HetMEC: Latency-Optimal Task Assignment and Resource Allocation for Heterogeneous Multi-Layer Mobile Edge Computing

Wang, Pengfei; Zheng, Zijie; Di, Boya; Song, Lingyang

doi:10.1109/twc.2019.2931315

Cited by 97 publications

(32 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The noise spectral density is σ 2 = −169 dBm/Hz. The transmit powers at the U-BS and the cloud server are p u = p c = 0.5 W. We consider the input data size I i follows a uniform distribution with I i ∼ U [10,15] KB, the ratio between O i and I i is set as α = 2, and the required number of CPU cycles per bit is distributed as F i ∼ U [500, 800] cycles/bit. The computing capacity of VR users follows a distribution of f local i ∼ U [0.5, 1] GHz.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…where r i is the transmission rate between the U-BS and the i-th VR user which is shown in (10). The first term in the righthand-side (RHS) of (2) shows that if the data is computed at the U-BS, the output data O i after being processed will be transmitted from U-BS to the VR user and the second term means that if the data is computed locally, the U-BS transmits the input data I i to the VR user for calculation.…”

Section: A Computing Modelmentioning

confidence: 99%

“…In [9], the authors implemented and developed a low-latency management framework for distributed service function chains enabling tactile internet with MEC. In [10], by jointly coordinating the task assignment, computing, and transmission resources among edge devices, multi-layer MEC servers and cloud center, the authors proposed an efficient algorithm that aimed at minimizing the system latency including total computing and transmission time in heterogeneous multi-layer MEC networks. In [11], by jointly optimizing the users' transmit power, computing capacity allocation, and user association, a latency minimization problem of an MEC system was formulated.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Communication-and-Computing Latency Minimization for UAV-Enabled Virtual Reality Delivery Systems

Zhou

Pan

Yeoh

et al. 2021

IEEE Trans. Commun.

View full text Add to dashboard Cite

In this paper, we propose a low-latency virtual reality (VR) delivery system where an unmanned aerial vehicle (UAV) base station (U-BS) is deployed to deliver VR content from a cloud server to multiple ground VR users. Each VR input data requested by the VR users can be either projected at the U-BS before transmission or processed locally at each user. Popular VR input data is cached at the U-BS to further reduce backhaul latency from the cloud server. For this system, we design a low-complexity iterative algorithm to minimize the maximum communications and computing latency among all VR users subject to the computing, caching and transmit power constraints, which is guaranteed to converge. Numerical results indicate that our proposed algorithm can achieve a lower latency compared to other benchmark schemes. Moreover, we observe that the maximum latency mainly comes from communication latency when the bandwidth resource is limited, while it is dominated by computing latency when computing capacity is low. In addition, we find that caching is helpful to reduce latency.

show abstract

Section: Simulation Resultsmentioning

confidence: 99%

Section: A Computing Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Communication-and-Computing Latency Minimization for UAV-Enabled Virtual Reality Delivery Systems

Zhou

Pan

Yeoh

et al. 2021

IEEE Trans. Commun.

View full text Add to dashboard Cite

show abstract

“…The IoT-edge-cloud network can make full use of IoT, MEC computing and cloud computing [21][22][23][24][25][26]. Wang et al [27] proposed a task offloading and resource allocation algorithm in a heterogeneous network. The network divided different MEC servers into multiple layers, the objective was to minimize the overall system latency.…”

Section: Background and Related Workmentioning

confidence: 99%

Computation offloading through mobile vehicles in IoT-edge-cloud network

Long

Luo

Zhu

et al. 2020

J Wireless Com Network

View full text Add to dashboard Cite

With the developing of Internet of Things (IoT) and mobile edge computing (MEC), more and more sensing devices are widely deployed in the smart city. These sensing devices generate various kinds of tasks, which need to be sent to cloud to process. Usually, the sensing devices do not equip with wireless modules, because it is neither economical nor energy saving. Thus, it is a challenging problem to find a way to offload tasks for sensing devices. However, many vehicles are moving around the city, which can communicate with sensing devices in an effective and low-cost way. In this paper, we propose a computation offloading scheme through mobile vehicles in IoT-edge-cloud network. The sensing devices generate tasks and transmit the tasks to vehicles, then the vehicles decide to compute the tasks in the local vehicle, MEC server or cloud center. The computation offloading decision is made based on the utility function of the energy consumption and transmission delay, and the deep reinforcement learning technique is adopted to make decisions. Our proposed method can make full use of the existing infrastructures to implement the task offloading of sensing devices, the experimental results show that our proposed solution can achieve the maximum reward and decrease delay.

show abstract

“…The IoT-edge-cloud network can make full use of WSNs, MEC computing and cloud computing [20][21][22][23][24]. Wang et al [25] proposed a task offloading and resource allocation algorithm in a heterogeneous network. The network divided different MEC servers into multiple layers, the objective were minimize the overall system latency.…”

Section: Background and Related Workmentioning

confidence: 99%

Computation offloading through mobile vehicles in IoT-edge-cloud network

Long

Luo

Zhu

et al. 2020

Preprint

View full text Add to dashboard Cite

With the developing of Wireless Sensor Networks (WSNs), more and more sensing devices are widely deployed in the smart city. These sensing devices generate various kinds of tasks, which need to be sent to cloud to process. Usually, the sensing devices do not equip with wireless modules, because it is neither economical nor energy saving. Thus, it is a challenging problem to find a way to offload tasks for sensing devices. However, many vehicles are moving around the city, which can communicate with sensing devices in an effective and low-cost way. In this paper, we propose a computation offloading scheme through mobile vehicles in IoT-edge-cloud network. The sensing devices generate tasks and transmit the tasks to vehicles, then the vehicles decide to compute the tasks in the local vehicle, MEC server or cloud center. The computation offloading decision is made based on the utility function of the energy consumption and transmission delay, and the deep reinforcement learning technique is adopted to make decisions. Our proposed method can make full use of the existing infrastructures to implement the task offloading of sensing devices, the experimental results show that our proposed solution can achieve the maximum reward and decrease delay.

show abstract

HetMEC: Latency-Optimal Task Assignment and Resource Allocation for Heterogeneous Multi-Layer Mobile Edge Computing

Cited by 97 publications

References 33 publications

Communication-and-Computing Latency Minimization for UAV-Enabled Virtual Reality Delivery Systems

Communication-and-Computing Latency Minimization for UAV-Enabled Virtual Reality Delivery Systems

Computation offloading through mobile vehicles in IoT-edge-cloud network

Computation offloading through mobile vehicles in IoT-edge-cloud network

Contact Info

Product

Resources

About