Age Based Task Scheduling and Computation Offloading in Mobile-Edge Computing Systems

Song, Xianxin; Qin, Xiaoqi; Tao, Yunzheng; Liu, Baoling; Zhang, Ping

doi:10.48550/arxiv.1905.11570

Cited by 4 publications

(12 citation statements)

References 12 publications

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“…3 cannot be optimal. 4 We note that the result of the lemma holds regardless of whether (28) holds or not. The proof is in Appendix C.…”

Section: Corollarymentioning

confidence: 87%

“…where the subscript zw stands for zero-wait. Now that we established a necessary and sufficient condition for the optimality of a zero-wait policy in Lemma 3, we proceed by investigating the case in which the inequality condition in (28) does not hold. First, an immediate corollary follows in this case.…”

Section: Lemmamentioning

confidence: 99%

“…Thus, a decision has to be made on whether to drop the newly arriving updates or switch to them via preemption. Recently, in the context of computing, AoI analysis has been carried out through various tandem queuing models in [24]- [27], and through a task-specific age metric in [28]. The notion of sending timely measurements to the cloud has been discussed in the context of gaming in [29].…”

Section: Introductionmentioning

confidence: 99%

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Timely Cloud Computing: Preemption and Waiting

Arafa

Yates

Poor

2019

2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

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The notion of timely status updating is investigated in the context of cloud computing. Measurements of a timevarying process of interest are acquired by a sensor node, and uploaded to a cloud server to undergo some required computations. These computations consume random amounts of service time that are independent and identically distributed across different uploads. After the computations are done, the results are delivered to a monitor, constituting an update. The goal is to keep the monitor continuously fed with fresh updates over time, which is assessed by an age-of-information (AoI) metric. A scheduler is employed to optimize the measurement acquisition times. Following an update, an idle waiting period may be imposed by the scheduler before acquiring a new measurement. The scheduler also has the capability to preempt a measurement in progress if its service time grows above a certain cutoff time, and upload a fresher measurement in its place. Focusing on stationary deterministic policies, in which waiting times are deterministic functions of the instantaneous AoI and the cutoff time is fixed for all uploads, it is shown that the optimal waiting policy that minimizes the long term average AoI has a threshold structure, in which a new measurement is uploaded following an update only if the AoI grows above a certain threshold that is a function of the service time distribution and the cutoff time. The optimal cutoff is then found for standard and shifted exponential service times. While it has been previously reported that waiting before updating can be beneficial for AoI, it is shown in this work that preemption of late updates can be even more beneficial.

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“…3 cannot be optimal. 4 We note that the result of the lemma holds regardless of whether (28) holds or not. The proof is in Appendix C.…”

Section: Corollarymentioning

confidence: 87%

Section: Lemmamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Timely Cloud Computing: Preemption and Waiting

Arafa

Yates

Poor

2019

2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

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“…With the increasing demand of real-time status update applications such as autonomous driving, virtual reality and etc., age of information (AoI) [1] is introduced as an effective data freshness metric, which is defined as the time elapsed since the generation of the latest received update. Recently, the impact of computing on AoI [2]- [8] is drawing more and more attention, as in many applications, the information embedded in a status update packet is not revealed until being processed. Due to the limited computing capacity of mobile devices, computing tasks for extracting information from status update packets are usually offloaded to the core network.…”

Section: Introductionmentioning

confidence: 99%

“…For multiple users, an optimal workconserving scheduling policy was proposed in [7]. A novel performance metric, age of task (AoT), was proposed in [8] where task scheduling, computation offloading and energy consumption were jointly considered. Most of the existing computation related AoI analysis assumed a random computing time.…”

Section: Introductionmentioning

confidence: 99%

Joint Transmission and Computing Scheduling for Status Update with Mobile Edge Computing

Gong

Kuang

Chen

2020

Preprint

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Age of Information (AoI), defined as the time elapsed since the generation of the latest received update, is a promising performance metric to measure data freshness for realtime status monitoring. In many applications, status information needs to be extracted through computing, which can be processed at an edge server enabled by mobile edge computing (MEC). In this paper, we aim to minimize the average AoI within a given deadline by jointly scheduling the transmissions and computations of a series of update packets with deterministic transmission and computing times. The main analytical results are summarized as follows. Firstly, the minimum deadline to guarantee the successful transmission and computing of all packets is given. Secondly, a no-wait computing policy which intuitively attains the minimum AoI is introduced, and the feasibility condition of the policy is derived. Finally, a closed-form optimal scheduling policy is obtained on the condition that the deadline exceeds a certain threshold. The behavior of the optimal transmission and computing policy is illustrated by numerical results with different values of the deadline, which validates the analytical results.

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Age of Processing: Age-Driven Status Sampling and Processing Offloading for Edge-Computing-Enabled Real-Time IoT Applications

Gong

et al. 2021

IEEE Internet Things J.

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The freshness of status information is of great importance for time-critical Internet of Things (IoT) applications. A metric measuring status freshness is the age-of-information (AoI), which captures the time elapsed from the status being generated at the source node (e.g., a sensor) to the latest status update. However, in intelligent IoT applications such as video surveillance, the status information is revealed after some computationintensive and time-consuming data processing operations, which would affect the status freshness. In this paper, we propose a novel metric, age-of-processing (AoP), to quantify such status freshness, which captures the time elapsed of the newest received processed status data since it is generated. Compared with AoI, AoP further takes the data processing time into account. Since an IoT device has limited computation and energy resource, the device can choose to offload the data processing to the nearby edge server under constrained status sampling frequency. We aim to minimize the average AoP in a long-term process by jointly optimizing the status sampling frequency and processing offloading policy. We formulate this online problem as an infinite-horizon constrained Markov decision process (CMDP) with average reward criterion. We then transform the CMDP problem into an unconstrained Markov decision process (MDP) by leveraging a Lagrangian method, and propose a Lagrangian transformation framework for the original CMDP problem. Furthermore, we integrate the framework with perturbation based refinement for achieving the optimal policy of the CMDP problem. Extensive numerical evaluations show that the proposed algorithm outperforms the benchmarks, with an average AoP reduction up to 30%.

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Age Based Task Scheduling and Computation Offloading in Mobile-Edge Computing Systems

Cited by 4 publications

References 12 publications

Timely Cloud Computing: Preemption and Waiting

Timely Cloud Computing: Preemption and Waiting

Joint Transmission and Computing Scheduling for Status Update with Mobile Edge Computing

Age of Processing: Age-Driven Status Sampling and Processing Offloading for Edge-Computing-Enabled Real-Time IoT Applications

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