Tracy D. Braun

A Comparison of Eleven Static Heuristics for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing Systems

Mixed-machine heterogeneous computing (HC) environments utilize a distributed suite of different high-performance machines, interconnected with high-speed links, to perform different computationally intensive applications that have diverse computational requirements. HC environments are well suited to meet the computational demands of large, diverse groups of tasks. The problem of optimally mapping (defined as matching and scheduling) these tasks onto the machines of a distributed HC environment has been shown, in general, to be NP-complete, requiring the development of heuristic techniques. Selecting the best heuristic to use in a given environment, however, remains a difficult problem, because comparisons are often clouded by different underlying assumptions in the original study of each heuristic. Therefore, a collection of 11 heuristics from the literature has been selected, adapted, implemented, and analyzed under one set of common assumptions. It is assumed that the heuristics derive a mapping statically (i.e., off-line). It is also assumed that a metatask (i.e., a set of independent, noncommunicating tasks) is being mapped and that the goal is to minimize the total execution time of the metatask. The 11 heuristics examined are Opportunistic Load Balancing, Minimum Execution Time, Minimum Completion Time, Min?min, Max?min, Duplex, Genetic Algorithm, Simulated Annealing, Genetic Simulated Annealing, Tabu, and A*. This study provides one even basis for comparison and insights into circumstances where one technique will out-perform another. The evaluation procedure is specified, the heuristics are defined, and then comparison results are discussed. It is shown that for the cases studied here, the relatively simple Min?min heuristic performs well in comparison to the other techniques.

A comparison study of static mapping heuristics for a class of meta-tasks on heterogeneous computing systems

Heterogeneous computing (HC) environments are well suited to meet the computational demands of large, diverse groups of tasks (i.e., a meta-task). The problem of mapping (defined as matching and scheduling) these tasks onto the machines of an HC environment has been shown, in general, to be NP-complete, requiring the development of heuristic techniques. Selecting the best heuristic to use in a given environment, however, remains a difficult problem, because comparisons are often clouded by different underlying assumptions in the original studies of each heuristic. Therefore, a collection of eleven heuristics from the literature has been selected, implemented, and analyzed under one set of common assumptions. The eleven heuristics examined are opportunistic load balancing, user-directed assignment, fast greedy, min-min, max-min, greedy, genetic algorithm, simulated annealing, genetic simulated annealing, tabu, and A*. This study provides one even basis for comparison and insights into circumstances where one technique will outperform another. The evaluation procedure is specified, the heuristics are defined, and then selected results are compared.

A taxonomy for describing matching and scheduling heuristics for mixed-machine heterogeneous computing systems

The problem of mapping (defined as matching and scheduling) tasks and communications onto multiple machines and networks in a heterogeneous computing (HC) environment has been shown to be NP-complete, in general, requiring the development of heuristic techniques. Many different types of mapping heuristics have been developed in recent years. However, selecting the best heuristic to use in any given scenario remains a difficult problem. Factors making this selection difficult are discussed. Motivated by these difficulties, a new taxonomy for classifying mapping heuristics for HC environments is proposed (Purdue HC Taxonomy). The taxonomy is defined in three major parts: the models used for applications and communication requests; the models used for target hardware platforms; and the characteristics of mapping heuristics, Each part of the taxonomy is described, with examples given to help clarify the taxonomy. The benefits and uses of this taxonomy are also discussed.

Heterogeneous Distributed Computing

The sections in this article are 1 Examples of HC Application Studies 2 Examples of HC Environments and Tools 3 PVM and HeNCE 4 Taxonomies of Heterogeneous Computing 5 A Conceptual Model of Heterogeneous Computing 6 Task Profiling and Analytical Benchmarking 7 Matching and Scheduling 8 Matching and Scheduling Metatasks 9 Summary and Future Directions 10 Acknowledgment

Dynamically mapping tasks with priorities and multiple deadlines in a heterogeneous environment

In a distributed heterogeneous computing system, the resources have different capabilities and tasks have different requirements. To maximize the performance of the system, it is essential to assign the resources to tasks (match) and order the execution of tasks on each resource (schedule) to exploit the heterogeneity of the resources and tasks. Dynamic mapping (defined as matching and scheduling) is performed when the arrival of tasks is not known a priori. In the heterogeneous environment considered in this study, tasks arrive randomly, tasks are independent (i.e., no inter-task communication), and tasks have priorities and multiple soft deadlines. The value of a task is calculated based on the priority of the task and the completion time of the task with respect to its deadlines. The goal of a dynamic mapping heuristic in this research is to maximize the value accrued of completed tasks in a given interval of time. This research proposes, evaluates, and compares eight dynamic mapping heuristics. Two static mapping schemes (all arrival information of tasks are known) are designed also for comparison. The performance of the best heuristics is 84% of a calculated upper bound for the scenarios considered.

Characterizing Resource Allocation Heuristics for Heterogeneous Computing Systems

In many distributed computing environments, collections of applications need to be processed using a set of heterogeneous computing (HC) resources to maximize some performance goal. An important research problem in these environments is how to assign resources to applications (matching) and order the execution of the applications (scheduling) so as to maximize some performance criterion without violating any constraints. This process of matching and scheduling is called mapping. To make meaningful comparisons among mapping heuristics, a system designer needs to understand the assumptions made by the heuristics for (1) the model used for the application and communication tasks, (2) the model used for system platforms, and (3) the attributes of the mapping heuristics. This chapter presents a three-part classification scheme ( 3PCS ) for HC systems. The 3PCS is useful for researchers who want to (a) understand a mapper given in the literature, (b) describe their design of a mapper more thoroughly by using a common standard, and (c) select a mapper to match a given real-world environment.

Heterogeneous Computing: Goals, Methods, and Open Problems

This paper discusses the material to be presented by H. J. Siegel in his keynote talk. Distributed high-performance heterogeneous computing (HC) environments are composed of machines with varied computational capabilities interconnected by high-speed links. These environments are well suited to meet the computational demands of large, diverse groups of applications. One key factor in achieving the best performance possible from HC environments is the ability to assign effectively the applications to machines and schedule their execution. Several factors must be considered during this assignment. A conceptual model for the automatic decomposition of an application into tasks and assignment of tasks to machines is presented. An example of a static matching and scheduling approach for an HC environment is summarized. Some examples of current HC technology and open research problems are discussed.

Static resource allocation for heterogeneous computing environments with tasks having dependencies, priorities, deadlines, and multiple versions

Heterogeneous computing (HC) environments composed of interconnected machines with varied computational capabilities are well suited to meet the computational demands of large, diverse groups of tasks. One aspect of resource allocation in HC environments is matching tasks with machines and scheduling task execution on the assigned machines. We will refer to this matching and scheduling process as mapping. The problem of mapping these tasks onto the machines of a distributed HC environment has been shown, in general, to be NP-complete. Therefore, the development of heuristic techniques to find near-optimal solutions is required. In the HC environment investigated, tasks have deadlines, priorities, multiple versions, and may be composed of communicating subtasks. The best static (off-line) techniques from some previous studies are adapted and applied to this mapping problem: a genetic algorithm (GA), a GENITOR-style algorithm, and a two phase greedy technique based on the concept of Min-min heuristics. Simulation studies compare the performance of these heuristics in several overloaded scenarios, i.e., not all tasks can be executed by their deadlines. The performance measure used is the sum of weighted priorities of tasks that completed before their deadline, adjusted based on the version of the task used. It is shown that for the cases studied here, the GENITOR technique finds the best results, but the faster two phase greedy approach also performs very well.

Dynamic mapping in a heterogeneous environment with tasks having priorities and multiple deadlines

In a distributed heterogeneous computing system, the resources have different capabilities and tasks have different requirements. To maximize the performance of the system, it is essential to assign resources to tasks (match) and order the execution of tasks on each resource (schedule in a manner that exploits the heterogeneity of the resources and tasks. The mapping (defined as matching and scheduling) of tasks onto machines with varied computational capabilities has been shown, in general, to be an NP-complete problem. Therefore, heuristic techniques to find a near-optimal solution to this mapping problem are required. Dynamic mapping is performed when the arrival of tasks is not known a priori. In the heterogeneous environment considered in this study, tasks arrive randomly, tasks are independent (i.e., no communication among tasks), and tasks have priorities and multiple deadlines. This research proposes, evaluates, and compares eight dynamic heuristics. The performance of the best heuristics is 83% of an upper bound.

Static mapping heuristics for tasks with dependencies, priorities, deadlines, and multiple versions in heterogeneous environments

Heterogeneous computing (HC) environments composed of interconnected machines with varied computational capabilities are well suited to meet the computational demands of large, diverse groups of tasks. The problem, of mapping (defined as matching and scheduling) these tasks onto the machines of a distributed HC environment. has been shown, in general, to be NP-complete. Therefore; the development of heuristic techniques to find near-optimal solutions is required. In the HC environment investigated, tasks had deadlines, priorities, multiple versions, and may be composed of communicating subtasks. The best static (off-line) techniques from some previous studies were adapted and applied to this mapping problem: a genetic algorithm (GA), a GENITOR-style algorithm., and a greedy Min-min technique. Simulation studies compared the performance of these heuristics in several overloaded scenarios, i.e., not all tasks executed. The performance measure used was a sum of weighted priorities of tasks that completed before their deadline, adjusted based on the version of the task used. It is shown that for the cases studied here, the GENITOR technique found the best results, but the faster Min-min approach also performed very well.