Process control and scheduling issues for multiprogrammed shared-memory multiprocessors
SOSP '89 Proceedings of the twelfth ACM symposium on Operating systems principles
A dynamic processor allocation policy for multiprogrammed shared-memory multiprocessors
ACM Transactions on Computer Systems (TOCS)
Theoretical Computer Science - Special issue on dynamic and on-line algorithms
Online computation and competitive analysis
Online computation and competitive analysis
Static scheduling algorithms for allocating directed task graphs to multiprocessors
ACM Computing Surveys (CSUR)
Speed is as powerful as clairvoyance
Journal of the ACM (JACM)
Theoretical Computer Science - Selected papers in honor of Manuel Blum
Integrated scheduling: the best of both worlds
Journal of Parallel and Distributed Computing
Xen and the art of virtualization
SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
Non-clair voy ant multiprocessor scheduling of jobs with changing execution characteristics
Journal of Scheduling - Special issue: On-line scheduling
Performance-Driven Processor Allocation
IEEE Transactions on Parallel and Distributed Systems
Adaptive scheduling with parallelism feedback
Proceedings of the eleventh ACM SIGPLAN symposium on Principles and practice of parallel programming
Competitive online scheduling for server systems
ACM SIGMETRICS Performance Evaluation Review
Pull-based data broadcast with dependencies: be fair to users, not to items
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Usher: an extensible framework for managing custers of virtual machines
LISA'07 Proceedings of the 21st conference on Large Installation System Administration Conference
Provably Efficient Online Nonclairvoyant Adaptive Scheduling
IEEE Transactions on Parallel and Distributed Systems
Improved results for scheduling batched parallel jobs by using a generalized analysis framework
Journal of Parallel and Distributed Computing
Competitive Two-Level Adaptive Scheduling Using Resource Augmentation
Job Scheduling Strategies for Parallel Processing
Non-clairvoyant batch sets scheduling: fairness is fair enough
ESA'07 Proceedings of the 15th annual European conference on Algorithms
Malleable-Lab: A Tool for Evaluating Adaptive Online Schedulers on Malleable Jobs
PDP '10 Proceedings of the 2010 18th Euromicro Conference on Parallel, Distributed and Network-based Processing
Scalable Hierarchical Scheduling for Multiprocessor Systems Using Adaptive Feedback-Driven Policies
ISPA '10 Proceedings of the International Symposium on Parallel and Distributed Processing with Applications
Efficient Adaptive Scheduling of Multiprocessors with Stable Parallelism Feedback
IEEE Transactions on Parallel and Distributed Systems
Speed scaling for energy and performance with instantaneous parallelism
TAPAS'11 Proceedings of the First international ICST conference on Theory and practice of algorithms in (computer) systems
Fair and Efficient Online Adaptive Scheduling for Multiple Sets of Parallel Applications
ICPADS '11 Proceedings of the 2011 IEEE 17th International Conference on Parallel and Distributed Systems
Scalably scheduling processes with arbitrary speedup curves
ACM Transactions on Algorithms (TALG)
Dynamic Fractional Resource Scheduling versus Batch Scheduling
IEEE Transactions on Parallel and Distributed Systems
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We study online adaptive scheduling for multiple sets of parallel jobs, where each set may contain one or more jobs with time-varying parallelism. This two-level scheduling scenario arises naturally when multiple parallel applications are submitted by different users or user groups in large parallel systems, where both user-level fairness and system-wide efficiency are of important concerns. To achieve fairness, we use the well-known equi-partitioning algorithm to distribute the available processors among the active job sets at any time. For efficiency, we apply a feedback-driven adaptive scheduler that periodically adjusts the processor allocations within each set by consciously exploiting the jobs' execution history. We show that our algorithm achieves asymptotically competitive performance with respect to the set response time, which incorporates two widely used performance metrics, namely, total response time and makespan, as special cases. Both theoretical analysis and simulation results demonstrate that our algorithm improves upon an existing scheduler that provides only fairness but lacks efficiency. Furthermore, we provide a generalized framework for analyzing a family of scheduling algorithms based on feedback-driven policies with provable efficiency. Finally, we consider an extended multi-level hierarchical scheduling model and present a fair and efficient solution that effectively reduces the problem to the two-level model.