1,720,988 research outputs found
On scheduling series-parallel dags to maximize area
No full textThe AREA of a schedule for executing DAGs is the average number of DAG-chores that are eligible for execution at each step of the computation. AREA maximization is a new optimization goal for schedules that execute DAGs within computational environments, such as Internet-based computing, clouds, and volunteer computing projects, that are dynamically heterogeneous, in the sense that the environments' constituent computers can change their effective powers at times and in ways that are not predictable. This paper is motivated by the thesis that, within dynamically heterogeneous environments, DAG-schedules that have larger AREAs execute a computation-DAG with smaller completion time under many circumstances; this thesis is supported by preliminary simulation-based experiments. While every DAG admits an AREA-maximizing schedule, it is likely computationally difficult to find such a schedule for an arbitrary DAG. Earlier work has shown how to craft AREA-maximizing schedules efficiently for a number of families of DAGs whose structures are reminiscent of many scientific computations. The current paper extends this work by showing how to efficiently craft AREA-maximizing schedules for series-parallel DAGs, a family that models a multithreading computing paradigm. The techniques for crafting these schedules promise to apply also to other large families of recursively defined DAGs. Moreover, the ability to derive these schedules efficiently leads to an efficient AREA-oriented heuristic for scheduling arbitrary DAGs. © World Scientific Publishing Company
On scheduling dag s for volatile computing platforms: Area-maximizing schedules
No full textMany 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. © 2012 Elsevier Inc. All rights reserved
Advances in IC-Scheduling Theory: Scheduling Expansive and Reductive Dags and Scheduling Dags via Duality
No full textAn AREA-Oriented Heuristic for Scheduling DAGs on Volatile Computing Platforms
No full textMany 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 chores that a schedule renders eligible for execution at each step of a computation. Even though the definition of AREA does not mention any properties of host platforms (such as volatility), intuition suggests that rendering chores eligible at a faster rate will have a benign impact on the performance of volatile platforms. We report on simulation experiments that support this intuition. Earlier work has derived the basic properties of the AREA metric and has shown how to efficiently craft AREA-maximizing (A-M) schedules for several classes of significant computations. Even though A-M schedules always exist for every computation, it is not always known how to derive such schedules efficiently. In response, the current study develops an efficient algorithm that produces AREA-Oriented (A-O) schedules, which aim to efficiently approximate the AREAs of A-M schedules on arbitrary computations. The simulation experiments reported on here suggest that, in common with A-M schedules, A-O schedules complete computations on volatile heterogeneous platforms faster than a variety of heuristics that range from lightweight ones to computationally intensive ones - albeit not to the same degree as A-M schedules do. Our experiments suggest that schedules having larger AREAs have smaller completion times - but no proof of that yet exists
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