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IReS: Intelligent, Multi-Engine Resource Scheduler for Big Data Analytics Workflows

Summary: Intelligent resource scheduler for multi-engine big-data analytics. IReS models task costs across diverse compute/data engines and maps workflow fragments to the best engine per user policy, enabling heterogeneous runtimes and data stores. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4992
Venue
SIGMOD
Year
2015
Pagerank
4.3341665e-05
Overall Rank
9,503 | 33.89%
DOI
10.1145/2723372.2723377

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
10,263 APEROL: Adaptive Parallel Edge-to-cloud Runtime Optimization for Layered Workflow Execution 2026 VLDB 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

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

Rank Cited Paper Year Venue Pagerank
1,071 Starfish: A Self-tuning System for Big Data Analytics 2011 CIDR 0.00014312777
2,747 Stubby: A Transformation-based Optimizer for MapReduce Workflows 2012 VLDB 8.1828918e-05
5,050 xPAD: A Platform for Analytic Data Flows 2013 SIGMOD 5.7340229e-05
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