Effect of message transmission diversity on status age

Kam, Clement; Kompella, Sastry; Ephremides, Anthony

doi:10.1109/isit.2014.6875266

Cited by 89 publications

(41 citation statements)

References 3 publications

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“…Further, the lower-bound is achieved for 1) Pareto distributed service (11) as α → 1, 2) Log-normal distributed service (12) as σ → +∞, and 3) Weibull distributed service (13) as κ → 0.…”

Section: Lemma 3: For the G/g/∞ Queue The Average Is Given Bymentioning

confidence: 99%

“…Such an age drop occurs only when an update packet with a lower age, than all packets received thus far, is received by the destination. We refer to such packets, that result in age drops, as the informative packets [13].…”

Section: A Delay and Age Of Informationmentioning

confidence: 99%

“…2) Log-normal distributed service S, as σ → +∞; see (12). 3) Weibull distributed service S, as κ → 0; see (13). For the second part, we first compute E SI {S≤x} for α < α x :…”

Section: B Properties Of the Heavy Tailed Distributionsmentioning

confidence: 99%

“…Upon reception of a fresh update packet i, at time t i , the AoI drops to the time since the packet i was generated, which is t i −t i ; here t i is the time of generation of the update packet i. Unlike the traditional latency metrics such as packet delay, AoI only accounts for the update packets that deliver fresh updates to the destinationsuch packets are called informative packets [13]. For example, in Figure 1, packet 3 is an informative packet but packet 2 is not.…”

Section: Introductionmentioning

confidence: 99%

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Age-Delay Tradeoffs in Single Server Systems

Talak

Modiano

2019

2019 IEEE International Symposium on Information Theory (ISIT)

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We consider an M server system in which each server can service at most one update packet at a time. The system designer controls (1) scheduling -the order in which the packets get serviced, (2) routing -the server that an arriving update packet joins for service, and (3) the service time distribution with fixed service rate. Given a fixed update generation process, we prove a strong age-delay and age-delay variance tradeoff, wherein, as the average AoI approaches its minimum, the packet delay and its variance approach infinity. In order to prove this result, we consider two special cases of the M server system, namely, a single server system with last come first server with preemptive service and an infinite server system. In both these cases, we derive sufficient conditions to show that three heavy tailed service time distributions, namely Pareto, lognormal, and Weibull, asymptotically minimize the average AoI as their tail gets heavier, and establish the age-delay tradeoff results. We provide an intuitive explanation as to why such a seemingly counter intuitive age-delay tradeoff is natural, and that it should exist in many systems.

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Section: Lemma 3: For the G/g/∞ Queue The Average Is Given Bymentioning

confidence: 99%

Section: A Delay and Age Of Informationmentioning

confidence: 99%

“…2) Log-normal distributed service S, as σ → +∞; see (12). 3) Weibull distributed service S, as κ → 0; see (13). For the second part, we first compute E SI {S≤x} for α < α x :…”

Section: B Properties Of the Heavy Tailed Distributionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Age-Delay Tradeoffs in Single Server Systems

Talak

Modiano

2019

2019 IEEE International Symposium on Information Theory (ISIT)

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“…Age for M/M/2 and M/M/∞ systems was studied in [9], [10] to demonstrate the advantage of having parallel servers, while [13], analyzed parallel LCFS queues with preemptive service (LCFSp). Average age for a series of LCFSp queues in tandem was analysed in [15], [18].…”

Section: Introductionmentioning

confidence: 99%

When a Heavy Tailed Service Minimizes Age of Information

Talak

Karaman

Modiano

2019

2019 IEEE International Symposium on Information Theory (ISIT)

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Age-of-information (AoI) is a newly proposed performance metric of information freshness. It differs from the traditional delay metric, because it is destination centric and measures the time that elapsed since the last received fresh information update was generated at the source. AoI has been analyzed for several queueing models, and the problem of optimizing AoI over arrival and service rates has been studied in the literature. We consider the problem of minimizing AoI over the space of update generation and service time distributions. In particular, we ask whether determinacy, i.e. periodic generation of update packets and/or deterministic service, optimizes AoI. By considering several queueing systems, we show that in certain settings, deterministic service can in fact result in the worst case AoI, while a heavy-tailed distributed service can yield the minimum AoI. This leads to an interesting conclusion that, in some queueing systems, the service time distribution that minimizes expected packet delay, or variance in packet delay can, in fact, result in the worst case AoI. This exposes a fundamental difference between AoI metrics and packet delay.

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