Trading Off Computation with Transmission in Status Update Systems

Zou, Peng; Özel, Ömur; Subramaniam, Suresh

doi:10.1109/pimrc.2019.8904244

Cited by 25 publications

(14 citation statements)

References 37 publications

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“…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%

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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Section: Introductionmentioning

confidence: 99%

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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“…We use the equivalent queue model approach in [6]- [8] to analyze the system. This model produces identical AoI expression as in the actual system and simplifies analysis.…”

Section: Equivalent Queue Modelmentioning

confidence: 99%

“…server system for improving average AoI. In [6], [7] waiting is used as a mechanism to regulate the traffic while in [8] tandem computation-transmission operations and queue management are combined. We also refer the reader to [9]- [24] for other work closely related to this paper.…”

Section: Introductionmentioning

confidence: 99%

“…We build on previous papers [6]- [8] and perform average AoI and average peak AoI analysis with two new aspects that were not considered before: (a) The computation server goes to OFF (stand-by) state as a means to save power, and (b) The transmission server applies a packet deadline for the sitting packet while using a last come first serve (LCFS) with discarding policy. As in [8], our motivation comes from applications in which computation at the sensor node could be prolonged in order to reduce the amount of data to be transmitted to the edge receiver. For example, the computation unit could represent an image processing device that has to inform a remote receiver about the image it captured in a timely manner.…”

Section: Introductionmentioning

confidence: 99%

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Maintaining Information Freshness in Power-Efficient Status Update Systems

Rafiee¹,

Zou²,

Özel³

et al. 2020

Preprint

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This paper is motivated by emerging edge computing systems which consist of sensor nodes that acquire and process information and then transmit status updates to an edge receiver for possible further processing. As power is a scarce resource at the sensor nodes, the system is modeled as a tandem computation-transmission queue with power-efficient computing. Jobs arrive at the computation server with rate λ as a Poisson process with no available data buffer. The computation server can be in one of three states: (i) OFF: the server is turned off and no jobs are observed or processed, (ii) ON-Idle: the server is turned on but there is no job in the server, (iii) ON-Busy: the server is turned on and a job is processed in the server. These states cost zero, one and pc > 1 units of power, respectively. Under a longterm power constraint, the computation server switches from one state to another in sequence: first a deterministic To time units in OFF state, then waiting for a job arrival in ON-Idle state and then in ON-Busy state for an independent identically distributed compute time duration. The transmission server has a single unit data buffer to save incoming packets and applies last come first serve with discarding as well as a packet deadline to discard a sitting packet for maintaining information freshness, which is measured by the Age of Information (AoI). Additionally, there is a monotonic functional relation between the mean time spent in ON-Busy state and the mean transmission time. We obtain closed-form expressions for average AoI and average peak AoI. Our numerical results illustrate various regimes of operation for best AoI performances optimized over packet deadlines with relation to power efficiency.

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“…[28] considered AoI minimization in two data processing scenes, the complicated initial feature extraction and classification in computer vision, as well as the optimization of sampling and updating processes in an Internet of things device's sampled physical process. The authors in [29] considered a general analysis with packet management for average AoI and average peak AoI with a computation server and a transmission queue. Ref.…”

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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Trading Off Computation with Transmission in Status Update Systems

Cited by 25 publications

References 37 publications

Timely Cloud Computing: Preemption and Waiting

Timely Cloud Computing: Preemption and Waiting

Maintaining Information Freshness in Power-Efficient Status Update Systems

Analysis on Computation-Intensive Status Update in Mobile Edge Computing

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