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13
Automatic Deployment for Hierarchical Network Enabled
, 2003
"... This paper focus on the deployment of grid infrastructures, more specifically Problem Solving Environments (PSE) for numerical applications on the grid. Even if the deployment of such an architecture is forced by physical constraints (firewall, access permission, security,...) its efficiency heavily ..."
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Cited by 24 (19 self)
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This paper focus on the deployment of grid infrastructures, more specifically Problem Solving Environments (PSE) for numerical applications on the grid. Even if the deployment of such an architecture is forced by physical constraints (firewall, access permission, security,...) its efficiency heavily depends on the quality of the mapping between its different components and the grid resources. This paper proposes a new model based on linear programming to estimate the performance of a deployment of a hierarchical PSE. The advantages of the modeling approach in this case are multiple: evaluate a virtual deployment before an actual deployment, provide a decision builder tool (i.e., designed to compare different architectures or buy new resource), take into account the platform scalability. Using this modeling, it is possible to determine the bottleneck of the platform and thus to know whether a given deployment can be improved or not. We illustrate this modeling by applying this results to an existing hierarchical PSE called DIET.
Efficient assignment and scheduling for heterogeneous dsp systems
 IEEE Trans. on Parallel and Distributed Systems
, 2005
"... This paper addresses high level synthesis for realtime digital signal processing (DSP) architectures using heterogeneous functional units (FUs). For such special purpose architecture synthesis, an important problem is how to assign a proper FU type to each operation of a DSP application and genera ..."
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Cited by 11 (6 self)
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This paper addresses high level synthesis for realtime digital signal processing (DSP) architectures using heterogeneous functional units (FUs). For such special purpose architecture synthesis, an important problem is how to assign a proper FU type to each operation of a DSP application and generate a schedule in such a way that all requirements can be met and the total cost can be minimized. We propose a twophase approach to solve this problem. In the first phase, we solve heterogeneous assignment problem, i.e., given the types of heterogeneous FUs, a DataFlow Graph (DFG) in which each node has different execution times and costs (may relate to power, reliability, etc.) for different FU types, and a timing constraint, how to assign a proper FU type to each node such that the total cost can be minimized while the timing constraint is satisfied. In the second phase, based on the assignments obtained in the first phase, we propose a minimum resource scheduling algorithm to generate a schedule and a feasible configuration that uses as little resource as possible. We prove heterogeneous assignment problem is NPcomplete. Efficient algorithms are proposed to find an optimal solution when the given DFG is a simple path or a tree. Three other algorithms are proposed to solve the general problem. The experiments show that our algorithms can effectively reduce the total cost compared with the previous work.
Static Scheduling Strategies for Heterogeneous Systems
, 2002
"... In this paper, we consider static scheduling techniques for heterogeneous systems, such as clusters and grids. We successively deal with minimum makespan scheduling, divisible load scheduling and steadystate scheduling. Finally, ..."
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Cited by 10 (1 self)
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In this paper, we consider static scheduling techniques for heterogeneous systems, such as clusters and grids. We successively deal with minimum makespan scheduling, divisible load scheduling and steadystate scheduling. Finally,
Cost minimization while satisfying hard/soft timing constraints for heterogeneous embedded systems
 ACM Transactions on Design Automation of Electronic Systems (TODAES
, 2009
"... In highlevel synthesis for realtime embedded systems using heterogeneous functional units (FUs), it is critical to select the best FU type for each task. However, some tasks may not have fixed execution times. This article models each varied execution time as a probabilistic random variable and so ..."
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Cited by 7 (3 self)
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In highlevel synthesis for realtime embedded systems using heterogeneous functional units (FUs), it is critical to select the best FU type for each task. However, some tasks may not have fixed execution times. This article models each varied execution time as a probabilistic random variable and solves heterogeneous assignment with probability (HAP) problem. The solution of the HAP problem assigns a proper FU type to each task such that the total cost is minimized while the timing constraint is satisfied with a guaranteed confidence probability. The solutions to the HAP problem are useful for both hard realtime and soft realtime systems. Optimal algorithms are proposed to find the optimal solutions for the HAP problem when the input is a tree or a simple path. Two other algorithms, one is optimal and the other is nearoptimal heuristic, are proposed to solve the general problem. The experiments show that our algorithms can effectively reduce the total cost while satisfying timing constraints with guaranteed confidence probabilities. For example, our algorithms achieve an average reduction of 33.0 % on total cost with 0.90 confidence probability satisfying timing constraints compared with the previous work using worstcase scenario.
Allocating series of workflows on computing grids
 IN ICPADS. IEEE COMPUTER SOCIETY
, 2008
"... In this paper, we focus on scheduling jobs on computing Grids. In our model, a Grid job is made of a large collection of input data sets, which must all be processed by the same task graph or workflow, thus resulting in a series of workflow problem. We are looking for an efficient solution with rega ..."
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Cited by 2 (1 self)
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In this paper, we focus on scheduling jobs on computing Grids. In our model, a Grid job is made of a large collection of input data sets, which must all be processed by the same task graph or workflow, thus resulting in a series of workflow problem. We are looking for an efficient solution with regard to throughput and latency, while avoiding solutions requiring complex control. We thus only consider singleallocation strategies. We present an algorithm based on mixed linear programming to find an optimal allocation, and this for different routing policies depending on how much latitude we have on communications. Then, using simulations, we compare our allocations to reference heuristics. The results show that our algorithm almost always finds an allocation with good throughput and low latency, and that it outperforms the reference heuristics, especially under communicationintensive scenarios.
The case for globally irregular locally regular algorithm architecture adequation
 In Journées Francophones sur l’Adéquation Algorithme Architecture (JFAAA’05
, 2005
"... Abstract — In modern embedded systems, parallelism is a good way to reduce power consumption while respecting the realtime constraints. To achieve this, one needs to efficiently exploit the potential parallelism of the application and of the architecture. We propose in this paper a hybrid optimizat ..."
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Cited by 1 (1 self)
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Abstract — In modern embedded systems, parallelism is a good way to reduce power consumption while respecting the realtime constraints. To achieve this, one needs to efficiently exploit the potential parallelism of the application and of the architecture. We propose in this paper a hybrid optimization method to improve the handling of repetitions in both the algorithm and the architecture. The approach is called Globally Irregular Locally Regular and consists in combining irregular heuristics and regular ones to take advantage of the strong points of both. I.
Efficient Scheduling of Task Graph Collections on Heterogeneous Resources
"... In this paper, we focus on scheduling jobs on computing Grids. In our model, a Grid job is made of a large collection of input data sets, which must all be processed by the same task graph or workflow, thus resulting in a collection of task graphs problem. We are looking for a competitive scheduling ..."
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Cited by 1 (0 self)
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In this paper, we focus on scheduling jobs on computing Grids. In our model, a Grid job is made of a large collection of input data sets, which must all be processed by the same task graph or workflow, thus resulting in a collection of task graphs problem. We are looking for a competitive scheduling algorithm not requiring complex control. We thus only consider singleallocation strategies. In addition to a mixed linear programming approach to find an optimal allocation, we present different heuristic schemes. Then, using simulations, we compare the performance of our different heuristics to the performance of a classical scheduling policy in Grids, HEFT. The results show that some of our staticscheduling policies take advantage of their platform and application knowledge and outperform HEFT, especially under communicationintensive scenarios. In particular, one of our heuristics, DELEGATE, almost always achieves the best performance while having lower running times than HEFT.
Stochastic Approach to Scheduling Multiple Divisible Tasks on a Heterogeneous Distributed Computing System
"... Heterogeneity has been considered in scheduling, but without taking into account the temporal variation of completion times of the subtasks for a divisible, independent task. In this paper, the problem of scheduling multiple, divisible independent tasks on a heterogeneous distributed computing syst ..."
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Heterogeneity has been considered in scheduling, but without taking into account the temporal variation of completion times of the subtasks for a divisible, independent task. In this paper, the problem of scheduling multiple, divisible independent tasks on a heterogeneous distributed computing system is addressed. The “stochastic ” approach, which was previously applied to DAG scheduling, is employed for scheduling a group of multiple divisible as well as whole independent tasks. It explicitly considers the standard deviations (temporal heterogeneity) in addition to the mean execution times in deriving a schedule, in order to model more closely what would actually happen “on average” on a temporally heterogeneous system (instead of approximating the random weights by their means only as in other approaches). Through an extensive computer simulation, it has been shown that the proposed approach can improve schedules significantly over those by a scheme which uses the average weights only. 1