  In recent years, the graph partitioning problem gained importance as a
mandatory preprocessing step for distributed graph processing on very large
graphs. Existing graph partitioning algorithms minimize partitioning latency by
assigning individual graph edges to partitions in a streaming manner --- at the
cost of reduced partitioning quality. However, we argue that the mere
minimization of partitioning latency is not the optimal design choice in terms
of minimizing total graph analysis latency, i.e., the sum of partitioning and
processing latency. Instead, for complex and long-running graph processing
algorithms that run on very large graphs, it is beneficial to invest more time
into graph partitioning to reach a higher partitioning quality --- which
drastically reduces graph processing latency. In this paper, we propose ADWISE,
a novel window-based streaming partitioning algorithm that increases the
partitioning quality by always choosing the best edge from a set of edges for
assignment to a partition. In doing so, ADWISE controls the partitioning
latency by adapting the window size dynamically at run-time. Our evaluations
show that ADWISE can reach the sweet spot between graph partitioning latency
and graph processing latency, reducing the total latency of partitioning plus
processing by up to 23-47 percent compared to the state-of-the-art.
