Energy-Saving Offloading by Jointly Allocating Radio and Computational Resources for Mobile Edge Computing

Zhao, Pengtao; Tian, Hui; Qin, Cheng; Nie, Gaofeng

doi:10.1109/access.2017.2710056

Cited by 245 publications

(120 citation statements)

References 30 publications

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“…This implies that H a (u k ) > 0, ∀u k > 0, and we can conclude that ∂ 2 H(p k , y k )/∂p 2 k > 0 as shown in (18). As H(p k , y k ) is a convex function, Ξ k and T k are also convex.…”

Section: Appendix D Proof Of Propositionmentioning

confidence: 55%

“…Computation offloading design for MCC/MCE systems has been studied extensively in the literature, see recent surveys [14,15] and the references therein. Most existing works consider two main performance metrics for their designs, namely energy-efficiency [16][17][18][19] and delayefficiency [20][21][22][23]. Focusing on energy-efficiency, the authors of [16] develop partial offloading frameworks for multiuser MEC systems employing time division multiple access and frequencydivision multiple access.…”

Section: A Related Workmentioning

confidence: 99%

“…In [17], wireless power transfer is integrated into the computation offloading design. Moreover, different binary offloading frameworks are developed in [18,19] where various branch-and-bound and heuristic algorithms are proposed to tackle the resulting mixed integer optimization problems.…”

Section: A Related Workmentioning

confidence: 99%

“…However, references [7,9,[16][17][18][19][20][21][22]24] have not exploited DC for computation offloading.…”

Section: A Related Workmentioning

confidence: 99%

“…if (PB) is feasible then η max = η, BOOL = True, else η min = η, BOOL = False, end if 6: end while subproblems which optimize the offloading decision, and the computing and radio resource allocation separately as in [9,22] or relax the binary variables as in [18,19]. These approaches can obtain only sub-optimal solutions.…”

mentioning

confidence: 99%

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Joint Data Compression and Computation Offloading in Hierarchical Fog-Cloud Systems

Nguyen

et al. 2020

IEEE Trans. Wireless Commun.

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Data compression (DC) has the potential to significantly improve the computation offloading performance in hierarchical fog-cloud systems. However, it remains unknown how to optimally determine the compression ratio jointly with the computation offloading decisions and the resource allocation. This joint optimization problem is studied in the current paper where we aim to minimize the maximum weighted energy and service delay cost (WEDC) of all users. First, we consider a scenario where DC is performed only at the mobile users. We prove that the optimal offloading decisions have a threshold structure. Moreover, a novel three-step approach employing convexification techniques is developed to optimize the compression ratios and the resource allocation. Then, we address the more general design where DC is performed at both the mobile users and the fog server. We propose three efficient algorithms to overcome the strong coupling between the offloading decisions and the resource allocation. We show that the proposed optimal algorithm for DC at only the mobile users can reduce the WEDC by up to 65% compared to computation offloading strategies that do not leverage DC or use sub-optimal optimization approaches. Besides, the proposed algorithms for additional DC at the fog server can further reduce the WEDC.

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Section: Appendix D Proof Of Propositionmentioning

confidence: 55%

Section: A Related Workmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

“…However, references [7,9,[16][17][18][19][20][21][22]24] have not exploited DC for computation offloading.…”

Section: A Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

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Joint Data Compression and Computation Offloading in Hierarchical Fog-Cloud Systems

Nguyen

et al. 2020

IEEE Trans. Wireless Commun.

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Energy‐efficient and delay‐aware multitask offloading for mobile edge computing networks

Chanyour

Ghmary

Hmimz

et al. 2019

Trans Emerging Tel Tech

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Mobile edge computing (MEC) is a recent technology that intends to free mobile devices from computationally intensive workloads by offloading them to a nearby resource-rich edge architecture. It helps to reduce network traffic bottlenecks and offers new opportunities regarding data and processing privacy. Moreover, MEC-based applications can achieve lower latency level compared to cloud-based ones. However, in a multitask multidevice context, the decision of the part to offload becomes critical. Actually, it must consider the available communication resources, the resulting delays that have to be met during the offloading process, and particularly, both local and remote energy consumption. In this paper, we consider a multitask multidevice scenario where smart mobile devices retain a list of heavy offloadable tasks that are delay constrained. Therefore, we formulated the corresponding optimization problem, and we derive an equivalent multiple-choice knapsack problem formulation. Because of the short decision time constraint and the NP-hardness of the obtained problem, the optimal solution implementation is infeasible. Hence, we propose a solution that provides, in pseudopolynomial time, the optimal or near-optimal solutions depending on the problem's settings. In order to evaluate our solution, we carried out a set of simulation experiments to evaluate and compare the performances of the different components of this solution. Finally, the obtained results in terms of execution's time as well as energy consumption are satisfactory and very encouraging.

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Efficient data transfer in edge envisioned environment using artificial intelligence based edge node algorithm

Kumar

Batth

et al. 2020

Trans Emerging Tel Tech

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Edge computing technology has drawn the keystone of future intelligent transportation systems, especially in smart cities, because of processing data that are near to the user location present at the edge of the cloud server. Generally, in smart cities where distributed things have access to computational resources, data transfer becomes inevitable because of high latency, thereby resulting in crucial situations. Even though numerous technologies have emerged for improving the data communication among geo-distributed devices received from the cloud server but still, it lags in low learning performance. To address these challenges, an innovative artificial intelligence (AI) based edge node (E-Node) algorithm is implemented to optimize the edge to edge learning for well-organized data migration. To attain high reliability, AI-K-means neural network (KNN) and convolution neural network (CNN) is used initially for pre-processing and filtering the edge node. Further, the proposed E-Node algorithm outperforms the optimization technique effectively through the edge to edge computing The reliability performance is increased by reducing the node optimization time from 132 to 98 ms for 25 kb data and data transmission time is reduced from 93 to 45 ms for 80 kb data, thus reducing the latency in an edge envisioned environment. INTRODUCTIONArtificial intelligent (AI) frameworks help to solve problems involving the learning abilities of computers, language processing, and identifying various speech in the environment in a highly reliable and accurate manner. 1 Smart cities concept endures the cities with intelligence and other concepts involving smart vehicles, smart grid, and smart healthcare using the concept of deep learning. Internet of Vehicles (IoV) 2,3 lays the foundation for several new transportation systems . 4 IoV helps to improve the efficiency of transportation, reducing the accident rate and reducing the energy consumption in vehicles being reduced. 5,6 The data generated from these smart vehicles are stored in the cloud. However, it is difficult to handle several request messages from the client and process it, during a particular grave learning task. The proposed system uses a new model to foil centralized cloud computing, which reduces the latency during communication and improves the quality through the provision of various resources 7 that are near to terminal devices for latency-sensitive tasks. The use of edge to edge cooperative AI increases the learning efficiency, quality of service. It also improves variousAbbreviations: CNN, convolution neural network; E-Node, edge node; KNN, K-means neural network.

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Energy-Saving Offloading by Jointly Allocating Radio and Computational Resources for Mobile Edge Computing

Cited by 245 publications

References 30 publications

Joint Data Compression and Computation Offloading in Hierarchical Fog-Cloud Systems

Joint Data Compression and Computation Offloading in Hierarchical Fog-Cloud Systems

Energy‐efficient and delay‐aware multitask offloading for mobile edge computing networks

Efficient data transfer in edge envisioned environment using artificial intelligence based edge node algorithm

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