Static scheduling algorithms for allocating directed task graphs to multiprocessors
ACM Computing Surveys (CSUR)
SETI@HOME—massively distributed computing for SETI
Computing in Science and Engineering
Introduction to Algorithms
Models and Scheduling Mechanisms for Global Computing Applications
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
On Scheduling Mesh-Structured Computations for Internet-Based Computing
IEEE Transactions on Computers
Guidelines for Scheduling Some Common Computation-Dags for Internet-Based Computing
IEEE Transactions on Computers
Toward a Theory for Scheduling Dags in Internet-Based Computing
IEEE Transactions on Computers
Scheduling DAGs on asynchronous processors
Proceedings of the nineteenth annual ACM symposium on Parallel algorithms and architectures
IEEE Transactions on Parallel and Distributed Systems
Extending IC-Scheduling via the Sweep Algorithm
PDP '08 Proceedings of the 16th Euromicro Conference on Parallel, Distributed and Network-Based Processing (PDP 2008)
On scheduling dags to maximize area
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
Quincy: fair scheduling for distributed computing clusters
Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles
Improving MapReduce performance in heterogeneous environments
OSDI'08 Proceedings of the 8th USENIX conference on Operating systems design and implementation
Area-maximizing schedules for series-parallel DAGs
Euro-Par'10 Proceedings of the 16th international Euro-Par conference on Parallel processing: Part II
Assessing the computational benefits of AREA-oriented DAG-scheduling
Euro-Par'11 Proceedings of the 17th international conference on Parallel processing - Volume Part I
Scheduling Parallel Iterative Applications on Volatile Resources
IPDPS '11 Proceedings of the 2011 IEEE International Parallel & Distributed Processing Symposium
An adaptive scheduling method for grid computing
Euro-Par'06 Proceedings of the 12th international conference on Parallel Processing
The master-slave paradigm with heterogeneous processors
IEEE Transactions on Parallel and Distributed Systems
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Many modern computing platforms-notably clouds and desktop grids-exhibit dynamic heterogeneity: the availability and computing power of their constituent resources can change unexpectedly and dynamically, even in the midst of a computation. We introduce a new quality metric, area, for schedules that execute computations having interdependent constituent chores (jobs, tasks, etc.) on such platforms. Area measures the average number of tasks that a schedule renders eligible for execution at each step of a computation. Even though the definition of area does not mention and properties of host platforms (such as volatility), intuition suggests that rendering tasks eligible at a faster rate will have a benign impact on the performance of volatile platforms-and we report on simulation experiments that support this intuition. We derive the basic properties of the area metric and show how to efficiently craft area-maximizing (A-M) schedules for several classes of significant computations. Simulations that compare A-M scheduling against heuristics ranging from lightweight ones (e.g., FIFO) to computationally intensive ones suggest that A-M schedules complete computations on volatile heterogeneous platforms faster than their competition, by percentages that vary with computation structure and platform behavior-but are often in the double digits.