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

ABSTRACT Il\u27lixed-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 comput ational requirements. HC environments are well suited to meet thl: computational dell-tands 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, requiring the development of heuristic techniques. Selecting the best heuristic to use in a given enviroi~menth, owever, remains a difficult problem, because comparisons are often clouded by different underlying assumptions in the original studies of each heuristic. There~fore; a collection of eleven heuristics from the literature has been selected: a,dapted, in~plementeda, nd anaiyzed 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 meta-task (i.e., a set of independent, non-communicating tasks) is being mapped, and that the goal is to minimize the total execution time of the metla-task. The eleven heuristics examined are Opportunistic Load Balancing, Minimum Execution Time, MininLlum Clompletion Time, Min-min, hllax-min, Duplex? Genetic i-Ilgorithm, Simulated Annealing, Genetic Simulat.ed .Annealing, Tabu, and Ax. This study provides one even basis for comparisor] 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 ca.ses studied here, the relat,ively simple Min-min heuristic performs well in comparison to the other techniques

Social Potential Models For Modeling Traffic And Transportation

The Social Potential , which the authors will refer to as the SP, is the name given to a technique of implementing multi-agent movement in simulations by representing behaviors, goals, and motivations as artificial social forces. These forces then determine the movement of the individual agents. Several SP models, including the Flocking, Helbing-Molnar-Farkas-Visek (HMFV), and Lakoba-Kaup-Finkelstein (LKF) models, are commonly used to describe pedestrian movement. A systematic procedure is described here, whereby one can construct and use these and other SP models. The theories behind these models are discussed along with the application of the procedure. Through the use of these techniques, it has been possible to represent schools of fish swimming, flocks of birds flying, crowds exiting rooms, crowds walking through hallways, and individuals wandering in open fields. Once one has an understanding of these models, more complex and specific scenarios could be constructed by applying additional constraints and parameters. The models along with the procedure give a guideline for understanding and implementing simulations using SP techniques. © 2009, IGI Global

"Social Potential" Models for Modeling Traffic and Transportation

The Social Potential , which the authors will refer to as the SP, is the name given to a technique of implementing multi-agent movement in simulations by representing behaviors, goals, and motivations as artificial social forces. These forces then determine the movement of the individual agents. Several SP models, including the Flocking, Helbing-Molnar-Farkas-Visek (HMFV), and Lakoba-Kaup-Finkelstein (LKF) models, are commonly used to describe pedestrian movement. A systematic procedure is described here, whereby one can construct and use these and other SP models. The theories behind these models are discussed along with the application of the procedure. Through the use of these techniques, it has been possible to represent schools of fish swimming, flocks of birds flying, crowds exiting rooms, crowds walking through hallways, and individuals wandering in open fields. Once one has an understanding of these models, more complex and specific scenarios could be constructed by applying additional constraints and parameters. The models along with the procedure give a guideline for understanding and implementing simulations using SP techniques. © 2009, IGI Global

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 prob lem of mapping (defi ned as matching and scheduling ) these tasks onto the machines of an HC environment has been shown in general to be NP- complete, requir ing the development of heuristic techniques. Selecting the best heuristic to use in a given environment , how ever, remains a di cult problem because comparisons are often clouded by di erent 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 exam ined are Opportunistic Load Balancing, User- Directed Assignment, Fast Greedy, Min min Max- min, Greedy, Genetic Algorithm, Simulated Annealing , Genetic Sim ulated Annealing, Tabu , and A*. This study provides one even basis for comparison and insights into circum stances where one technique will outperform another . The evaluation procedure is speci ed the heuristics are defined and then selected results are compared

A Comparison Study of Static Mapping Heuristics for a Class of Meta-tasks on Heterogeneous Computing Systems

Heterogeneous computing (HC) environments are well suited tomeet the computational demands of large, diverse groups of tasks (i.e., a meta-task). The problem of mapping (de ned 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 di cult problem, because comparisons are often clouded by di erent 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 examine