Intelligent task prediction and computation offloading based on mobile-edge cloud computing

Miao, Yiming; Wu, Guang; Li, Miao; Ghoneim, Ahmed; Al-Rakhami, Mabrook; Hossain, M. Shamim

doi:10.1016/j.future.2019.09.035

Cited by 136 publications

(56 citation statements)

References 12 publications

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“…Han et al [9] developed a unified framework that minimizes the overall outage probability in various mobile computation offloading scenarios. Miao et al [10] put forward a new intelligent computation offloading based on MECC architecture in combination with artificial intelligence (AI) technology with the increasing requirements and services of mobile users, and an offloading strategy of simple edge computing is no longer applicable to MEC architecture. In 2019, the U.S. DOD officially announced that Microsoft will be responsible for building a cloud computing system for the U.S. military with a project cost of up to 10 billion dollars.…”

Section: Related Workmentioning

confidence: 99%

Dispersed Computing for Tactical Edge in Future Wars: Vision, Architecture, and Challenges

Yang

Sun

et al. 2021

Wireless Communications and Mobile Computing

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In the future, the tactical edge is far away from the command center, the resources of communication and computing are limited, and the battlefield situation is changing rapidly, which leads to the weak connection and fast changes of network topology in a harsh and complex battlefield environment. Thus, to meet the needs of communication and computing to build a new generation of computing architecture for real-time sharing and service collaboration of tactical edge resources to win the future war, the dispersed computing (DCOMP) seeks a new solution to satisfy the requirements of fast and efficient sensing, transmission, integrating, scheduling, and processing of various information in the tactical edge. Through the research of a traditional computing paradigm of mobile cloud computing (MCC), fog computing (FC), mobile edge computing (MEC), mobile ad hoc network (MANET), etc., it can be found that these computations have difficulty in meeting the high changing and complex battlefield environment and we propose a novel architecture of DCOMP to build a scalable, extensible, and robust decision-making system, to realize powerful and secure communication, computing, storage, and information processing capabilities for the tactical edge. We illustrate the fundamental principles of building a network model, channel allocation, and forwarding control mechanism of the network architecture for DCOMP called DANET and then design a new architecture, programming model, task awareness, and computing scheduling for DCOMP. Finally, we discuss the main requirements and challenges of DCOMP in future wars.

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Section: Related Workmentioning

confidence: 99%

Dispersed Computing for Tactical Edge in Future Wars: Vision, Architecture, and Challenges

Yang

Sun

et al. 2021

Wireless Communications and Mobile Computing

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“…Miao et al [23] propose a prediction-based computation offloading and task migration algorithm to reduce the processing delay of users' applications. However, it is impossible to predict future values accurately, especially in such a dynamic and random MEC environment.…”

Section: General Approaches For Task Offloadingmentioning

confidence: 99%

Energy-aware task offloading with deadline constraint in mobile edge computing

Chang

et al. 2021

J Wireless Com Network

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With the development of the wireless network, increasing mobile applications are emerging and receiving great popularity. These applications cover a wide area, such as traffic monitoring, smart homes, real-time vision processing, objective tracking, and so on, and typically require computation-intensive resources to achieve a high quality of experience. Although the performance of mobile devices (MDs) has been continuously enhanced, running all the applications on a single MD still causes high energy consumption and latency. Fortunately, mobile edge computing (MEC) allows MDs to offload their computation-intensive tasks to proximal eNodeBs (eNBs) to augment computational capabilities. However, the current task offloading schemes mainly concentrate on average-based performance metrics, failing to meet the deadline constraint of the tasks. Based on the deep reinforcement learning (DRL) approach, this paper proposes an Energy-aware Task Offloading with Deadline constraint (DRL-E2D) algorithm for a multi-eNB MEC environment, which is to maximize the reward under the deadline constraint of the tasks. In terms of the actor-critic framework, we integrate the action representation into DRL-E2D to handle the large discrete action space problem, i.e., using the low-complexity k-nearest neighbor as an approximate approach to extract optimal discrete actions from the continuous action space. The extensive experimental results show that DRL-E2D achieves better performance than the comparison algorithms on all parameter settings, indicating that DRL-E2D is robust to the state changes in the MEC environment.

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“…A very few works concern the QoE as a metric directly. energy Gao et al [49] independent full cost Chen et al [50] independent full cost Chen et al [51] independent full profit Yuan et al [52] independent full profit Lin et al [53] independent full performance, energy Du et al [54] independent full performance, energy Duan et al [55] independent full performance, energy Mahmud et al [56] independent full performance, profit Li et al [57] independent full Performance, cost Sun et al [58] independent full performance, cost Adhikari et al [59] independent full performance, utilization Ma et al [60] independent full QoE, cost Miao et al [61] independent partial performance Kai et al [62] independent partial performance Guo et al [63] independent partial performance Meng et al [64], [65] independent partial performance hop-e Cui et al [66], [67] independent partial performance hop-d, hop-e Sarkar et al [68] independent partial performance hop-e Ouyang et al [69] independent partial performance Y Cheng et al [70] independent partial energy Xia et al [71] independent partial energy Zhang et al [72] independent partial cost Chabbouh et al [73] independent partial performance, balance Y Wang et al [74] independent partial performance, cost Zhao et al [75] independent partial performance, cost Khayyat et al [76] independent partial performance, energy Alshahrani et al [77] independent partial performance, energy Chen et al [78] independent partial performance, cost, energy Hong et al [16] independent partial performance, energy hop-d Sun et al [79] independent partial performance, energy Long et al [80] independent partial performance, energy Nguyen et al…”

Section: Optimization Objectivementioning

confidence: 99%

“…Miao et al [61] present a task offloading for optimizing the delay for each user task. They propose to use Long Short-Term Memory (LSTM) method for predicting the data size for each task.…”

Section: ) Partial Offloading A: Response Time Optimizationmentioning

confidence: 99%

A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing

et al. 2020

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Edge-cloud computing, combining the benefits of both edge computing and cloud computing, is one of the most promising ways to address the resource insufficiency of smart devices. Task offloading is an important challenge must be addressed for edge-cloud computing in practice, which decides the place and the time for performing each task. Even though there is existing research focusing on the task offloading in edge-cloud computing, a lot of problems should be solved before the application of these offloading technologies. Thus, in this article, we first propose a taxonomy of task offloading in edge-cloud environments to investigate and classify related research articles, and then summarize several challenges which have not been addressed for future research directions on this area to promote the development of edge-cloud market.

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Intelligent task prediction and computation offloading based on mobile-edge cloud computing

Cited by 136 publications

References 12 publications

Dispersed Computing for Tactical Edge in Future Wars: Vision, Architecture, and Challenges

Dispersed Computing for Tactical Edge in Future Wars: Vision, Architecture, and Challenges

Energy-aware task offloading with deadline constraint in mobile edge computing

A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing

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