Energy-efficient user selection and resource allocation in mobile edge computing

Feng, Hao; Guo, Songtao; Zhu, Anqi; Wang, Quyuan; Liu, Defang

doi:10.1016/j.adhoc.2020.102202

Cited by 18 publications

(9 citation statements)

References 30 publications

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“…But, MTS assumes that there is only one VM, and we extend it to multiple VMs. We also compare EE-PRO [38] with our methods. We suppose that the processing ability of a VM is [0.8, 1.2] standard machines (standard machine: with 8 Cores, 8G memory, 1T hard disk).…”

Section: Comparison Between the Proposed Methods And Other Methodsmentioning

confidence: 99%

Energy-Aware Scheduling in Edge Computing Based on Energy Internet

et al. 2020

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Section: Comparison Between the Proposed Methods And Other Methodsmentioning

confidence: 99%

Energy-Aware Scheduling in Edge Computing Based on Energy Internet

et al. 2020

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“…They did not consider data processing capability for IoT devices, and only concentrated on the short packet transmission between the IoT devices and edge servers. In [15], researchers aim to select the best edge/cloud in a dense cell to execute tasks. They proposed a joint user selection and resource allocation algorithm to maximize the user's energy efficiency, while their work was not encompassed the computation capacity of nodes.…”

Section: Related Workmentioning

confidence: 99%

Optimal Placement of Recurrent Service Chains on Distributed Edge-Cloud Infrastructures

Mahjoubi

Taheri

Grinnemo

2021

2021 IEEE 46th Conference on Local Computer Networks (LCN)

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By increasing the number of IoT-devices, cloudcomputing faces challenges for some computation and timesensitive applications. Edge-computing has emerged to enable IoT-devices offload their computation tasks. Offloading tasks is a complex and challenging issue. We propose a comprehensive model including user, edge and cloud layers for scheduling continuous offering of services. Furthermore, we modeled the tasks of service as recurrent (repetitive) with a given frequency. The serviceplacement problem is formulated as a Mixed-Integer Linear Programming problem that aims to minimize the total delay of all services. We solve the problem with CPLEX, and proposed four fast heuristics to find near-optimal solutions. We compared the results of our proposed heuristics with the result obtained with CPLEX, in terms of problem-solving speed and accuracy, as well as resource utilization of all nodes. The results show that two of our proposed heuristics produce near-optimal solutions in a fraction of the time taken by CPLEX.Index Terms-edge computing, service placement, IoT devices, cloud computing• We propose a comprehensive model that includes all layers (user, edge, and cloud). It models three main node parameters (CPU, RAM, and network capacities), as well as the link

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“…A Lyapunov optimization approach based on the task offloading scheme has been proposed in [21], [22] to reduce the delay in the implementation of the proposed system for energy consumption. Multi-user multi-MEC server task offloading design [23] can reduce the pressure of single link communication in order to improve based network performance and reduce the amount of communication overhead. The proposed model was designed to maximize resource allocation energy.…”

Section: Related Workmentioning

confidence: 99%

A User-Centric QoS-Aware Multi-Path Service Provisioning in Mobile Edge Computing

et al. 2021

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Recent development in modern wireless applications and services, such as augmented reality, image processing, and network gaming requires persistent computing on average commercial wireless devices to perform complex tasks with low latency. The traditional cloud systems are unable to meet those requirements solely. In the said perspective, Mobile Edge Computing (MEC) serves as a proxy between the things (devices) and the cloud, pushing the computations at the edge of the network. The MEC provides an effective solution to fulfill the demands of low-latency applications and services by executing most of the tasks within the proximity of users. The main challenge, however, is that too many simultaneous service requests created by wireless access produce severe interference, resulting in a decreased rate of data transmission. In this paper, we made an attempt to overcome the aforesaid limitation by proposing a usercentric QoS-aware multi-path service provisioning approach. A densely deployed base station MEC environment has overlapping coverage regions. We exploit such regions to distribute the service requests in a way that avoid hotspots and bottlenecks. Our approach is adaptive and can tune to different parameters based on service requirements. We performed several experiments to evaluate the effectiveness of our approach and compared it with the traditional Greedy approach. The results revealed that our approach improves the network state by 26.95% and average waiting time by 35.56% as compared to the Greedy approach. In addition, the QoS violations were also reduced by the fraction of 16.

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Energy-efficient user selection and resource allocation in mobile edge computing

Cited by 18 publications

References 30 publications

Energy-Aware Scheduling in Edge Computing Based on Energy Internet

Energy-Aware Scheduling in Edge Computing Based on Energy Internet

Optimal Placement of Recurrent Service Chains on Distributed Edge-Cloud Infrastructures

A User-Centric QoS-Aware Multi-Path Service Provisioning in Mobile Edge Computing

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