Optimal Power Management for Server Farm to Support Green Computing
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
Slack allocation algorithm for parallel machines
Journal of Parallel and Distributed Computing
Cooperative energy management in distributed wireless real-time systems
Wireless Networks
Dynamic resource management for a cell-based distributed mobile computing environment
UIC'11 Proceedings of the 8th international conference on Ubiquitous intelligence and computing
SLA-based resource provisioning for heterogeneous workloads in a virtualized cloud datacenter
ICA3PP'11 Proceedings of the 11th international conference on Algorithms and architectures for parallel processing - Volume Part I
Rolling-horizon scheduling for energy constrained distributed real-time embedded systems
Journal of Systems and Software
Adaptive energy-efficient scheduling for real-time tasks on DVS-enabled heterogeneous clusters
Journal of Parallel and Distributed Computing
Enhancing the performance of a distributed mobile computing environment by topology construction
ICA3PP'12 Proceedings of the 12th international conference on Algorithms and Architectures for Parallel Processing - Volume Part II
Efficient task scheduling for hard real-time tasks in asymmetric multicore processors
ICA3PP'12 Proceedings of the 12th international conference on Algorithms and Architectures for Parallel Processing - Volume Part II
3E: Energy-efficient elastic scheduling for independent tasks in heterogeneous computing systems
Journal of Systems and Software
Deadline and energy constrained dynamic resource allocation in a heterogeneous computing environment
The Journal of Supercomputing
Energy-Aware Scheduling on Multicore Heterogeneous Grid Computing Systems
Journal of Grid Computing
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An ad hoc grid is a wireless heterogeneous computing environment without a fixed infrastructure. This study considers wireless devices that have different capabilities, have limited battery capacity, support dynamic voltage scaling, and are expected to be used for eight hours at a time and then recharged. 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 while considering the energy constraints of the devices. In the single-hop ad hoc grid heterogeneous environment considered in this study, tasks arrive unpredictably, are independent (i.e., no precedent constraints for tasks), and have priorities and deadlines. The problem is to map (match and schedule) tasks onto devices such that the number of highest priority tasks completed by their deadlines during eight hours is maximized while efficiently utilizing the overall system energy. A model for dynamically mapping tasks onto wireless devices is introduced. Seven dynamic mapping heuristics for this environment are designed and compared to each other and to a mathematical bound.