TY - GEN
T1 - Task Offloading with Execution Cost Minimization in Heterogeneous Mobile Cloud Computing
AU - Liu, Xing
AU - Guo, Songtao
AU - Yang, Yuanyuan
N1 - Publisher Copyright: © Springer Nature Singapore Pte Ltd. 2018.
PY - 2018
Y1 - 2018
N2 - Mobile cloud computing (MCC) can significantly enhance computation capability and save energy of smart mobile devices (SMDs) by offloading remoteable tasks from resources-constrained SMDs onto the resource-rich cloud. However, it remains a challenge issue how to appropriately partition applications and select the suitable cloud to offload the task under the constraints of execution cost including completion time of the application and energy consumption of SMDs. To address such a challenge, in this paper, we first formulate the partitioning and cloud selection problem into execution cost minimization problem. To solve the optimization problem, we then propose a system framework for adaptive partitioning and dynamic selective offloading. Based on the framework, we design an optimal cloud selection algorithm with execution cost minimization which consists of offloading judgement and cloud selection. Finally, our experimental results in a real testbed demonstrate that our framework can effectively reduce the execution cost compared with other frameworks.
AB - Mobile cloud computing (MCC) can significantly enhance computation capability and save energy of smart mobile devices (SMDs) by offloading remoteable tasks from resources-constrained SMDs onto the resource-rich cloud. However, it remains a challenge issue how to appropriately partition applications and select the suitable cloud to offload the task under the constraints of execution cost including completion time of the application and energy consumption of SMDs. To address such a challenge, in this paper, we first formulate the partitioning and cloud selection problem into execution cost minimization problem. To solve the optimization problem, we then propose a system framework for adaptive partitioning and dynamic selective offloading. Based on the framework, we design an optimal cloud selection algorithm with execution cost minimization which consists of offloading judgement and cloud selection. Finally, our experimental results in a real testbed demonstrate that our framework can effectively reduce the execution cost compared with other frameworks.
KW - Application partition
KW - Cloud selection
KW - Execution cost minimization
KW - Mobile cloud computing
KW - Task offloading
UR - https://www.scopus.com/pages/publications/85146150509
U2 - 10.1007/978-981-10-8890-2_39
DO - 10.1007/978-981-10-8890-2_39
M3 - Conference contribution
SN - 9789811088896
T3 - Communications in Computer and Information Science
SP - 509
EP - 522
BT - Mobile Ad-hoc and Sensor Networks - 13th International Conference, MSN 2017, Revised Selected Papers
A2 - Zhu, Liehuang
A2 - Zhong, Sheng
PB - Springer Science and Business Media Deutschland GmbH
T2 - 13th International Conference on Mobile Ad-hoc and Sensor Networks, MSN 2017
Y2 - 17 December 2017 through 20 December 2017
ER -