Online Deadline-Aware Task Dispatching and Scheduling in Edge Computing

Meng, Jiaying; Tan, Haisheng; Li, Xiangyang; Han, Zhenhua; Li, Bojie

doi:10.1109/tpds.2019.2961905

Cited by 88 publications

(37 citation statements)

References 27 publications

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“…Considering that the uplink transmission power of each user is a fixed value, and the uplink and downlink bandwidth of user k changes in proportion to its channel gain [18], suppose that the optimization variable includes the number of shared input bits uploaded by user k D s k , and the cloud server allocates it for sharing the scale factor f s for the number of CPU cycles, the cloud server allocates a scale factor f k for executing the number of independent CPU cycles for different users k, and the base station corresponds to the downlink transmission power P d k of the user k and the downlink power P d m for multicast.…”

Section: Uplink Equal Power Allocationmentioning

confidence: 99%

“…Consider a training venue where 10 users are running AR sports dance applications. e users are randomly distributed in the training venue, and the wireless channel meets the Rayleigh fading [18]. In the sports dance movement system, each user needs to upload dance movement data D u k of 10 6 bits, each user needs to receive dance data D d k of 10 6 bits, and the total bandwidth of the uplink and downlink channels B u and B d is 1 × 10 8 Hz.…”

Section: Performance Evaluationmentioning

confidence: 99%

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Sports Dance Movement Assessment Method Using Augment Reality and Mobile Edge Computing

Chu

2021

Mobile Information Systems

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AR (Augment Reality) is an emerging technology that combines computer technology and simulation technology. It uses a computer to generate a simulation environment to immerse users in the environment. AR can simulate the real environment or things and present it to users by virtue of its multiperceptual, interactive, immersive, and other characteristics, to achieve an immersive effect. For sports dance, the same can be used to enhance the effect of teaching and learning through the use of AR technology. Aiming at the problems of delay and terminal equipment energy consumption caused by high-speed data transmission and calculation of virtual technology, this paper proposes a sports dance movement transmission scheme that uses equal power distribution on the uplink. Firstly, based on the collaborative attributes of the AR sports dance business, a system model for AR characteristics is established; secondly, the system frame structure is analyzed in detail, and the constraint conditions are established to minimize the total energy consumption of the system; finally, the mathematical model of Mobile Edge Computing (MEC) based on convex optimization is established under the condition that the delay and power consumption meet the constraints, so as to obtain the optimal communication and computing resource allocation scheme of sports dance in AR. The experimental results reveal that the proposed sports dance movement assessment method based on AR and MEC is efficient.

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Section: Uplink Equal Power Allocationmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

Sports Dance Movement Assessment Method Using Augment Reality and Mobile Edge Computing

Chu

2021

Mobile Information Systems

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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%

“…Ment et al [64], [65] propose an online method, Dedas, trying to maximize the number of tasks that meet the deadlines and minimize the average completion time (ACT) of the tasks, by jointly scheduling of networking and computing resources. In an edge server or the cloud, Dedas inserts the new task in a position or replaces an existing task if there is a deadline violation due to adding the new task, to generate a feasible schedule with the minimum ATC.…”

Section: ) Partial Offloading A: Response Time Optimizationmentioning

confidence: 99%

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

Wang

Huang

et al. 2020

IEEE Access

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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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“…A heuristic algorithm was proposed in [ 24 ] to address the energy-efficient and delay-sensitive task scheduling in IoT edge computing. In [ 25 ], the task scheduling and dispatching of networking and computing resources were investigated to maximize the number of completed tasks. These methods are based on an ideal mathematical model and optimized by a mixed-integer non-linear programming (MINLP) or heuristic algorithms.…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning-Based Task Scheduling in IoT Edge Computing

Sheng

Chen

et al. 2021

Sensors

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Edge computing (EC) has recently emerged as a promising paradigm that supports resource-hungry Internet of Things (IoT) applications with low latency services at the network edge. However, the limited capacity of computing resources at the edge server poses great challenges for scheduling application tasks. In this paper, a task scheduling problem is studied in the EC scenario, and multiple tasks are scheduled to virtual machines (VMs) configured at the edge server by maximizing the long-term task satisfaction degree (LTSD). The problem is formulated as a Markov decision process (MDP) for which the state, action, state transition, and reward are designed. We leverage deep reinforcement learning (DRL) to solve both time scheduling (i.e., the task execution order) and resource allocation (i.e., which VM the task is assigned to), considering the diversity of the tasks and the heterogeneity of available resources. A policy-based REINFORCE algorithm is proposed for the task scheduling problem, and a fully-connected neural network (FCN) is utilized to extract the features. Simulation results show that the proposed DRL-based task scheduling algorithm outperforms the existing methods in the literature in terms of the average task satisfaction degree and success ratio.

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Online Deadline-Aware Task Dispatching and Scheduling in Edge Computing

Cited by 88 publications

References 27 publications

Sports Dance Movement Assessment Method Using Augment Reality and Mobile Edge Computing

Sports Dance Movement Assessment Method Using Augment Reality and Mobile Edge Computing

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

Deep Reinforcement Learning-Based Task Scheduling in IoT Edge Computing

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