A Generic Framework for Task Offloading in mmWave MEC Backhaul Networks

Noghani, Kyoomars Alizadeh; Ghazzai, Hakim; Kassler, Andreas

doi:10.1109/glocom.2018.8647559

Cited by 7 publications

(6 citation statements)

References 15 publications

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“…Whereas, the constraint ( 24) is not convex with respect to the specific domain of variables. Hence, we need to further manipulate (24) by applying the approach of the first-order Taylor expansion, in order to guarantee the strict convexity.…”

Section: A Design Of Jbtpmentioning

confidence: 99%

“…In [23], they jointly considered mmW MEC offloading under the coexistence of communication-oriented users and computing-oriented users. In addition, the study in [24] paid attention to the downlink of MEC offloading transmission, and discussed the task offloading of mmW MEC by optimizing backhaul bandwidth and edge server resource allocation to reduce the overall delay. Nevertheless, the above works rarely discussed the MEC offloading for multiuser scenarios, which may challenge the accessing efficiency of mmW links with limited beam coverage.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Delay Minimization for NOMA-mmW Scheme-Based MEC Offloading

Shi

Zhou

et al. 2023

IEEE Internet Things J.

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Section: A Design Of Jbtpmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Delay Minimization for NOMA-mmW Scheme-Based MEC Offloading

Shi

Zhou

et al. 2023

IEEE Internet Things J.

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“…The waiting time of a task before the computation begins is determined by two factors, the transfer time and the end time of all precursor tasks [18].…”

Section: Delay Model (A) Waiting Delaymentioning

confidence: 99%

“…Among them, constraints (15) and constraints ( 16) ensure the indivisibility of tasks. Constraint (17) means that the total time taken by the system to complete all computing tasks does not exceed the specified maximum completion time T max ; Constraint (18) means that the cost of energy consumption generated shall not exceed the maximum energy consumption E max specified by the system; Constraint (19) ensures that all computation data of the task has been transferred to the assigned execution device before the task begins to execute, and that all precursor tasks of the current task have ended execution.…”

Section: Optimization Problem Modelmentioning

confidence: 99%

Delay and Energy Consumption Optimization Oriented Multi-service Cloud Edge Collaborative Computing Mechanism in IoT

Shao

Tang

Wu³

et al. 2021

JWE

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The rapid development of the Internet of Things has put forward higher requirements for the processing capacity of the network. The adoption of cloud edge collaboration technology can make full use of computing resources and improve the processing capacity of the network. However, in the cloud edge collaboration technology, how to design a collaborative assignment strategy among different devices to minimize the system cost is still a challenging work. In this paper, a task collaborative assignment algorithm based on genetic algorithm and simulated annealing algorithm is proposed. Firstly, the task collaborative assignment framework of cloud edge collaboration is constructed. Secondly, the problem of task assignment strategy was transformed into a function optimization problem with the objective of minimizing the time delay and energy consumption cost. To solve this problem, a task assignment algorithm combining the improved genetic algorithm and simulated annealing algorithm was proposed, and the optimal task assignment strategy was obtained. Finally, the simulation results show that compared with the traditional cloud computing, the proposed method can improve the system efficiency by more than 25%.

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“…In [26], the authors proposed a jointly optimization policy in heterogeneous networks in order to minimize the cost of system respect to the energy consumption, computation and transmission cost. The authors of [37] investigated the task allocation problem in MEC environment with mmWave technology. In their works, the backhaul bandwidth and the edge server resource allocation are jointly optimized to minimize the total task serving time.…”

Section: Joint Computation Offloading and Resource Allocationmentioning

confidence: 99%

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...

show abstract

A Generic Framework for Task Offloading in mmWave MEC Backhaul Networks

Cited by 7 publications

References 15 publications

Delay Minimization for NOMA-mmW Scheme-Based MEC Offloading

Delay Minimization for NOMA-mmW Scheme-Based MEC Offloading

Delay and Energy Consumption Optimization Oriented Multi-service Cloud Edge Collaborative Computing Mechanism in IoT

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

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