Task Matching and Scheduling in Heterogeneous Systems Using Simulated Evolution

ABSTRACT This paper describes and analyzes the application of a simulated evolution (SE) approach to the problem of matching and scheduling of coarse-grained tasks in a heterogeneous suite of machines. The various steps of the SE algorithm are first discussed. Goodness function required by SE is designed and explained. Then experimental results applied on various types of workloads are analyzed. Workloads are characterized according to the connectivity, heterogeneity, and communication-to-cost ratio of the task graphs. The performance of SE is also compared with a genetic algorithm (GA) approach for the same problem with respect to the quality of solutions generated, and timing requirements of the algorithms

Task Matching and Scheduling in Heterogeneous Systems Using Simulated Evolution

ABSTRACT This paper describes and analyzes the application of a simulated evolution (SE) approach to the problem of matching and scheduling of coarse-grained tasks in a heterogeneous suite of machines. The various steps of the SE algorithm are first discussed. Goodness function required by SE is designed and explained. Then experimental results applied on various types of workloads are analyzed. Workloads are characterized according to the connectivity, heterogeneity, and communication-to-cost ratio of the task graphs. The performance of SE is also compared with a genetic algorithm (GA) approach for the same problem with respect to the quality of solutions generated, and timing requirements of the algorithms

Resource management in heterogeneous computing systems with tasks of varying importance

2014 Summer.The problem of efficiently assigning tasks to machines in heterogeneous computing environments where different tasks can have different levels of importance (or value) to the computing system is a challenging one. The goal of this work is to study this problem in a variety of environments. One part of the study considers a computing system and its corresponding workload based on the expectations for future environments of Department of Energy and Department of Defense interest. We design heuristics to maximize a performance metric created using utility functions. We also create a framework to analyze the trade-offs between performance and energy consumption. We design techniques to maximize performance in a dynamic environment that has a constraint on the energy consumption. Another part of the study explores environments that have uncertainty in the availability of the compute resources. For this part, we design heuristics and compare their performance in different types of environments

Task matching and scheduling in heterogeneous computing environments using a genetic-algorithm-based approach

Includes bibliographical references (pages 21-22).To exploit a heterogeneous computing (HC) environment, an application task may be decomposed into subtasks that have data dependencies. Subtask matching and scheduling consists of assigning subtasks to machines, ordering subtask execution for each machine, and ordering intermachine data transfers. The goal is to achieve the minimal completion time for the task. A heuristic approach based on a genetic algorithm is developed to do matching and scheduling in HC environments. It is assumed that the matcher/scheduler is in control of a dedicated HC suite of machines. The characteristics of this genetic-algorithm-based approach include: separation of the matching and the scheduling representations, independence of the chromosome structure from the details of the communication subsystem, and consideration of overlap among all computations and communications that obey subtask precedence constraints. It is applicable to the static scheduling of production jobs and can be readily used to collectively schedule a set of tasks that are decomposed into subtasks. Some parameters and the selection scheme of the genetic algorithm were chosen experimentally to achieve the best performance. Extensive simulation tests were conducted. For small-sized problems (e.g., a small number of subtasks and a small number of machines), exhaustive searches were used to verify that this genetic-algorithm-based approach found the optimal solutions. Simulation results for larger-sized problems showed that this genetic-algorithm-based approach outperformed two nonevolutionary heuristics and a random search

Mapping of subtasks with multiple versions in a heterogeneous ad hoc grid environment

Includes bibliographical references (pages 7-8).An ad hoc grid is a heterogeneous computing system composed of mobile devices. The problem studied here is to statically assign resources to the subtasks of an application, which has an execution time constraint, when the resources are oversubscribed. Each subtask has a preferred version, and a secondary version that uses fewer resources. The goal is to assign resources so that the application meets its execution time constraint while minimizing the number of secondary versions used. Five resource allocation heuristics to derive near-optimal solutions to this problem are presented and evaluated