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Dynamic vs. static quantum-based parallel processor allocation

https://doi.org/10.1007/bfb0022295
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/spe.4380231203openalex-snapshot
Utopia: A load sharing facility for large, heterogeneous distributed computer systems
inferredcites →https://doi.org/10.1007/3-540-60153-8_26openalex-snapshot
Multiprocessor scheduling for high-variability service time distributions
inferredcites →https://doi.org/10.1007/3-540-60153-8_38openalex-snapshot
Job characteristics of a production parallel scientific workload on the NASA Ames iPSC/860
inferredcites →https://doi.org/10.1007/bfb0022285openalex-snapshot
Workload evolution on the Cornell Theory Center IBM SP2
inferredcites →https://doi.org/10.1007/bfb0022293openalex-snapshot
Using runtime measured workload characteristics in parallel processor scheduling
inferredcites →https://doi.org/10.1007/bfb0022296openalex-snapshot
Dynamic versus adaptive processor allocation policies for message passing parallel computers: An empirical comparison
inferredcites →https://doi.org/10.1007/bfb0022297openalex-snapshot
Dynamic partitioning in different distributed-memory environments
inferredcites →https://doi.org/10.1016/0166-5316(91)90044-4openalex-snapshot
Characterisation of programs for scheduling in multiprogrammed parallel systems
inferredcites →https://doi.org/10.1016/0166-5316(94)90036-1openalex-snapshot
Application scheduling and processor allocation in multiprogrammed parallel processing systems
inferredcites →https://doi.org/10.1109/32.90447openalex-snapshot
The processor working set and its use in scheduling multiprocessor systems
inferredcites →https://doi.org/10.1109/ipps.1995.395855openalex-snapshot
Data parallel programming in an adaptive environment
inferredcites →https://doi.org/10.1145/1007771.55608openalex-snapshot
Scheduling in multiprogrammed parallel systems
inferredcites →https://doi.org/10.1145/151244.151246openalex-snapshot
A dynamic processor allocation policy for multiprogrammed shared-memory multiprocessors
inferredcites →https://doi.org/10.1145/169627.169848openalex-snapshot
Performance analysis of job scheduling policies in parallel supercomputing environments
inferredcites →https://doi.org/10.1145/183018.183021openalex-snapshot
Analysis of the impact of memory in distributed parallel processing systems
inferredcites →https://doi.org/10.1145/183018.183023openalex-snapshot
Use of application characteristics and limited preemption for run-to-completion parallel processor scheduling policies
inferredcites →https://doi.org/10.1145/195470.195485openalex-snapshot
Scheduling and page migration for multiprocessor compute servers
inferredcites →https://doi.org/10.1145/195473.195485openalex-snapshot
Scheduling and page migration for multiprocessor compute servers
inferredcites →https://doi.org/10.1145/223586.223618openalex-snapshot
The interaction of parallel and sequential workloads on a network of workstations
inferredcites →https://doi.org/10.1145/223587.223618openalex-snapshot
The interaction of parallel and sequential workloads on a network of workstations
inferredcites →https://doi.org/10.1145/224056.224072openalex-snapshot
Implementing global memory management in a workstation cluster
inferredcites →https://doi.org/10.1145/55595.55608openalex-snapshot
Scheduling in multiprogrammed parallel systems
inferredcites →https://doi.org/10.1145/74850.74866openalex-snapshot
Process control and scheduling issues for multiprogrammed shared-memory multiprocessors
inferredcites →https://doi.org/10.1145/74851.74866openalex-snapshot
Process control and scheduling issues for multiprogrammed shared-memory multiprocessors
inferredcites →https://doi.org/10.1145/75108.75391openalex-snapshot
Characterizations of parallelism in applications and their use in scheduling
inferredcites →https://doi.org/10.1145/98457.98760openalex-snapshot
Processor scheduling in shared memory multiprocessors
inferredcites →https://doi.org/10.1145/98457.98761openalex-snapshot
The performance of multiprogrammed multiprocessor scheduling algorithms

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/cpe.887openalex-snapshot
Loosely coordinated coscheduling in the context of other approaches for dynamic job scheduling: a survey
inferred← citeshttps://doi.org/10.1007/3-540-39997-6_12openalex-snapshot
Adaptive Selection of Partition Size for Supercomputer Requests
inferred← citeshttps://doi.org/10.1007/3-540-47954-6_5openalex-snapshot
The Effect of Correlating Quantum Allocation and Job Size for Gang Scheduling
inferred← citeshttps://doi.org/10.1007/3-540-63574-2_14openalex-snapshot
Theory and practice in parallel job scheduling
inferred← citeshttps://doi.org/10.1007/978-3-540-71035-6_1openalex-snapshot
Provably Efficient Two-Level Adaptive Scheduling
inferred← citeshttps://doi.org/10.1007/978-3-540-71035-6_6openalex-snapshot
Adaptive Job Scheduling Via Predictive Job Resource Allocation
inferred← citeshttps://doi.org/10.1007/978-3-540-78699-3_5openalex-snapshot
QBETS: Queue Bounds Estimation from Time Series
inferred← citeshttps://doi.org/10.1007/978-3-642-04633-9_14openalex-snapshot
Adaptive Scheduling for QoS Virtual Machines under Different Resource Allocation – Performance Effects and Predictability
inferred← citeshttps://doi.org/10.1007/bfb0022284openalex-snapshot
Toward convergence in job schedulers for parallel supercomputers
inferred← citeshttps://doi.org/10.1007/bfb0053978openalex-snapshot
Metrics and benchmarking for parallel job scheduling
inferred← citeshttps://doi.org/10.1016/0166-5316(96)00025-9openalex-snapshot
Benefits of speedup knowledge in memory-constrained multiprocessor scheduling
inferred← citeshttps://doi.org/10.1016/j.jpdc.2015.09.007openalex-snapshot
Combining performance and priority for scheduling resizable parallel applications
inferred← citeshttps://doi.org/10.1109/ccgrid.2002.1017131openalex-snapshot
A Malleable-Job System for Timeshared Parallel Machines
inferred← citeshttps://doi.org/10.1109/hipc.2014.7116905openalex-snapshot
Towards realizing the potential of malleable jobs
inferred← citeshttps://doi.org/10.1109/icarc54489.2022.9754037openalex-snapshot
An Improved Round Robin Algorithm for an Efficient CPU Scheduling
inferred← citeshttps://doi.org/10.1109/icdcs.2006.14openalex-snapshot
An empirical evaluation of work stealing with parallelism feedback
inferred← citeshttps://doi.org/10.1109/icpp.1997.622685openalex-snapshot
Trace-driven analysis of migration-based gang scheduling policies for parallel computers
inferred← citeshttps://doi.org/10.1109/icppw.2010.51openalex-snapshot
Predictive Space- and Time-Resource Allocation for Parallel Job Scheduling in Clusters, Grids, Clouds
inferred← citeshttps://doi.org/10.1109/iiswc.2006.302746openalex-snapshot
Predicting Bounds on Queuing Delay in Space-shared Computing Environments
inferred← citeshttps://doi.org/10.1109/ipdps.2007.370303openalex-snapshot
Provably Efficient Online Non-clairvoyant Adaptive Scheduling
inferred← citeshttps://doi.org/10.1109/ipdps.2010.5470430openalex-snapshot
ADEPT scalability predictor in support of adaptive resource allocation
inferred← citeshttps://doi.org/10.1109/rtss.2006.38openalex-snapshot
Processor Scheduler for Multi-Service Routers
inferred← citeshttps://doi.org/10.1109/tpds.2008.39openalex-snapshot
Provably Efficient Online Nonclairvoyant Adaptive Scheduling
inferred← citeshttps://doi.org/10.1142/s0129626407002855openalex-snapshot
Using Model-based Clustering to Improve Predictions for Queueing Delay on Parallel Machines
inferred← citeshttps://doi.org/10.1145/1229428.1229448openalex-snapshot
Adaptive work stealing with parallelism feedback
inferred← citeshttps://doi.org/10.1145/1394441.1394443openalex-snapshot
Adaptive work-stealing with parallelism feedback
inferred← citeshttps://doi.org/10.1145/309746.309753openalex-snapshot
Experiences with parallel job scheduling on a transputer system
inferred← citeshttps://doi.org/10.3390/computers10050063openalex-snapshot
Enhanced Round-Robin Algorithm in the Cloud Computing Environment for Optimal Task Scheduling
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