Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On Smart Query Routing: For Distributed Graph Querying with Decoupled
  Storage   We study online graph queries that retrieve nearby nodes of a query node from
a large network. To answer such queries with high throughput and low latency,
we partition the graph and process the data in parallel across a cluster of
servers. State-of-the-art distributed graph querying systems place each graph
partition on a separate server, where query answering over that partition takes
place. This design has two major disadvantages. First, the router needs to
maintain a fixed routing table. Hence, these systems are less flexible with
respect to query routing, fault tolerance, and graph updates. Second, the graph
data must be partitioned such that the workload across the servers is balanced,
and the inter-machine communication is minimized. In addition, it is required
to update the existing partitions based on workload changes over graph nodes.
However, graph partitioning, online monitoring of workloads, and dynamically
updating the graph partitions are expensive. In this work, we mitigate both
these problems by decoupling graph storage from query processors, and by
developing smart routing strategies that improve the cache locality in query
processors. Since a query processor is no longer assigned any fixed part of the
graph, it is equally capable of handling any request, thus facilitating load
balancing and fault tolerance. On the other hand, due to our smart routing
strategies, query processors can effectively leverage their cache contents,
reducing the overall impact of how the graph is partitioned across storage
servers. A detailed experimental evaluation with several real-world, large
graph datasets demonstrates that our proposed framework, gRouting - even with
simple hash partitioning of the data - achieves up to an order of magnitude
better query throughput compared to existing graph querying systems that employ
expensive graph partitioning and re-partitioning strategies.

20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
2
The categories of this paper are: math
17
The categories of this paper are: q-fin
12
The categories of this paper are: nucl-th
11
The categories of this paper are: hep-lat
8
The categories of this paper are: hep-ex
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an
19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph
16
The categories of this paper are: stat
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The categories of this paper are: plasm-ph
14
The categories of this paper are: math-ph