If clients issue queries
with mean freshness limit 0.6 (which means at most 20 minutes old), they obtain
the results about 30% faster, compared to a requested freshness of 1. This is exactly
the effect freshness-aware scheduling is targeting on: trading data ???up-to-dateness???
for query performance. The results also nicely show that there is no slowdown with
increasing cluster size, as it would be the case with synchronous updates: we were
doubling the number of clients and the cluster size at the same time, and mean response
time did not change, but query throughput doubled. This means that at least
Figure 5. Performance of FAS with varying freshness limits
(a) (b)
OLAP with a Database Cluster 24
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up to 128 nodes, freshness-aware scheduling scales linearly with increasing cluster
size. At the same time, the throughput achieved by 10 concurrent update streams
remains constant, even with a large OLAP cluster of up to 128 nodes (not shown in
the graphs). Obviously, the coordination middleware can keep up with the updaters
and the increasing OLAP workload (on 128 nodes, 256 query streams are active)
without a slowdown for either queries or updates. At the same time, the CPU load
of the scheduler with 128 nodes was only 30% on average.
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