Energy-Efficient Joint Offloading and Wireless Resource Allocation Strategy in Multi-MEC Server Systems

Cheng, Kun; Teng, Yinglei; Sun, Weiqi; Liu, An; Wang, Xianbin

doi:10.1109/icc.2018.8422877

Cited by 127 publications

(81 citation statements)

References 16 publications

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“…How to improve EE of mobile devices in MEC systems subject to the delay constraint has been widely studied in existing literature [7,8,[12][13][14]. To study the tradeoff between EE and latency, a weighted sum of energy consumption and latency was minimized in a single-AP scenario [7].…”

Section: Related Workmentioning

confidence: 99%

“…On the other hand, mobile devices have only limited battery capacities. Improving the battery lifetime or energy efficiency (EE) of users is an urgent task [3,6,7]. By offloading tasks to MEC servers, we can save energy consumptions at the local servers (equipped at mobile devices), but extra energy is consumed for data transmissions.…”

mentioning

confidence: 99%

“…19a), (19e), (19f), (1), (2),(7),(8),(11),(12),(14) and(15),If η ξ k ≤ η th , then N ξ * k ≤ N th,ξ k . Otherwise, N ξ * k ≥ N th,ξ k (See proof in Subsection V-C).…”

mentioning

confidence: 99%

“…. By substituting D lc,u From the CCDF of the queueing delay in(6), the minimal service rate can be obtained when ǫ lq,u k = ǫ max,u . We denote the minimal service rate that is required to satisfy D lq,u k and ǫ lq,u k as C u * k .…”

mentioning

confidence: 99%

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Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn From a Digital Twin

Dong

She

Hardjawana

et al. 2019

IEEE Trans. Wireless Commun.

244

126

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In this work, we consider a mobile edge computing system with both ultra-reliable and lowlatency communications services and delay tolerant services. We aim to minimize the normalized energy consumption, defined as the energy consumption per bit, by optimizing user association, resource allocation, and offloading probabilities subject to the quality-of-service requirements. The user association is managed by the mobility management entity (MME), while resource allocation and offloading probabilities are determined by each access point (AP). We propose a deep learning (DL) architecture, where a digital twin of the real network environment is used to train the DL algorithm off-line at a central server. From the pre-trained deep neural network (DNN), the MME can obtain user association scheme in a real-time manner. Considering that real networks are not static, the digital twin monitors the variation of real networks and updates the DNN accordingly. For a given user association scheme, we propose an optimization algorithm to find the optimal resource allocation and offloading probabilities at each AP. Simulation results show that our method can achieve lower normalized energy consumption with less computation complexity compared with an existing method and approach to the performance of the global optimal solution.

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Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

“…19a), (19e), (19f), (1), (2),(7),(8),(11),(12),(14) and(15),If η ξ k ≤ η th , then N ξ * k ≤ N th,ξ k . Otherwise, N ξ * k ≥ N th,ξ k (See proof in Subsection V-C).…”

mentioning

confidence: 99%

mentioning

confidence: 99%

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Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn From a Digital Twin

Dong

She

Hardjawana

et al. 2019

IEEE Trans. Wireless Commun.

244

126

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“…The work in [30] studies the problem of task offloading from a single device to multiple edge servers to minimize the total execution latency and energy consumption by jointly optimizing task allocation and computational frequency scaling. In [31], the authors study task offloading and wireless resource allocation in an environment with multiple MEC servers. [32] formulates an optimization model to maximize the profit of a mobile service provider by jointly scheduling network resources in C-RAN and computation resources in MEC.…”

Section: Other Perspectivesmentioning

confidence: 99%

Joint Network Slicing and Mobile Edge Computing in 5G Networks

Xiang

Elias

Martignon

et al. 2019

ICC 2019 - 2019 IEEE International Conference on Communications (ICC)

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Multi-access Edge Computing (MEC) facilitates the deployment of critical applications with stringent QoS requirements, latency in particular. Our paper considers the problem of jointly planning the availability of computational resources at the edge, the slicing of mobile network and edge computation resources, and the routing of heterogeneous traffic types to the various slices. These aspects are intertwined and must be addressed together to provide the desired QoS to all mobile users and traffic types still keeping costs under control. We formulate our problem as a mixed-integer nonlinear program (MINLP) and we define a heuristic, named Neighbor Exploration and Sequential Fixing (NESF), to facilitate the solution of the problem. The approach allows network operators to fine tune the network operation cost and the total latency experienced by users. We evaluate the performance of the proposed model and heuristic against two natural greedy approaches. We show the impact of the variation of all the considered parameters (viz., different types of traffic, tolerable latency, network topology and bandwidth, computation and link capacity) on the defined model. Numerical results demonstrate that NESF is very effective, achieving near-optimal planning and resource allocation solutions in a very short computing time even for large-scale network scenarios.

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Issues and Challenges Facing Low Latency in the Tactile Internet

ahiya

2021

The Tactile Internet

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Energy-Efficient Joint Offloading and Wireless Resource Allocation Strategy in Multi-MEC Server Systems

Cited by 127 publications

References 16 publications

Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn From a Digital Twin

Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn From a Digital Twin

Joint Network Slicing and Mobile Edge Computing in 5G Networks

Issues and Challenges Facing Low Latency in the Tactile Internet

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