Waiting Before Serving: A Companion to Packet Management in Status Update Systems

Zou, Peng; Özel, Ömur; Subramaniam, Suresh

doi:10.1109/tit.2019.2963035

Cited by 47 publications

(21 citation statements)

References 35 publications

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“…Note that the improvement in average AoI is significantly higher for larger variances while it is not the case for peak AoI. This is analogous to the effect of waiting as in [29], [30] where larger variance in the service time distribution yields a higher improvement in average AoI. In particular, the mean service time E[P ] has an analogous role as waiting time from the point of view of the second queue.…”

Section: Numerical Results and Discussionmentioning

confidence: 87%

See 1 more Smart Citation

Trading Off Computation with Transmission in Status Update Systems

Zou

Özel

Subramaniam

2019

2019 IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

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This paper is motivated by emerging edge computing applications in which generated data are preprocessed at the source and then transmitted to an edge server. In such a scenario, there is typically a tradeoff between the amount of pre-processing and the amount of data to be transmitted. We model such a system by considering two non-preemptive queues in tandem whose service times are independent over time but the transmission service time is dependent on the computation service time in mean value. The first queue is in M/GI/1/1 form with a single server, memoryless exponential arrivals, general independent service and no extra buffer to save incoming status update packets. The second queue is in GI/M/1/2 * form with a single server receiving packets from the first queue, memoryless service and a single data buffer to save incoming packets. Additionally, mean service times of the first and second queues are dependent through a deterministic monotonic function. We perform stationary distribution analysis in this system and obtain closed form expressions for average age of information (AoI) and average peak AoI. Our numerical results illustrate the analytical findings and highlight the tradeoff between average AoI and average peak AoI generated by the tandem nature of the queueing system with dependent service times.

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Section: Numerical Results and Discussionmentioning

confidence: 87%

“…We use an equivalent queue model that yields an identical AoI pattern to our system's. This approach has first appeared in our earlier work [29], [30] for a single server queue. We adapt this approach to the tandem queue in the current paper by using it in the second queue.…”

Section: A Equivalent Tandem Queuementioning

confidence: 99%

Trading Off Computation with Transmission in Status Update Systems

Zou

Özel

Subramaniam

2019

2019 IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

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“…We analyze the benefits of idle waiting after both energy and data are available for a given source, with a focus on threshold policies, in which a new transmission occurs only if the time until energy and data arrives surpasses a certain threshold. Idle waiting before updating has been analyzed previously in [2] for a single source, yet in a nonenergy-harvesting setting with fixed waiting times, and in the single-source energy harvesting work in [3], yet with a first-come first-serve discipline with infinite battery and data storage. Our work focuses on last-come first-serve discipline with preemption, which is better for AoI minimization.…”

Section: Introductionmentioning

confidence: 99%

Timely Updating with Intermittent Energy and Data for Multiple Sources over Erasure Channels

Daniel¹,

Arafa²

2021

Preprint

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A status updating system is considered in which multiple data sources generate packets to be delivered to a destination through a shared energy harvesting sensor. Only one source's data, when available, can be transmitted by the sensor at a time, subject to energy availability. Transmissions are prune to erasures, and each successful transmission constitutes a status update for its corresponding source at the destination. The goal is to schedule source transmissions such that the collective longterm average age-of-information (AoI) is minimized. AoI is defined as the time elapsed since the latest successfully-received data has been generated at its source. To solve this problem, the case with a single source is first considered, with a focus on threshold waiting policies, in which the sensor attempts transmission only if the time until both energy and data are available grows above a certain threshold. The distribution of the AoI is fully characterized under such a policy. This is then used to analyze the performance of the multiple sources case under maximum-age-first scheduling, in which the sensor's resources are dedicated to the source with the maximum AoI at any given time. The achievable collective longterm average AoI is derived in closed-form. Multiple numerical evaluations are demonstrated to show how the optimal threshold value behaves as a function of the system parameters, and showcase the benefits of a threshold-based waiting policy with intermittent energy and data arrivals.

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“…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%

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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Waiting Before Serving: A Companion to Packet Management in Status Update Systems

Cited by 47 publications

References 35 publications

Trading Off Computation with Transmission in Status Update Systems

Trading Off Computation with Transmission in Status Update Systems

Timely Updating with Intermittent Energy and Data for Multiple Sources over Erasure Channels

Maintaining Information Freshness in Power-Efficient Status Update Systems

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