Age of information: Design and analysis of optimal scheduling algorithms

Hsu, Yu-Pin; Modiano, Eytan; Duan, Lingjie

doi:10.1109/isit.2017.8006590

Cited by 228 publications

(158 citation statements)

References 15 publications

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“…Fortunately, the relation between the optimal value of the constraint CMDP in (5) and the optimal value of the relaxed unconstrained MDP in (7) can be expressed as follows [13].…”

Section: B Cmdp Relaxation Via Lagrange Methodsmentioning

confidence: 99%

Age of Information Analysis for Dynamic Spectrum Sharing

Zhao

Zhou

Saad

et al. 2019

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

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Timely information updates are critical to timesensitive applications in networked monitoring and control systems. In this paper, the problem of real-time status update is considered for a cognitive radio network (CRN), in which the secondary user (SU) can relay the status packets from the primary user (PU) to the destination. In the considered CRN, the SU has opportunities to access the spectrum owned by the PU to send its own status packets to the destination. The freshness of information is measured by the age of information (AoI) metric. The problem of minimizing the average AoI and energy consumption by developing new optimal status update and packet relaying schemes for the SU is addressed under an average AoI constraint for the PU. This problem is formulated as a constrained Markov decision process (CMDP). The monotonic and decomposable properties of the value function are characterized and then used to show that the optimal update and relaying policy is threshold-based with respect to the AoI of the SU. These structures reveal a tradeoff between the AoI of the SU and the energy consumption as well as between the AoI of the SU and the AoI of the PU. An asymptotically optimal algorithm is proposed. Numerical results are then used to show the effectiveness of the proposed policy.Index Terms-Age of information, relay network, constrained Markov decision process (CMDP), scheduling.

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“…Fortunately, the relation between the optimal value of the constraint CMDP in (5) and the optimal value of the relaxed unconstrained MDP in (7) can be expressed as follows [13].…”

Section: B Cmdp Relaxation Via Lagrange Methodsmentioning

confidence: 99%

Age of Information Analysis for Dynamic Spectrum Sharing

Zhao

Zhou

Saad

et al. 2019

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

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“…where F µ (·) andF η (·) = 1 − F η (·) are given by (22) and (17), respectively. Proof: With the stable condition being satisfied, taking an expectation on both sides of (7) yields…”

Section: Peak Aoimentioning

confidence: 99%

Optimizing Information Freshness in Wireless Networks: A Stochastic Geometry Approach

Yang

Arafa

Quek

et al. 2021

IEEE Trans. on Mobile Comput.

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Optimization of information freshness in wireless networks has usually been performed based on queueing analysis that captures only the temporal traffic dynamics associated with the transmitters and receivers. However, the effect of interference, which is mainly dominated by the interferers' geographic locations, is not well understood. In this paper, we leverage a spatiotemporal model, which allows one to characterize the age of information (AoI) from a joint queueing-geometry perspective, for the design of a decentralized scheduling policy that exploits local observation to make transmission decisions that minimize the AoI. To quantify the performance, we also derive accurate and tractable expressions for the peak AoI. Numerical results reveal that: i) the packet arrival rate directly affects the service process due to queueing interactions, ii) the proposed scheme can adapt to traffic variations and largely reduce the peak AoI, and iii) the proposed scheme scales well as the network grows in size. This is done by adaptively adjusting the radio access probability at each transmitter to the change of the ambient environment.Index Terms-Poisson bipolar network, age of information, scheduling policy, spatiotemporal analysis, stochastic geometry.

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“…Theoretic lower bound for AoI performance is derived and various scheduling policies are proposed and analyzed in [10]. When the generation of update packets cannot be controlled at will and appear in a stochastic manner, theoretic performance and scheduling algorithms have been studied in [14]- [18]. However, scheduling policies provided in [14]- [17] are based on error free transmission, which is impossible for wireless communication scenario.…”

Section: Introductionmentioning

confidence: 99%

“…When the generation of update packets cannot be controlled at will and appear in a stochastic manner, theoretic performance and scheduling algorithms have been studied in [14]- [18]. However, scheduling policies provided in [14]- [17] are based on error free transmission, which is impossible for wireless communication scenario. Moreover, those researches all focus on AoI, while AoS, which measures the content desynchronization alone has not received much attention and is not well understood.…”

Section: Introductionmentioning

confidence: 99%

Scheduling to Minimize Age of Synchronization in Wireless Broadcast Networks With Random Updates

Tang

Wang

Tang

et al. 2020

IEEE Trans. Wireless Commun.

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In this work, a wireless broadcast network with a base station (BS) sending random time-sensitive information updates to multiple users with interference constraints is considered. The Age of Synchronization (AoS), namely the amount of time elapsed since the information stored at the network user becomes desynchronized, is adopted to measure data freshness from the perspective of network users.Compared with the more widely used metric-the Age of Information (AoI), AoS accounts for the freshness of the randomly changing content. The AoS minimization scheduling problem is formulated into a discrete time Markov decision process and the optimal solution is approximated through structural finite state policy iteration. An index based heuristic scheduling policy based on restless multi-arm bandit (RMAB) is provided to further reduce computational complexity. Simulation results show that the proposed index policy can achieve compatible performance with the MDP and close to the AoS lower bound. Moreover, theoretic analysis and simulations reveal the differences between AoS and AoI.AoI minimization scheduling policy cannot guarantee a good AoS performance. Index TermsAge of information, Age of synchronization, Markov decision processes, Whittle's index.The authors are with the DRAFT 3 unreliable wireless broadcast network with random information updates. Similar models with error-free transmissions are considered in [14]-[16]. To the best of the authors' knowledge, this is the first time that AoS is considered as the performance measure of such model. Our contributions are summarized as follows:• We formulate the problem of minimizing AoS in a broadcast wireless network with unreliable communication links. The theoretic lower bound of average AoS over the entire network is derived.• The problem is reformulated into a Markov decision process (MDP). Based on the switching structure that is exploited in the paper, we approximate the optimal solution to the MDP by using structural finite state policy iteration.• To overcome the computational load by the MDP solution, we propose an index based heuristic algorithm based on restless multi-arm bandit (RMAB). We prove that the problem is indexable and derive the closed form expression of the Whittle's index. Simulation resultsshow that the Whittle's index policy can achieve AoS performance close to the MDP solution and the AoS lower bound.The remainder of this paper is organized as follows. The network model and the two metrics, AoI and AoS are introduced and compared in Sec. II. The AoS lower bound is derived in Sec. III. In Sec. IV, we reformulate the problem into a Markov decision process and propose a finite state structural policy iteration to approximate the MDP solution. In Sec. V, we propose an index based algorithm based on restless multi-arm bandit. The scheduling strategies are evaluated through simulations and analyzed in Sec. VI. Sec. VII draws the conclusion.Notations: Vectors are written in boldface letters. The probability of event A given condition B is denoted as...

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Age of information: Design and analysis of optimal scheduling algorithms

Cited by 228 publications

References 15 publications

Age of Information Analysis for Dynamic Spectrum Sharing

Age of Information Analysis for Dynamic Spectrum Sharing

Optimizing Information Freshness in Wireless Networks: A Stochastic Geometry Approach

Scheduling to Minimize Age of Synchronization in Wireless Broadcast Networks With Random Updates

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