An ad hoc grid is a heterogeneous computing and

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.

An ad hoc grid is a heterogeneous computing and communication system that allows a group of mobile devices to accomplish a mission, often in a hostile environment. Energy management is a major concern in ad hoc grids. The problem studied here focuses on statically assigning resources in an ad hoc grid to an application composed of communicating subtasks. The goal of the allocation is to minimize the average percentage of energy consumed by the application to execute across the machines in the ad hoc grid, while meeting an application execution time constraint. This pre-computed allocation is then used when the application is deployed in a mission. Six different heuristic approaches of varying time complexities have been designed and compared via simulations to solve this ad hoc grid allocation problem. Also, a lower bound based on the performance metric has been designed to compare the performance of the heuristics developed.

Abstract-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.

An ad hoc grid is a heterogeneous computing system composed of mobile devices. Each computing resource is constrained in battery energy. The problem being studied is to assign statically computing resources to the subtasks of an application that has an execution time constraint, when the resources are oversubscribed. All subtasks must be executed; to accommodate this in an oversubscribed environment, each subtask has two versions: the primary or full version, and the secondary or degraded version. The secondary version utilizes only 10% of the resources that the primary version requires, and produces only 10% of the data output for the subsequent children subtasks. Thus, the degraded version (secondary version) represents a reduced capability of lesser overall value, while consuming fewer resources. The goal is to assign resources so that the application meets an execution time constraint and the battery energy constraint while minimizing the number of degraded versions used. Five resource allocation heuristics to derive near-optimal solutions to this problem are presented, evaluated, and compared.