Optimizing Information Freshness Through Computation-Transmission Tradeoff and Queue Management in Edge Computing

TL;DR

This paper analyzes a tandem queue system in edge computing to optimize data freshness by balancing computation and transmission times, providing analytical expressions for age metrics under various queue management schemes.

Contribution

It introduces a novel queue model capturing the computation-transmission tradeoff and derives closed-form expressions for age of information metrics.

Findings

Optimal tradeoff between computation and transmission times for minimal AoI.

Impact of queue management schemes on data freshness.

Analytical expressions for average AoI and peak AoI.

Abstract

Edge computing applications typically require generated data to be preprocessed at the source and then transmitted to an edge server. In such cases, transmission time and preprocessing time are coupled, yielding a tradeoff between them to achieve the targeted objective. This paper presents analysis of such a system with the objective of optimizing freshness of received data at the edge server. We model this system as two queues in tandem whose service times are independent over time but the transmission service time is monotonically dependent on the computation service time in mean value. This dependence captures the natural decrease in transmission time due to lower offloaded computation. We analyze various queue management schemes in this tandem queue where the first queue has a single server, Poisson packet arrivals, general independent service and no extra buffer to save incoming status update packets. The second queue has a single server receiving packets from the first queue and service is memoryless. We consider the second queue in two forms: (i) No data buffer and (ii) One unit data buffer and last come first serve with discarding. We analyze various non-preemptive as well as preemptive cases. We perform stationary distribution analysis and obtain closed form expressions for average age of information (AoI) and average peak AoI. Our numerical results illustrate analytical findings on how computation and transmission times could be traded off to optimize AoI and reveal a consequent tradeoff between average AoI and average peak AoI.