Approximate algorithms scheduling parallelizable tasks
SPAA '92 Proceedings of the fourth annual ACM symposium on Parallel algorithms and architectures
Approximation algorithms for NP-hard problems
Approximation algorithms for NP-hard problems
Flow and stretch metrics for scheduling continuous job streams
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Linear-time approximation schemes for scheduling malleable parallel tasks
Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms
Speed is as powerful as clairvoyance
Journal of the ACM (JACM)
Scheduling Algorithms
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Cluster Computing
A Symbolic Approachto Modeling Cellular Behavior
HiPC '02 Proceedings of the 9th International Conference on High Performance Computing
Theory and Practice in Parallel Job Scheduling
IPPS '97 Proceedings of the Job Scheduling Strategies for Parallel Processing
Core Algorithms of the Maui Scheduler
JSSPP '01 Revised Papers from the 7th International Workshop on Job Scheduling Strategies for Parallel Processing
Selective Reservation Strategies for Backfill Job Scheduling
JSSPP '02 Revised Papers from the 8th International Workshop on Job Scheduling Strategies for Parallel Processing
Online Scheduling to Minimize Average Stretch
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Average stretch without migration
Journal of Computer and System Sciences
Scheduling data transfers in a network and the set scheduling problem
Journal of Algorithms
Multi-processor scheduling to minimize flow time with ε resource augmentation
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
Bi-criteria algorithm for scheduling jobs on cluster platforms
Proceedings of the sixteenth annual ACM symposium on Parallelism in algorithms and architectures
Preemptive Maximum Stretch Optimization Scheduling for Wireless On-Demand Data Broadcast
IDEAS '04 Proceedings of the International Database Engineering and Applications Symposium
A batch scheduler with high level components
CCGRID '05 Proceedings of the Fifth IEEE International Symposium on Cluster Computing and the Grid (CCGrid'05) - Volume 2 - Volume 02
A $\frac32$-Approximation Algorithm for Scheduling Independent Monotonic Malleable Tasks
SIAM Journal on Computing
Minimizing the stretch when scheduling flows of divisible requests
Journal of Scheduling
Scheduling to minimize staleness and stretch in real-time data warehouses
Proceedings of the twenty-first annual symposium on Parallelism in algorithms and architectures
Parallel short sequence mapping for high throughput genome sequencing
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
Moldable parallel job scheduling using job efficiency: an iterative approach
JSSPP'06 Proceedings of the 12th international conference on Job scheduling strategies for parallel processing
Adaptive job scheduling via predictive job resource allocation
JSSPP'06 Proceedings of the 12th international conference on Job scheduling strategies for parallel processing
A moldable online scheduling algorithm and its application to parallel short sequence mapping
JSSPP'10 Proceedings of the 15th international conference on Job scheduling strategies for parallel processing
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This paper addresses the problem of scheduling non-preemptive moldable tasks to minimize the stretch of the tasks in an online non-clairvoyant setting. To the best of the authors' knowledge, this problem has never been studied before. To tackle this problem, first the sequential subproblem is studied through the lens of the approximation theory. An algorithm, called DASEDF, is proposed and, through simulations, it is shown to outperform the first-come, first-served scheme. Furthermore, it is observed that machine availability is the key to getting good stretch values. Then, the moldable task scheduling problem is considered, and, by leveraging the results from the sequential case, another algorithm, DBOS, is proposed to optimize the stretch while scheduling moldable tasks. This work is motivated by a task scheduling problem in the context of parallel short sequence mapping which has important applications in biology and genetics. The proposed DBOS algorithm is evaluated both on synthetic data sets that represent short sequence mapping requests and on data sets generated using log files of real production clusters. The results show that the DBOS algorithm significantly outperforms the two state-of-the-art task scheduling algorithms on stretch optimization.