Virtual Resource Allocation for Heterogeneous Services in Full Duplex-Enabled SCNs With Mobile Edge Computing and Caching

Tan, Zhiyuan; Yu, F. Richard; Li, Xi; Ji, Hong; Leung, Victor C. M.

doi:10.1109/tvt.2017.2764002

Cited by 90 publications

(62 citation statements)

References 33 publications

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“…Noticing that content offloading can largely save the transmission resource and decrease the service delay, some works try to integrate caching with the computation offloading and resource allocation to further the performance in the MEC systems. Tan et al [13] studied the virtual resource allocation for heterogeneous services which include data service and computation service.…”

Section: Joint Caching Computation Offloading and Resource Allocationmentioning

confidence: 99%

“…Liu et al [34] furthered the work in [13] and studied the joint offloading and resource allocation problem in blockchain-based video streaming systems. In their work, D2D technology was adopted to enhance the sharing of computing power to end users.…”

Section: Joint Caching Computation Offloading and Resource Allocationmentioning

confidence: 99%

“…In their work, D2D technology was adopted to enhance the sharing of computing power to end users. But both the work in [13,39] ignored the competition among UEs. Hao et al in [29] presented the task caching and resource allocation scheme for mobile edge computing to improve the energy efficiency of the system.…”

Section: Joint Caching Computation Offloading and Resource Allocationmentioning

confidence: 99%

“…As the UEs' tasks are heterogeneous, the competition of resources for other type of tasks (e.g., tasks with a large data size) is still universal in the RAN, thus how to further relieve the competition is an urgent problem to be solved [11].Device-to-device (D2D) communication and caching are proposed as technological components to tackle the data-rate problem. D2D enables devices to communicate directly and obtain the proximity gain, reuse gain, and hop gain [12][13][14]. Moreover, since requests of UEs are not simultaneous in the time domain, so in each period of task requesting, a large number of idle UEs would exist.…”

mentioning

confidence: 99%

“…Device-to-device (D2D) communication and caching are proposed as technological components to tackle the data-rate problem. D2D enables devices to communicate directly and obtain the proximity gain, reuse gain, and hop gain [12][13][14]. Moreover, since requests of UEs are not simultaneous in the time domain, so in each period of task requesting, a large number of idle UEs would exist.…”

mentioning

confidence: 99%

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Collaborative Computation Offloading and Resource Allocation in Cache-Aided Hierarchical Edge-Cloud Systems

Lan

Wang

et al. 2019

Electronics

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The hierarchical edge-cloud enabled paradigm has recently been proposed to provide abundant resources for 5G wireless networks. However, the computation and communication capabilities are heterogeneous which makes the potential advantages difficult to be fully explored. Besides, previous works on mobile edge computing (MEC) focused on server caching and offloading, ignoring the computational and caching gains brought by the proximity of user equipments (UEs). In this paper, we investigate the computation offloading in a three-tier cache-assisted hierarchical edge-cloud system. In this system, UEs cache tasks and can offload their workloads to edge servers or adjoining UEs by device-to-device (D2D) for collaborative processing. A cost minimization problem is proposed by the tradeoff between service delay and energy consumption. In this problem, the offloading decision, the computational resources and the offloading ratio are jointly optimized in each offloading mode. Then, we formulate this problem as a mixed-integer nonlinear optimization problem (MINLP) which is non-convex. To solve it, we propose a joint computation offloading and resource allocation optimization (JORA) scheme. Primarily, in this scheme, we decompose the original problem into three independent subproblems and analyze their convexity. After that, we transform them into solvable forms (e.g., convex optimization problem or linear optimization problem). Then, an iteration-based algorithm with the Lagrange multiplier method and a distributed joint optimization algorithm with the adoption of game theory are proposed to solve these problems. Finally, the simulation results show the performance of our proposed scheme compared with other existing benchmark schemes. and transmission techniques applied in the conventional cellular networks may not be efficient to meet UEs' requirements of high throughput and adequate computational power. To improve the delay performance and operational costs of services in fifth-generation (5G) wireless networks, future communication networks not only need to support seamless wireless access but also to offer the provisioning of computational offloading for UEs [3,4].To meet with such a challenge of limited computational capability of UEs, a typical paradigm of mobile edge computing (MEC) is proposed which combines wireless network service and cloud computing at the edge of the small cell networks (SCNs) [5]. In an MEC system, a huge number of heterogeneous ubiquitous and decentralized devices are enabled to communicate, potentially cooperate and perform computation offloading by uploading their computational tasks to the MEC server via access ratio networks [6,7]. UEs no longer need to offload all of their tasks (e.g., high-quality video streaming, mobile gaming, etc.) to the central and remote cloud. Thus their requirement can be satisfied at any time and anywhere. However, the limited computational capabilities of edge servers may not be sufficient when there exists competition for resources by a large number of device...

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Section: Joint Caching Computation Offloading and Resource Allocationmentioning

confidence: 99%

Section: Joint Caching Computation Offloading and Resource Allocationmentioning

confidence: 99%

Section: Joint Caching Computation Offloading and Resource Allocationmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Collaborative Computation Offloading and Resource Allocation in Cache-Aided Hierarchical Edge-Cloud Systems

Lan

Wang

et al. 2019

Electronics

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Joint Caching, Computing, and Routing for Video Transcoding in Wireless Networks

Liu

et al. 2018

Encyclopedia of Wireless Networks

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Joint Caching, Computing, and Routing for Video Transcoding in Wireless Networks

Liu¹,

Yu²,

Li³

et al. 2020

Encyclopedia of Wireless Networks

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Virtual Resource Allocation for Heterogeneous Services in Full Duplex-Enabled SCNs With Mobile Edge Computing and Caching

Cited by 90 publications

References 33 publications

Collaborative Computation Offloading and Resource Allocation in Cache-Aided Hierarchical Edge-Cloud Systems

Collaborative Computation Offloading and Resource Allocation in Cache-Aided Hierarchical Edge-Cloud Systems

Joint Caching, Computing, and Routing for Video Transcoding in Wireless Networks

Joint Caching, Computing, and Routing for Video Transcoding in Wireless Networks

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