Response time and energy consumption co-offloading with SLRTA algorithm in cloud–edge collaborative computing

Tong, Zhao; Deng, Xiaomei; Mei, Jing; Liu, Bilan; Li, Keqin

doi:10.1016/j.future.2021.11.014

Cited by 9 publications

(2 citation statements)

References 24 publications

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“…Combining cloud and edge computing can optimize execution efficiency and system power consumption. The related research on collaborative computing mainly focuses on the collaborative computing of cloud computing and edge computing ( Tong et al, 2022 ), with the optimization objectives of completion time, energy consumption, and the weighted sum of time and energy. There are already several methods for the synergy between cloud and edge computing.…”

Section: Related Workmentioning

confidence: 99%

Optimization design of railway logistics center layout based on mobile cloud edge computing

Zhang

2023

PeerJ Computer Science

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With the development of the economy, the importance of railway freight transportation has become essential. The efficiency of a railway logistics center depends on the types, quantities, information exchange, and layout optimization. Edge collaboration technology can consider the advantages of cloud computing’s rich computing storage resources and low latency. It can also provide additional computing power and real-time requirements for intelligent railway logistics construction. However, the cloud-side collaboration technology will introduce the wireless communication delay between the mobile terminal and the edge computing server. We designed a two-tier unloading strategy algorithm and solved the optimization problem by determining the unloading decision of each task. The cost of every task is calculated in the onboard device calculation, vehicular edge computing (VEC), and cloud computing server calculation. Simulation results show that the proposed method can save about 40% time delay compared to other unloading strategies.

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Section: Related Workmentioning

confidence: 99%

Optimization design of railway logistics center layout based on mobile cloud edge computing

Zhang

2023

PeerJ Computer Science

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“…As the number of data increases exponentially, the communication delay between interacting services becomes a nonignored problem for service offloading. roughout the studies for services offloading [16,17], although some works have a focus on the collaborative problem of cloud and edge servers [13,18], a few studies considered the collaboration of services. In summary, the mobility of vehicles and the interrelationships between services bring new challenges for service offloading.…”

Section: Introductionmentioning

confidence: 99%

CSO-DRL: A Collaborative Service Offloading Approach with Deep Reinforcement Learning in Vehicular Edge Computing

Huang

Cao

Zhang

et al. 2022

Scientific Programming

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In vehicular edge computing, vehicles move along the road and request the services from the nearest edge servers with low latency. Due to the limitation of computation capacity of vehicular devices, the services should be offloaded on RSUs equipped with edge servers to provide service with low latency. Noticed that the location of service offloading may affect the service requesting delay directly, and it may exist some interrelationship between interacting services; all of these are rarely considered in recent studies. To address such problems, we propose a collaborative service offloading approach with deep reinforcement learning in vehicular edge computing named CSO-DRL. Our approach first divides the road segments by k-means-based algorithm through analyzing the trajectory data of vehicles, and then the offloading location is determined by observing the vehicle running status. Secondly, the interacting services are discovered by a parallel frequent pattern-based algorithm efficiently. Furthermore, the collaborative service offloading algorithm is presented by the DDPG model for offloading the interacting services, which can minimize the service requesting delay and data communication delay between interacting services. Finally, the efficiency of the algorithm is evaluated by real-world data-based simulation experimental evaluations. The results show our algorithm can obtain a lower delay than other baseline algorithms in searching for the optimal service offloading strategy.

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Efficient job scheduling paradigm based on hybrid sparrow search algorithm and differential evolution optimization for heterogeneous cloud computing platforms

Khaleel

2023

Internet of Things

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Response time and energy consumption co-offloading with SLRTA algorithm in cloud–edge collaborative computing

Cited by 9 publications

References 24 publications

Optimization design of railway logistics center layout based on mobile cloud edge computing

Optimization design of railway logistics center layout based on mobile cloud edge computing

CSO-DRL: A Collaborative Service Offloading Approach with Deep Reinforcement Learning in Vehicular Edge Computing

Efficient job scheduling paradigm based on hybrid sparrow search algorithm and differential evolution optimization for heterogeneous cloud computing platforms

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