Offloading application to cloud can augment mobile devices' computation capabilities for the emerging resource-hungry mobile application, however it can also consume both much time and energy for mobile device off...Offloading application to cloud can augment mobile devices' computation capabilities for the emerging resource-hungry mobile application, however it can also consume both much time and energy for mobile device offloading application remotely to cloud. In this paper, we develop a newly adaptive application offloading decision-transmission scheduling scheme which can solve above problem efficiently. Specifically, we first propose an adaptive application offloading model which allows multiple target clouds coexisting. Second, based on Lyapunov optimization theory, a low complexity adaptive offloading decision-transmission scheduling scheme has been proposed. And the performance analysis is also given. Finally, simulation results show that,compared with that all applications are executed locally, mobile device can save 68.557% average execution time and 67.095% average energy consumption under situations.展开更多
Edge computation offloading allows mobile end devices to execute compute-inten?sive tasks on edge servers. End devices can decide whether the tasks are offloaded to edge servers, cloud servers or executed locally acco...Edge computation offloading allows mobile end devices to execute compute-inten?sive tasks on edge servers. End devices can decide whether the tasks are offloaded to edge servers, cloud servers or executed locally according to current network condition and devic?es'profiles in an online manner. In this paper, we propose an edge computation offloading framework based on deep imitation learning (DIL) and knowledge distillation (KD), which assists end devices to quickly make fine-grained decisions to optimize the delay of computa?tion tasks online. We formalize a computation offloading problem into a multi-label classifi?cation problem. Training samples for our DIL model are generated in an offline manner. Af?ter the model is trained, we leverage KD to obtain a lightweight DIL model, by which we fur?ther reduce the model's inference delay. Numerical experiment shows that the offloading de?cisions made by our model not only outperform those made by other related policies in laten?cy metric, but also have the shortest inference delay among all policies.展开更多
Mobile devices are increasingly interacting with clouds,and mobile cloud computing has emerged as a new paradigm.An central topic in mobile cloud computing is computation partitioning,which involves partitioning the e...Mobile devices are increasingly interacting with clouds,and mobile cloud computing has emerged as a new paradigm.An central topic in mobile cloud computing is computation partitioning,which involves partitioning the execution of applications between the mobile side and cloud side so that execution cost is minimized.This paper discusses computation partitioning in mobile cloud computing.We first present the background and system models of mobile cloud computation partitioning systems.We then describe and compare state-of-the-art mobile computation partitioning in terms of application modeling,profiling,optimization,and implementation.We point out the main research issues and directions and summarize our own works.展开更多
基金supported by National Natural Science Foundation of China (Grant No.61261017, No.61571143 and No.61561014)Guangxi Natural Science Foundation (2013GXNSFAA019334 and 2014GXNSFAA118387)+3 种基金Key Laboratory of Cognitive Radio and Information Processing, Ministry of Education (No.CRKL150112)Guangxi Key Lab of Wireless Wideband Communication & Signal Processing (GXKL0614202, GXKL0614101 and GXKL061501)Sci.and Tech.on Info.Transmission and Dissemination in Communication Networks Lab (No.ITD-U14008/KX142600015)Graduate Student Research Innovation Project of Guilin University of Electronic Technology (YJCXS201523)
文摘Offloading application to cloud can augment mobile devices' computation capabilities for the emerging resource-hungry mobile application, however it can also consume both much time and energy for mobile device offloading application remotely to cloud. In this paper, we develop a newly adaptive application offloading decision-transmission scheduling scheme which can solve above problem efficiently. Specifically, we first propose an adaptive application offloading model which allows multiple target clouds coexisting. Second, based on Lyapunov optimization theory, a low complexity adaptive offloading decision-transmission scheduling scheme has been proposed. And the performance analysis is also given. Finally, simulation results show that,compared with that all applications are executed locally, mobile device can save 68.557% average execution time and 67.095% average energy consumption under situations.
基金This work was supported in part by the National Science Foundation of China under Grant No.61972432the Program for Guangdong Introduc⁃ing Innovative and Entrepreneurial Teams under Grant No.2017ZT07X355.
文摘Edge computation offloading allows mobile end devices to execute compute-inten?sive tasks on edge servers. End devices can decide whether the tasks are offloaded to edge servers, cloud servers or executed locally according to current network condition and devic?es'profiles in an online manner. In this paper, we propose an edge computation offloading framework based on deep imitation learning (DIL) and knowledge distillation (KD), which assists end devices to quickly make fine-grained decisions to optimize the delay of computa?tion tasks online. We formalize a computation offloading problem into a multi-label classifi?cation problem. Training samples for our DIL model are generated in an offline manner. Af?ter the model is trained, we leverage KD to obtain a lightweight DIL model, by which we fur?ther reduce the model's inference delay. Numerical experiment shows that the offloading de?cisions made by our model not only outperform those made by other related policies in laten?cy metric, but also have the shortest inference delay among all policies.
基金supported in part by Hong Kong RGC under GRF Grant 510412the National High-Technology Research and Development Program (863 Program) of China under Grant 2013AA01A212.
文摘Mobile devices are increasingly interacting with clouds,and mobile cloud computing has emerged as a new paradigm.An central topic in mobile cloud computing is computation partitioning,which involves partitioning the execution of applications between the mobile side and cloud side so that execution cost is minimized.This paper discusses computation partitioning in mobile cloud computing.We first present the background and system models of mobile cloud computation partitioning systems.We then describe and compare state-of-the-art mobile computation partitioning in terms of application modeling,profiling,optimization,and implementation.We point out the main research issues and directions and summarize our own works.
