TY - GEN
T1 - Dynamic mapping in energy constrained heterogeneous computing systems
AU - Kim, Jong Kook
AU - Siegel, H. J.
AU - Maciejewski, Anthony A.
AU - Eigenmann, Rudolf
PY - 2005
Y1 - 2005
N2 - An ad hoc grid is a wireless heterogeneous computing environment without a fixed infrastructure. The wireless devices 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.
AB - An ad hoc grid is a wireless heterogeneous computing environment without a fixed infrastructure. The wireless devices 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.
UR - http://www.scopus.com/inward/record.url?scp=33746322239&partnerID=8YFLogxK
U2 - 10.1109/IPDPS.2005.177
DO - 10.1109/IPDPS.2005.177
M3 - Conference contribution
AN - SCOPUS:33746322239
SN - 0769523129
SN - 0769523129
SN - 9780769523125
T3 - Proceedings - 19th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2005
SP - 64a
BT - Proceedings - 19th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2005
T2 - 19th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2005
Y2 - 4 April 2005 through 8 April 2005
ER -