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

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

doi:10.1109/wcncw.2019.8902529

Cited by 48 publications

(20 citation statements)

References 18 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%

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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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“…Then we calculate E[T i B i−1 ] in detail. For status update i, T i consists of service time and waiting time similar to (34). The waiting time W i is in accordance with the system time of the (i − 1)-th message, T i−1 , and the inter-arrival time B i .…”

Section: Appendix C Proof Of Theoremmentioning

confidence: 99%

“…In [33], a cloud computing status updating was studied with preemption policy. The authors in [34] proposed a new performance metric called age of task (AoT) to evaluate the temporal value of computation tasks and jointly considered task scheduling, computation offloading and energy consumption. Although the above papers considered data processing, they did not take advantage of MEC's short distance to the source node and sufficient computation resources.…”

Section: Introductionmentioning

confidence: 99%

Analysis on Computation-Intensive Status Update in Mobile Edge Computing

Kuang

Gong

Chen

et al. 2020

IEEE Trans. Veh. Technol.

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In status update scenarios, the freshness of information is measured in terms of age-of-information (AoI), which essentially reflects the timeliness for real-time applications to transmit status update messages to a remote controller. For some applications, computational expensive and time consuming data processing is inevitable for status information of messages to be displayed. Mobile edge servers are equipped with adequate computation resources and they are placed close to users. Thus, mobile edge computing (MEC) can be a promising technology to reduce AoI for computation-intensive messages. In this paper, we study the AoI for computation-intensive messages with MEC, and consider three computing schemes: local computing, remote computing at the MEC server, and partial computing, i.e., some part of computing tasks are performed locally, and the rest is executed at the MEC server. Zero-wait policy is adopted in all three schemes. Specifically, in local computing, a new message is generated immediately after the previous one is revealed by computing. While in remote computing and partial computing, a new message is generated once the previous one is received by the remote MEC server. With infinite queue size and exponentially distributed transmission time, closed-form average AoI for exponentially distributed computing time is derived for the three computing schemes. For deterministic computing time, the average AoI is analyzed numerically. Simulation results show that by carefully partitioning the computing tasks, the average AoI in partial computing is the smallest compared to local computing and remote computing. The results also indicate numerically the conditions on which remote computing attains smaller average AoI compared with local computing.

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