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Summary: An Adaptive Scheme for Overload Handling in
Active Data Warehouses
Hennadiy Leontyev, Theodore Johnson and James H. Anderson
University of North Carolina at Chapel Hill
{leontyev, anderson}@cs.unc.edu
AT&T Labs Research
johnsont@research.att.com
Abstract--This paper presents a novel adaptive approach for
scheduling updates in a data warehouse that processes "near-
real-time" data streams. Data is pushed to the warehouse from a
variety of external sources with a wide range of inter-arrival times
(e.g., once a minute to once a day). Due to network conditions,
the volume of incoming data can widely vary and data streams
can experience intermittent outages. Maintaining data freshness
in the presence of outages and load variations can be challenging.
In prior work, ad hoc heuristic algorithms have been proposed for
doing this. In this paper, a systematic approach based upon global
multiprocessor real-time scheduling theory is considered. The
proposed approach can handle overload and recovery situations
and maintain guarantees on data freshness for warehouse tables
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