Timely Probabilistic Data Preprocessing in Mobile Edge Computing

Zou, Peng; Wei, Xianglin; Özel, Ömur; Lan, Tian; Subramaniam, Suresh

doi:10.1109/wcnc49053.2021.9417295

Cited by 5 publications

(4 citation statements)

References 22 publications

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“…In this section, we consider stationary deterministic policies for problem (10), where W k is determined based on T k−1 , D k−1 and C k−1 . We define three functions f, g, h to calculate W k depending on whether…”

Section: Stationary Deterministic Policiesmentioning

confidence: 99%

“…8. The lower bound in the figure is obtained by solving the offline version of the problem (10) under constant (14) using the convex optimization method. When E[T ]/E[C] > 1, the PAoI-TP policy is better than the long wait policy.…”

Section: E[t ] + E[c] Remains Unchanged At 1 For All Experiments and ...mentioning

confidence: 99%

“…Ref. [10] considers the collaboration of an edge server and a cloud center. To reduce AoI in the cloud center, the computing offloading policy in the edge server is investigated.…”

Section: Introductionmentioning

confidence: 99%

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

Zhu¹,

Gong²

2022

Preprint

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Age of Information (AoI), which measures the time elapsed since the generation of the last received packet at the destination, is a new metric for real-time status update tracking applications. In this paper, we consider a status-update system in which a source node samples updates and sends them to an edge server over a delay channel. The received updates are processed by the server with an infinite buffer and then delivered to a destination. The channel can send only one update at a time, and the server can process one at a time as well. The source node applies generate-at-will model according to the state of the channel, the edge server, and the buffer. We aim to minimize the average AoI with independent and identically distributed transmission time and processing time. We consider three online scheduling policies. The first one is the optimal long wait policy, under which the source node only transmits a new packet after the old one is delivered. Secondly, we propose a peak age threshold policy, under which the source node determines the sending time based on the estimated peak age of information (PAoI). Finally, we improve the peak age threshold policy by considering a postponed plan to reduce the waiting time in the buffer. The AoI performance under these policies is illustrated by numerical results with different parameters.

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Section: Stationary Deterministic Policiesmentioning

confidence: 99%

Section: E[t ] + E[c] Remains Unchanged At 1 For All Experiments and ...mentioning

confidence: 99%

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

Zhu¹,

Gong²

2022

Preprint

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“…Existing works also consider the effect of different offloading policies between the Edge and Cloud on the AoI [30]: the massive processing capabilities of the Cloud reduce the load, but are partially offset by the higher transmission delay [31]. The choice between offloaded and local processing is also difficult [32], as the features of the Edge server [8] and the traffic load on it [33] may significantly affect the resulting AoI.…”

Section: Related Workmentioning

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

Zhu

Gong

2022

IEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

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Timely Probabilistic Data Preprocessing in Mobile Edge Computing

Cited by 5 publications

References 22 publications

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

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

Peak Age of Information Distribution for Edge Computing With Wireless Links

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

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