Collaborative Cloud and Edge Computing for Latency Minimization

Ren, Jinke; Yu, Guanding; He, Yinghui; Li, Geoffrey Ye

doi:10.1109/tvt.2019.2904244

Cited by 504 publications

(227 citation statements)

References 41 publications

Supporting

Mentioning

205

Contrasting

Order By: Relevance

“…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%

“…Then, they propose a coordinate descent based algorithm in which the offloading decision and time-sharing variables are iteratively updated until convergence. Considering the partial computation offloading problem, the authors in [23] propose a framework to minimize weighted-sum latency of all mobile users via the collaboration between cloud computing and fog computing assuming the TDMA based resource sharing strategy [23].…”

Section: A Related Workmentioning

confidence: 99%

“…Note that massive MIMO communications with interference cancellation using MIMO beamforming is assumed in this paper cuch that multiple mobile users can transmit their data to the fog server at the same time over the same frequency band. Moreover, unlike [23], we do not consider the TDMA transmission strategy where the users are scheduled and have to wait for their turns to transmit their data in the uplink. In other work, no time-based scheduling is required for the multi-user MIMO communication system considered in our paper.…”

Section: B Problem Formulationmentioning

confidence: 99%

See 2 more Smart Citations

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

Nguyen

et al. 2020

IEEE Trans. Wireless Commun.

View full text Add to dashboard Cite

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.

show abstract

Section: A Related Workmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

Section: B Problem Formulationmentioning

confidence: 99%

See 1 more Smart Citation

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

Nguyen

et al. 2020

IEEE Trans. Wireless Commun.

View full text Add to dashboard Cite

show abstract

“…Hou et al [22] proposed a model for horizontal offloading between autonomous vehicles and road infrastructure, and optimized for latency using a heuristic algorithm. Ren et al [8] assumed a vertical offloading model for edge and cloud, and optimized for latency using convex optimization. Ahn et al [23] proposed a cooperation model between edge and cloud for video analytics based on Internet of Things (IoT).…”

Section: Previous Workmentioning

confidence: 99%

“…Figure 2 shows two of these situations and illustrates the different responses of the CCC and MEC architectures. To address the challenges in reducing core network latency, a new network architecture called Multi-access Edge Computing (MEC) architecture has been proposed [6][7][8]. The main concept of MEC is to reduce the physical distance between user entity (UE) and application server.…”

mentioning

confidence: 99%

Comparative Analysis of 5G Mobile Communication Network Architectures

et al. 2020

View full text Add to dashboard Cite

Mobile communication technology is evolving from 4G to 5G. Compared to previous generations, 5G has the capability to implement latency-critical services, such as autonomous driving, real-time AI on handheld devices and remote drone control. Multi-access Edge Computing is one of the key technologies of 5G in guaranteeing ultra-low latency aimed to support latency critical services by distributing centralized computing resources to networks edges closer to users. However, due to its high granularity of computing resources, Multi-access Edge Computing has an architectural vulnerability in that it can lead to the overloading of regional computing resources, a phenomenon called regional traffic explosion. This paper proposes an improved communication architecture called Hybrid Cloud Computing, which combines the advantages of both Centralized Cloud Computing and Multi-access Edge Computing. The performance of the proposed network architecture is evaluated by utilizing a discrete-event simulation model. Finally, the results, advantages, and disadvantages of various network architectures are discussed.

show abstract