Minimizing the Age of Information through Queues (1709.04956v4)

Abstract: In this paper, we investigate scheduling policies that minimize the age of information in single-hop queueing systems. We propose a Last-Generated, First-Serve (LGFS) scheduling policy, in which the packet with the earliest generation time is processed with the highest priority. If the service times are i.i.d. exponentially distributed, the preemptive LGFS policy is proven to be age-optimal in a stochastic ordering sense. If the service times are i.i.d. and satisfy a New-Better-than-Used (NBU) distributional property, the non-preemptive LGFS policy is shown to be within a constant gap from the optimum age performance. These age-optimality results are quite general: (i) They hold for arbitrary packet generation times and arrival times (including out-of-order packet arrivals), (ii) They hold for multi-server packet scheduling with the possibility of replicating a packet over multiple servers, (iii) They hold for minimizing not only the time-average age and mean peak age, but also for minimizing the age stochastic process and any non-decreasing functional of the age stochastic process. If the packet generation time is equal to packet arrival time, the LGFS policies reduce to the Last-Come, First-Serve (LCFS) policies. Hence, the age optimality results of LCFS-type policies are also established.

• The paper introduces the prmp-LGFS-R policy which achieves age-optimality with exponential service times while ensuring throughput and delay optimal performance.
• The paper demonstrates that non-preemptive LGFS with replication is nearly age-optimal for New-Better-than-Used distributions, bounding the average age by the expected service time.
• The paper highlights the robust applicability of replicative scheduling techniques across diverse multi-server configurations to effectively reduce the Age of Information.

Age of Information Optimization in Multi-Server Queues

This paper discusses the optimization of the Age of Information (AoI) in single-hop queueing systems, proposing novel scheduling policies based on the Last-Generated, First-Serve (LGFS) discipline. The authors present both theoretical insights and algorithmic implementations to minimize AoI, a critical metric in real-time data communication reflecting the freshness of information available at a destination.

Key Contributions

The paper's core contribution is the introduction of the preemptive Last-Generated, First-Serve with replication (prmp-LGFS-R) policy and its non-preemptive variant, which prioritize packets based on their generation times rather than arrival times. These policies aim to optimize AoI in multi-server systems with varying scheduling dynamics, including packet replications and out-of-order arrivals.

1. Age-Optimality with Exponential Service Times:
   • The prmp-LGFS-R policy achieves age-optimality within the policy space when service times are exponentially distributed.
   • It minimizes the age stochastic process and any associated penalty functions, such as average age or peak age, compared to all policies with the same maximum replication degree.
   • It is also depicted that the prmp-LGFS-R policy is throughput and delay optimal.
2. Near Age-Optimality for NBU Distributions:
   • For New-Better-than-Used (NBU) service time distributions, the non-preemptive LGFS with replication policy demonstrates near-optimal AoI performance.
   • The gap from the optimal average age is bounded by the expected service time, independent of system parameters.
3. Robustness Across System Configurations:
   • The authors derive results applicable across different server configurations, buffer sizes, and replication strategies.
   • The use of replicative techniques is shown to reduce AoI effectively by exploiting server diversity, even when replication does not favor throughput or delay performance for certain distributions.

Implications and Future Directions

The methodologies proposed have profound implications for designing real-time information systems and networks, particularly in settings requiring frequent updates. These findings suggest the potential for further research into replicative scheduling policies, integrating AI-driven predictive modeling to dynamically adjust replication degrees and scheduling priorities based on real-time data analysis.

Future research directions may include:

• Extending the age-optimality framework to multihop networks, exploring routing policy impacts on AoI.
• Investigating the effects of dynamic real-world conditions on AoI and potential adaptive strategies leveraging machine learning.
• Examining cross-layer optimization strategies that can integrate LGFS policies with network protocol advancements to further enhance AoI.

In essence, this paper provides significant insights into AoI optimization strategies, shedding light on innovative scheduling approaches that are crucial for enhancing data freshness in networked systems, a cornerstone for modern digital communication technologies.