Energy-efficient offloading decision-making for mobile edge computing in vehicular networks

Huang, Xiaoge; Xu, Ke; Lai, Chenbin; Chen, Qianbin; Zhang, Jie

doi:10.1186/s13638-020-1652-5

Cited by 48 publications

(22 citation statements)

References 32 publications

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“…The delay budget T is the actual delay budget of the application less the time needed to run Algorithm 1. Energy conversion coefficients κ L = 10 −27 , κ C = 0.3 * 10 −27 [18] and the required CPU cycles for computing 1-bit data are γ = 1000 cycle/bit [33]. For large-scale fading, the distances for IoT device-edge cloud, IoT device-collaborator and collaborator-edge cloud are 300 m, 50 m and 260 m. Path-loss exponent is 2.4.…”

Section: Resultsmentioning

confidence: 99%

Computation offloading to edge cloud and dynamically resource-sharing collaborators in Internet of Things

Zhang

2020

J Wireless Com Network

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With the diversity of the communication technology and the heterogeneity of the computation resources at network edge, both the edge cloud and peer devices (collaborators) can be scavenged to provide computation resources for the resource-limited Internet-of-Things (IoT) devices. In this paper, a novel cooperative computing paradigm is proposed, in which the computation resources of IoT device, opportunistically idle collaborators and dedicated edge cloud are fully exploited. Computation/offloading assistance is provided by collaborators at idle/busy states, respectively. Considering the channel randomness and opportunistic computation resource share of collaborators, we study the stochastic offloading control for an IoT device, regarding how much computation load is processed locally, offloaded to the edge cloud and a collaborator. The problem is formulated into a finite horizon Markov decision problem with the objective of minimizing the expected total energy consumption of the IoT device and the collaborator, subject to satisfying the hard computation deadline constraint. Optimal offloading policy is derived based on the stochastic optimization theory, which demonstrates that the energy consumption can be reduced by a proportional factor through the cooperative computing. More energy saving is achieved with better wireless channel condition or higher computation energy efficiency of collaborators. Simulation results validate the optimality of the proposed policy and the efficiency of the cooperative computing between end devices and edge cloud, compared to several other offloading schemes.

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Section: Resultsmentioning

confidence: 99%

Computation offloading to edge cloud and dynamically resource-sharing collaborators in Internet of Things

Zhang

2020

J Wireless Com Network

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“…Several works in the literature proposed computation offloading algorithms in edge–cloud vehicular networks [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ]. However, very few works have focused on the optimization of energy consumption while considering the SLA requirements [ 10 , 11 , 12 , 13 ].…”

Section: Related Workmentioning

confidence: 99%

“…Huang et al [ 12 ] proposed an offloading scheme to process requests either locally on the vehicle or to schedule them to an edge server. The offloading decision considers the energy consumption of the vehicles in terms of computation and request transmission, and the packet drop rate.…”

Section: Related Workmentioning

confidence: 99%

“…However, these works [ 10 , 11 , 12 , 13 ] focus on two-tiered architectures, which consist of vehicles as one tier and the MEC-enabled RSUs as the second tier. Moreover, the offloading algorithm proposed in [ 10 ] focuses only on the energy optimization of the edge servers, whereas the ones proposed in [ 11 , 12 , 13 ] focus on the energy optimization of the vehicles alone. Furthermore, no work considers more than one SLA requirement.…”

Section: Related Workmentioning

confidence: 99%

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ESCOVE: Energy-SLA-Aware Edge–Cloud Computation Offloading in Vehicular Networks

Ismail

Materwala

2021

Sensors

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The vehicular network is an emerging technology in the Intelligent Smart Transportation era. The network provides mechanisms for running different applications, such as accident prevention, publishing and consuming services, and traffic flow management. In such scenarios, edge and cloud computing come into the picture to offload computation from vehicles that have limited processing capabilities. Optimizing the energy consumption of the edge and cloud servers becomes crucial. However, existing research efforts focus on either vehicle or edge energy optimization, and do not account for vehicular applications’ quality of services. In this paper, we address this void by proposing a novel offloading algorithm, ESCOVE, which optimizes the energy of the edge–cloud computing platform. The proposed algorithm respects the Service level agreement (SLA) in terms of latency, processing and total execution times. The experimental results show that ESCOVE is a promising approach in energy savings while preserving SLAs compared to the state-of-the-art approach.

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“…Task or computation offloading in the MEC environment has already been extensively studied. Traditional offloading schemes are model-based, i.e., usually assume that the mobile signal between MD and eNB is well modeled [7,[9][10][11]. However, the MEC environment is very complicated, and the users' mobilities are highly dynamic, making the mobile models hard to construct and predict.…”

mentioning

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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Energy-efficient offloading decision-making for mobile edge computing in vehicular networks

Cited by 48 publications

References 32 publications

Computation offloading to edge cloud and dynamically resource-sharing collaborators in Internet of Things

Computation offloading to edge cloud and dynamically resource-sharing collaborators in Internet of Things

ESCOVE: Energy-SLA-Aware Edge–Cloud Computation Offloading in Vehicular Networks

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

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