Prediction-Based Resource Deployment and Task Scheduling in Edge-Cloud Collaborative Computing

Su, Mingfeng; Wang, Guojun; Choo, Kim‐Kwang Raymond

doi:10.1155/2022/2568503

Cited by 8 publications

(5 citation statements)

References 53 publications

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“…Essentially, the optimal task scheduling strategy is different and not always the default strategy [21,22] . Hence, we leveraged the machine learning techniques and predicted which strategy to use for the coming task based on the task workload and user experience.…”

Section: Motivationmentioning

confidence: 99%

ETS-TEE: An Energy-Efficient Task Scheduling Strategy in a Mobile Trusted Computing Environment

Wang

Cai

et al. 2023

Tsinghua Sci. Technol.

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A trusted execution environment (TEE) is a system-on-chip and CPU system with a wide security solution available on today's Arm application (APP) processors, which dominate the smartphone market. Generally, mobile APPs create a trusted application (TA) in the TEE to process sensitive information, such as payment or message encryption, which is transparent to the APPs running in the rich execution environments (REEs). In detail, the REE and TEE interact and eventually send back the results to the APP in the REE through the interface provided by the TA.Such an operation definitely increases the overhead of mobile APPs. In this paper, we first present a comprehensive analysis of the performance of open-source TEE encrypted text. We then propose a high energy-efficient task scheduling strategy (ETS-TEE). By leveraging the deep learning algorithm, our policy considers the complexity of TA tasks, which are dynamically scheduled between modeling on the local device and offloading to an edge server.We evaluate our approach on Raspberry Pi 3B as the local mobile device and Jetson TX2 as the edge server. The results show that compared with the default scheduling strategy on the local device, our approach achieves an average of 38.0% energy reduction and 1.6×speedup. This greatly reduces the performance loss caused by mobile devices in order to protect the safe execution of applications, so that the trusted execution environment has both security and high performance.

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Section: Motivationmentioning

confidence: 99%

ETS-TEE: An Energy-Efficient Task Scheduling Strategy in a Mobile Trusted Computing Environment

Wang

Cai

et al. 2023

Tsinghua Sci. Technol.

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“…Li et al [ 8 ] proposed a genetic-algorithm-based two-stage heuristic for joint computation offloading and resource allocation in multi-user and multi-server scenarios, and they proved the effectiveness of their algorithm for reducing terminal energy consumption. Su et al [ 18 ] proposed a resource deployment and task scheduling algorithm based on task prediction and Pareto optimization. The user service quality and system service effect were significantly improved.…”

Section: Related Workmentioning

confidence: 99%

“…Equation (18) denotes the principle of conflict avoidance between individuals, A denotes the factor of conflict avoidance between individuals, G is gravity, and c 1 , c 2 , c 3 is a random number between [0, 1], respectively. H represents the social interaction between individuals, and p min , p max are the initial and subordinate velocities of social interactions between individuals, respectively, setting p min = 1, p max = 4.…”

Section: Jet Propulsionmentioning

confidence: 99%

Research on Cloud-Edge-End Collaborative Computing Offloading Strategy in the Internet of Vehicles Based on the M-TSA Algorithm

Guo

Wang

2023

Sensors

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In the Internet of Vehicles scenario, the in-vehicle terminal cannot meet the requirements of computing tasks in terms of delay and energy consumption; the introduction of cloud computing and MEC is an effective way to solve the above problem. The in-vehicle terminal requires a high task processing delay, and due to the high delay of cloud computing to upload computing tasks to the cloud, the MEC server has limited computing resources, which will increase the task processing delay when there are more tasks. To solve the above problems, a vehicle computing network based on cloud-edge-end collaborative computing is proposed, in which cloud servers, edge servers, service vehicles, and task vehicles themselves can provide computing services. A model of the cloud-edge-end collaborative computing system for the Internet of Vehicles is constructed, and a computational offloading strategy problem is given. Then, a computational offloading strategy based on the M-TSA algorithm and combined with task prioritization and computational offloading node prediction is proposed. Finally, comparative experiments are conducted under task instances simulating real road vehicle conditions to demonstrate the superiority of our network, where our offloading strategy significantly improves the utility of task offloading and reduces offloading delay and energy consumption.

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“…With the rapid development of AI, IoT and other information technologies, the scheduling mode of various products has also undergone great changes [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. For example, based on the improved NSGA-II algorithm, literature [1] built an emergency scheduling model for major emergencies and materials such as people's livelihood, medical care and logistics [1].…”

Section: Introductionmentioning

confidence: 99%

Integrated scheduling algorithm with Dynamic Adjustment on Machine Idle Time

Zhou,

Ding,

Xie

et al. 2024

Preprint

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During the scheduling of the production of small batch and multi-variety products with treelike structure, many devices are idle due to the process constraints. This increases the makespan of production and reduces the utilization rate of the equipment. Aiming at improving this situation, a resource cooperative scheduling algorithm is proposed to dynamically adjust the idle time of equipment: the integrated scheduling algorithm with dynamic adjustment on machine idle time(ISA_DA). This algorithm adopts the critical path chain and the optimal scheduling strategy. Besides, the adjustment strategy considering the backward movement of the process is put forward to improve the intensity of the close connection of the process and the intensity of parallel processing. Experimental results show that the effect of the proposed algorithm is better than the comparison algorithms: the hierarchical scheduling order algorithm and the process tree cyclic decomposition algorithm. It can increase the equipment utilization rate by 10% and 11.5% respectively, and reduce the makespan by 28.6%.

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Prediction-Based Resource Deployment and Task Scheduling in Edge-Cloud Collaborative Computing

Cited by 8 publications

References 53 publications

ETS-TEE: An Energy-Efficient Task Scheduling Strategy in a Mobile Trusted Computing Environment

ETS-TEE: An Energy-Efficient Task Scheduling Strategy in a Mobile Trusted Computing Environment

Research on Cloud-Edge-End Collaborative Computing Offloading Strategy in the Internet of Vehicles Based on the M-TSA Algorithm

Integrated scheduling algorithm with Dynamic Adjustment on Machine Idle Time

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