Hone: "Scaling Down" Hadoop on Shared-Memory Systems

Summary: Hone scales Hadoop to a shared-memory runtime, API- and binary-compatible with standard Hadoop so jars run unmodified. In-memory execution yields order-of-magnitude speedups over PDM and, for in-memory datasets, can exceed a 15-node cluster. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 10614
Venue: VLDB
Year: 2013
Pagerank: 4.7135369e-05
Overall Rank: 7,510 | 47.81%
DOI: -

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
11,980	Palette: Enabling Scalable Analytics for Big-Memory, Multicore Machines	2014	SIGMOD	4.1905499e-05

Outgoing Citations (Sorted by Pagerank)

Showing 2 of 2 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
408	HaLoop: Efficient Iterative Data Processing on Large Clusters	2010	VLDB	0.00023939456
3,511	M3R: Increased Performance for In-Memory Hadoop Jobs	2012	VLDB	7.0242945e-05

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