Efficient and Timely Memory Access

TL;DR

This paper develops an optimal sampling policy for status updating systems that balances age and sampling costs, using a threshold-based approach to minimize average costs.

Contribution

It introduces a novel threshold-based policy for memory sampling in status updating systems and derives the optimal thresholds and costs.

Findings

Optimal policy is stationary and deterministic.

Derived explicit optimal thresholds.

Minimized average cost balancing age and sampling expenses.

Abstract

This paper investigates the optimization of memory sampling in status updating systems, where source updates are published in shared memory, and reader process samples the memory for source updates by paying a sampling cost. We formulate a discrete-time decision problem to find a sampling policy that minimizes average cost comprising age at the client and the cost incurred due to sampling. We establish that an optimal policy is a stationary and deterministic threshold-type policy, and subsequently derive optimal threshold and the corresponding optimal average cost.