Update or Wait: How to Keep Your Data Fresh
Update or Wait: How to Keep Your Data Fresh
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DOI：
10.1109/tit.2017.2735804
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发表时间：
2017-11-01
影响因子：
2.5
通讯作者：
Shroff, Ness B.
中科院分区：
文献类型：
--
作者：
Sun, Yin;Uysal-Biyikoglu, Elif;Shroff, Ness B.
在本文中，我们研究如何最佳地管理从源节点发送到目的地通过一个通道的信息更新的新鲜度。目的地处的数据新鲜度的适当度量是信息的年龄，或简单地说是年龄，其被定义为从在源节点处生成该更新的时刻起，最新接收的更新有多老（例如，传感器）。合理的更新策略是零等待策略，即，一旦先前的更新被递送，源节点就提交新的更新，这实现了最大吞吐量和最小延迟。令人惊讶的是，这种零等待政策并不总是最小化年龄。这种违反直觉的现象促使我们研究如何最佳地控制信息更新以保持数据新鲜，并了解零等待策略何时是最佳的。我们引入了一个通用的年龄惩罚函数来描述对数据陈旧性的不满程度，并将平均年龄惩罚最小化问题表示为具有不可数状态空间的约束半马尔可夫决策问题。我们开发了有效的算法，找到最佳的更新策略之间的所有因果政策，并建立零等待政策的最优性的充分和必要条件。我们的研究表明，如果：1）年龄惩罚函数相对于年龄快速增长; 2）信道上的分组传输时间随时间正相关;或者3）分组传输时间是高度随机的（例如，重尾分布）。
In this paper, we study how to optimally manage the freshness of infimmation updates sent from a source node to a destination via a channel. A proper metric for data freshness at the destination is the age-of-information, or simply age, which is defined as how old the freshest received update is, since the moment that this update was generated at the source node (e.g., a sensor). A reasonable update policy is the zero wait policy, i.e., the source node submits a fresh update once the previous update is delivered, which achieves the maximum throughput and the minimum delay. Surprisingly, this zero wait policy does not always minimize the age. This counter-intuitive phenomenon motivates us to study how to optimally control information updates to keep the data fresh and to understand when the zero-wait policy is optimal. We introduce a general age penalty function to characterize the level of dissatisfaction on data staleness and formulate the average age penalty minimization problem as a constrained semi-Markov decision problem with an uncountable state space. We develop efficient algorithms to find the optimal update policy among all causal policies and establish sufficient and necessary conditions for the optimality of the zero-wait policy. Our investigation shows that the zero-wait policy is far from the optimum if: 1) the age penalty function grows quickly with respect to the age; 2) the packet transmission times over the channel are positively correlated over time; or 3) the packet transmission times are highly random (e.g., following a heavy-tail distribution).