TY - GEN
T1 - MobiDiC
T2 - 14th IEEE International Conference on Pervasive Computing and Communications, PerCom 2016
AU - Pandey, Parul
AU - Pompili, Dario
PY - 2016/4/19
Y1 - 2016/4/19
N2 - Mobile computing is one of the largest untapped reservoirs in today's pervasive computing world as it has the potential to enable a variety of in-situ, real-time applications. Yet, this computing paradigm suffers when the available resources - such as device battery, CPU cycles, memory, I/O data rate - are limited. In this paper, the new paradigm of approximate computing is proposed to harness such potential and to enable real-time computation-intensive mobile applications in resource-limited and uncertain environments. A reduction in time and energy consumed by an application is obtained via approximate computing by decreasing the amount of computation needed by different tasks in an application; such improvement, however, comes with the potential loss in accuracy. Hence, a Mobile Distributed Computing framework, MobiDiC, is introduced to determine offline the 'approximable' tasks in an application and a light-weight algorithm is devised to select the approximate version of the tasks in an application during run-time. The effectiveness of the proposed approach is validated through extensive simulation and testbed experiments by comparing approximate versus exact-computation performance.
AB - Mobile computing is one of the largest untapped reservoirs in today's pervasive computing world as it has the potential to enable a variety of in-situ, real-time applications. Yet, this computing paradigm suffers when the available resources - such as device battery, CPU cycles, memory, I/O data rate - are limited. In this paper, the new paradigm of approximate computing is proposed to harness such potential and to enable real-time computation-intensive mobile applications in resource-limited and uncertain environments. A reduction in time and energy consumed by an application is obtained via approximate computing by decreasing the amount of computation needed by different tasks in an application; such improvement, however, comes with the potential loss in accuracy. Hence, a Mobile Distributed Computing framework, MobiDiC, is introduced to determine offline the 'approximable' tasks in an application and a light-weight algorithm is devised to select the approximate version of the tasks in an application during run-time. The effectiveness of the proposed approach is validated through extensive simulation and testbed experiments by comparing approximate versus exact-computation performance.
KW - Approximate computing
KW - Mobile device clouds
KW - Mobile perception application
KW - Workflows
UR - http://www.scopus.com/inward/record.url?scp=84969135648&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84969135648&partnerID=8YFLogxK
U2 - 10.1109/PERCOM.2016.7456515
DO - 10.1109/PERCOM.2016.7456515
M3 - Conference contribution
AN - SCOPUS:84969135648
T3 - 2016 IEEE International Conference on Pervasive Computing and Communications, PerCom 2016
BT - 2016 IEEE International Conference on Pervasive Computing and Communications, PerCom 2016
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 14 March 2016 through 19 March 2016
ER -