Approximation algorithms for scheduling unrelated parallel machines
Mathematical Programming: Series A and B
The ANL/IBM SP Scheduling System
IPPS '95 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Theory and Practice in Parallel Job Scheduling
IPPS '97 Proceedings of the Job Scheduling Strategies for Parallel Processing
Classifying scheduling policies with respect to unfairness in an M/GI/1
SIGMETRICS '03 Proceedings of the 2003 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Fairness in Routing and Load Balancing
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Single-source unsplittable flow
FOCS '96 Proceedings of the 37th Annual Symposium on Foundations of Computer Science
Lottery and stride scheduling: flexible proportional-share resource management
Lottery and stride scheduling: flexible proportional-share resource management
Grid resource management
A resource-allocation queueing fairness measure
Proceedings of the joint international conference on Measurement and modeling of computer systems
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Lottery scheduling: flexible proportional-share resource management
OSDI '94 Proceedings of the 1st USENIX conference on Operating Systems Design and Implementation
Dryad: distributed data-parallel programs from sequential building blocks
Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007
Quincy: fair scheduling for distributed computing clusters
Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles
Delay scheduling: a simple technique for achieving locality and fairness in cluster scheduling
Proceedings of the 5th European conference on Computer systems
Mesos: a platform for fine-grained resource sharing in the data center
Proceedings of the 8th USENIX conference on Networked systems design and implementation
Sharing the data center network
Proceedings of the 8th USENIX conference on Networked systems design and implementation
Dominant resource fairness: fair allocation of multiple resource types
Proceedings of the 8th USENIX conference on Networked systems design and implementation
Modeling and synthesizing task placement constraints in Google compute clusters
Proceedings of the 2nd ACM Symposium on Cloud Computing
HotCloud'11 Proceedings of the 3rd USENIX conference on Hot topics in cloud computing
Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing
NSDI'12 Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation
PACMan: coordinated memory caching for parallel jobs
NSDI'12 Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation
Multi-resource fair queueing for packet processing
Proceedings of the ACM SIGCOMM 2012 conference on Applications, technologies, architectures, and protocols for computer communication
alsched: algebraic scheduling of mixed workloads in heterogeneous clouds
Proceedings of the Third ACM Symposium on Cloud Computing
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Max-Min Fairness is a flexible resource allocation mechanism used in most datacenter schedulers. However, an increasing number of jobs have hard placement constraints, restricting the machines they can run on due to special hardware or software requirements. It is unclear how to define, and achieve, max-min fairness in the presence of such constraints. We propose Constrained Max-Min Fairness (CMMF), an extension to max-min fairness that supports placement constraints, and show that it is the only policy satisfying an important property that incentivizes users to pool resources. Optimally computing CMMF is challenging, but we show that a remarkably simple online scheduler, called Choosy, approximates the optimal scheduler well. Through experiments, analysis, and simulations, we show that Choosy on average differs 2% from the optimal CMMF allocation, and lets jobs achieve their fair share quickly.