The Experts below are selected from a list of 123 Experts worldwide ranked by ideXlab platform
Howard Jay Siegel - One of the best experts on this subject based on the ideXlab platform.
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time and cost trade off management for scheduling Parallel applications on utility grids
Future Generation Computer Systems, 2010Co-Authors: Saurabh Garg, Rajkumar Buyya, Howard Jay SiegelAbstract:With the growth of Utility Grids and various Grid market infrastructures, the need for efficient and cost effective scheduling algorithms is also increasing rapidly, particularly in the area of meta-scheduling. In these environments, users not only may have conflicting requirements with other users, but also they have to manage the trade-off between time and cost such that their applications can be executed most economically in the minimum time. Thus, selection of the best Grid resources becomes a challenge in such a competitive environment. This paper presents three novel heuristics for scheduling Parallel applications on Utility Grids that manage and optimize the trade-off between time and cost constraints. The performance of the heuristics is evaluated through extensive simulations of a real-world environment with real Parallel Workload models to demonstrate the practicality of our algorithms. We compare our scheduling algorithms against existing common meta-schedulers experimentally. The results show that our algorithms outperform existing algorithms by minimizing the time and cost of application execution on Utility Grids.
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scheduling Parallel applications on utility grids time and cost trade off management
ACSC '09 Proceedings of the Thirty-Second Australasian Conference on Computer Science - Volume 91, 2009Co-Authors: Saurabh Garg, Rajkumar Buyya, Howard Jay SiegelAbstract:With the growth of Utility Grids and various Grid market infrastructures, the need for efficient and cost effective scheduling algorithms is also increasing rapidly, particularly in the area of meta-scheduling. In these environments, users not only may have conflicting requirements with other users, but also they have to manage the trade-off between time and cost such that their applications can be executed most economically in the minimum time. Thus, choosing of the best Grid resources becomes a challenge in such a competitive market. This paper presents two novel heuristics for scheduling Parallel applications on Utility Grids that manage and optimize the trade-off between time and cost constraints. The performance of the heuristics is evaluated through extensive simulations of a real-world environment with real Parallel Workload models to demonstrate the practicality of our algorithms. We compare our scheduling algorithms against other common algorithms used by current meta-schedulers. The results shows that our algorithms outperform other algorithms by minimizing the time and cost of application execution on Utility Grids.
Allen B Downey - One of the best experts on this subject based on the ideXlab platform.
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a Parallel Workload model and its implications for processor allocation
Cluster Computing, 1998Co-Authors: Allen B DowneyAbstract:We develop a Workload model based on the observed behavior of Parallel computers at the San Diego Supercomputer Center and the Cornell Theory Center. This model gives us insight into the performance of strategies for scheduling moldable jobs on space-sharing Parallel computers. We find that Adaptive Static Partitioning (ASP), which has been reported to work well for other Workloads, does not perform as well as strategies that adapt better to system load. The best of the strategies we consider is one that explicitly reduces allocations when load is high (a variation of Sevcik’s (1989) A+ strategy).
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a Parallel Workload model and its implications for processor allocation
High Performance Distributed Computing, 1997Co-Authors: Allen B DowneyAbstract:We develop a Workload model based on observations of Parallel computers at the San Diego Supercomputer Center and the Cornell Theory Center. This model gives us insight into the performance of strategies for scheduling moldable jobs on space-sharing Parallel computers. We find that Adaptive Static Partitioning (ASP), which has been reported to work well for other Workloads, does not perform as well as strategies that adapt better to system load. The best of the strategies we consider is one that explicitly reduces allocations when load is high (a variation of Sevcik's A+ strategy (1989)).
Saurabh Garg - One of the best experts on this subject based on the ideXlab platform.
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time and cost trade off management for scheduling Parallel applications on utility grids
Future Generation Computer Systems, 2010Co-Authors: Saurabh Garg, Rajkumar Buyya, Howard Jay SiegelAbstract:With the growth of Utility Grids and various Grid market infrastructures, the need for efficient and cost effective scheduling algorithms is also increasing rapidly, particularly in the area of meta-scheduling. In these environments, users not only may have conflicting requirements with other users, but also they have to manage the trade-off between time and cost such that their applications can be executed most economically in the minimum time. Thus, selection of the best Grid resources becomes a challenge in such a competitive environment. This paper presents three novel heuristics for scheduling Parallel applications on Utility Grids that manage and optimize the trade-off between time and cost constraints. The performance of the heuristics is evaluated through extensive simulations of a real-world environment with real Parallel Workload models to demonstrate the practicality of our algorithms. We compare our scheduling algorithms against existing common meta-schedulers experimentally. The results show that our algorithms outperform existing algorithms by minimizing the time and cost of application execution on Utility Grids.
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scheduling Parallel applications on utility grids time and cost trade off management
ACSC '09 Proceedings of the Thirty-Second Australasian Conference on Computer Science - Volume 91, 2009Co-Authors: Saurabh Garg, Rajkumar Buyya, Howard Jay SiegelAbstract:With the growth of Utility Grids and various Grid market infrastructures, the need for efficient and cost effective scheduling algorithms is also increasing rapidly, particularly in the area of meta-scheduling. In these environments, users not only may have conflicting requirements with other users, but also they have to manage the trade-off between time and cost such that their applications can be executed most economically in the minimum time. Thus, choosing of the best Grid resources becomes a challenge in such a competitive market. This paper presents two novel heuristics for scheduling Parallel applications on Utility Grids that manage and optimize the trade-off between time and cost constraints. The performance of the heuristics is evaluated through extensive simulations of a real-world environment with real Parallel Workload models to demonstrate the practicality of our algorithms. We compare our scheduling algorithms against other common algorithms used by current meta-schedulers. The results shows that our algorithms outperform other algorithms by minimizing the time and cost of application execution on Utility Grids.
Rajkumar Buyya - One of the best experts on this subject based on the ideXlab platform.
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time and cost trade off management for scheduling Parallel applications on utility grids
Future Generation Computer Systems, 2010Co-Authors: Saurabh Garg, Rajkumar Buyya, Howard Jay SiegelAbstract:With the growth of Utility Grids and various Grid market infrastructures, the need for efficient and cost effective scheduling algorithms is also increasing rapidly, particularly in the area of meta-scheduling. In these environments, users not only may have conflicting requirements with other users, but also they have to manage the trade-off between time and cost such that their applications can be executed most economically in the minimum time. Thus, selection of the best Grid resources becomes a challenge in such a competitive environment. This paper presents three novel heuristics for scheduling Parallel applications on Utility Grids that manage and optimize the trade-off between time and cost constraints. The performance of the heuristics is evaluated through extensive simulations of a real-world environment with real Parallel Workload models to demonstrate the practicality of our algorithms. We compare our scheduling algorithms against existing common meta-schedulers experimentally. The results show that our algorithms outperform existing algorithms by minimizing the time and cost of application execution on Utility Grids.
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scheduling Parallel applications on utility grids time and cost trade off management
ACSC '09 Proceedings of the Thirty-Second Australasian Conference on Computer Science - Volume 91, 2009Co-Authors: Saurabh Garg, Rajkumar Buyya, Howard Jay SiegelAbstract:With the growth of Utility Grids and various Grid market infrastructures, the need for efficient and cost effective scheduling algorithms is also increasing rapidly, particularly in the area of meta-scheduling. In these environments, users not only may have conflicting requirements with other users, but also they have to manage the trade-off between time and cost such that their applications can be executed most economically in the minimum time. Thus, choosing of the best Grid resources becomes a challenge in such a competitive market. This paper presents two novel heuristics for scheduling Parallel applications on Utility Grids that manage and optimize the trade-off between time and cost constraints. The performance of the heuristics is evaluated through extensive simulations of a real-world environment with real Parallel Workload models to demonstrate the practicality of our algorithms. We compare our scheduling algorithms against other common algorithms used by current meta-schedulers. The results shows that our algorithms outperform other algorithms by minimizing the time and cost of application execution on Utility Grids.
Dick H.j. Epema - One of the best experts on this subject based on the ideXlab platform.
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Parallel Workload Modeling with Realistic Characteristics
IEEE Transactions on Parallel and Distributed Systems, 2014Co-Authors: Tran Ngoc Minh, Dick H.j. EpemaAbstract:Workload modeling and performance evaluation play crucial roles in the study of scheduling algorithms on large-scale Parallel and distributed systems. An effective design of a scheduling algorithm for these systems requires experiments with hundreds of simulations to evaluate its performance. Since each simulation needs one Workload as input, only real Workloads with usually a limited availability are not sufficient, and so representative Workload models are needed. Several studies have shown that realistic Workload characteristics such as burstiness, bag-of-tasks, etc., cause significant performance impacts on scheduling. Therefore, we argue that realistic Workload models should contain as many characteristics of real Workloads as possible. In practice, researchers use unrealistic Workloads in their scheduling evaluations because they lack models that can help generate realistic Workloads. In this article, we analyze real Parallel Workloads to show the presence of important characteristics including long range dependence, periodicity and temporal burstiness of job arrivals, bag-of-tasks behavior, and correlation of runtime and number of processors. Then, we present a systematic approach to create a complete model that contains all of these characteristics. Validation of our model with real world data shows that it does not only capture the above characteristics, but also can fit marginal distributions well.
