpolation is simple. The proxy uses Equation 3 to predict the values and Equation 4 to re-estimate the confidence intervals for all samples between the newly inserted value and the next pulled or pushed value. In backward interpolation, the proxy scans backwards from the newly inserted value and modifies all cached predictions between the newly inserted value and the previous pushed or pulled value. To do so, it makes a simplifying assumption that the prediction error grows linearly at each step, and the corresponding prediction error is subtracted from each prediction.
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