Online Scheduling of Transmission and Processing for AoI Minimization with Edge Computing

Zhu, Jianqing; Gong, Jianya

doi:10.1109/infocomwkshps54753.2022.9798088

Cited by 9 publications

(3 citation statements)

References 14 publications

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“…If mobile device m offloads task k to edge node n ∈ N , then let τ tran n,m,k (in seconds) denote the service time of mobile device m ∈ M for sending task k to edge node n. The value of τ tran n,m,k depends on the time-varying wireless channels and is unknown a priori. We assume that τ local m,k and τ tran n,m,k are random variables that follow exponential distribution (Tang et al 2021;Liu et al 2022b;Zhu and Gong 2022), respectively, which are independent of edge loads.…”

Section: Device Modelmentioning

confidence: 99%

Fractional Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing

Jin,

Tang,

Zhang

et al. 2024

AAAI

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Mobile edge computing (MEC) is a promising paradigm for real-time applications with intensive computational needs (e.g., autonomous driving), as it can reduce the processing delay. In this work, we focus on the timeliness of computational-intensive updates, measured by Age-of-Information (AoI), and study how to jointly optimize the task updating and offloading policies for AoI with fractional form. Specifically, we consider edge load dynamics and formulate a task scheduling problem to minimize the expected time-average AoI. The uncertain edge load dynamics, the nature of the fractional objective, and hybrid continuous-discrete action space (due to the joint optimization) make this problem challenging and existing approaches not directly applicable. To this end, we propose a fractional reinforcement learning (RL) framework and prove its convergence. We further design a model-free fractional deep RL (DRL) algorithm, where each device makes scheduling decisions with the hybrid action space without knowing the system dynamics and decisions of other devices. Experimental results show that our proposed algorithms reduce the average AoI by up to 57.6% compared with several non-fractional benchmarks.

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Section: Device Modelmentioning

confidence: 99%

Fractional Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing

Jin,

Tang,

Zhang

et al. 2024

AAAI

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“…Considering the worst-case PAoI, in terms of either the violation probability for a specific requirement or a high percentile of the distribution, also increases the complexity of the calculation, but the closed-form PAoI distribution has been derived for some simple systems [19]. The scheduling of updates in this context can lead to better performance [28], but requires the sensor to be able to receive and process feedback, with a higher energy consumption, which may be offset by harvesting energy from the environment [29].…”

Section: Related Workmentioning

confidence: 99%

“…The rest of the derivation of the PAoI CDF is the same: the transition matrix in case of a failure P D is computed as in (28). The transition probability over m steps, where the first frame is the only successful one, is given by ( 29), and finally, the PAoI CDF can be obtained by using the FIFO versions of the terms in (30).…”

Section: B Fifo Queuingmentioning

confidence: 99%

Peak Age of Information Distribution for Edge Computing With Wireless Links

Chiariotti

Vikhrova

Soret

et al. 2021

IEEE Trans. Commun.

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Age of Information (AoI) is a critical metric for several Internet of Things (IoT) applications, where sensors keep track of the environment by sending updates that need to be as fresh as possible. The development of edge computing solutions has moved the monitoring process closer to the sensor, reducing the communication delays, but the processing time of the edge node needs to be taken into account. Furthermore, a reliable system design in terms of freshness requires the knowledge of the full distribution of the Peak AoI (PAoI), from which the probability of occurrence of rare, but extremely damaging events can be obtained. In this work, we model the communication and computation delay of such a system as two First Come First Serve (FCFS) queues in tandem, analytically deriving the full distribution of the PAoI for the M/M/1 -M/D/1 and the M/M/1 -M/M/1 tandems, which can represent a wide variety of realistic scenarios.

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Optimizing Peak Age of Information in Mobile Edge Computing

Zhu,

Gong

2023

2023 35th International Teletraffic Congress (ITC-35)

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Online Scheduling of Transmission and Processing for AoI Minimization with Edge Computing

Cited by 9 publications

References 14 publications

Fractional Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing

Fractional Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing

Peak Age of Information Distribution for Edge Computing With Wireless Links

Optimizing Peak Age of Information in Mobile Edge Computing

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