高效且及时的存储器访问
Efficient and Timely Memory Access
AI总结：
本文研究状态更新系统中存储器采样的优化，提出离散时间决策问题，证明最优策略为平稳确定性阈值型，并推导出最优阈值与平均成本。
摘要：
本文研究了状态更新系统中的存储器采样优化问题，其中源更新被发布在共享存储器中，读者进程通过支付采样成本来对存储器进行采样以获取源更新。我们构建了一个离散时间决策问题，以寻找一种采样策略，该策略最小化由客户端年龄和采样所产生的成本构成的平均成本。我们证明最优策略是一种平稳且确定性的阈值型策略，并随后推导出最优阈值及相应的最优平均成本。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
