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/infocom.2016.7524524
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发表时间：
2016-01
期刊：
影响因子：
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通讯作者：
Yin Sun;E. Uysal-Biyikoglu;R. Yates;C. E. Koksal;N. Shroff
中科院分区：
文献类型：
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作者：
Yin Sun;E. Uysal-Biyikoglu;R. Yates;C. E. Koksal;N. Shroff
在这项工作中，我们研究如何管理的新鲜度的状态更新从源发送到远程监视器通过网络服务器。监视器上数据新鲜度的一个适当度量是信息的年龄，它被定义为从源处生成此更新以来最新更新的时间。逻辑策略是零等待策略，即，一旦服务器空闲，源就提交新的更新，这实现了最大吞吐量和最小平均延迟。令人惊讶的是，这种零等待政策并不总是最小化平均年龄。这促使我们研究如何最佳地控制状态更新以保持数据新鲜，并了解何时零等待策略是最佳的。我们引入了一个惩罚函数来表征数据陈旧的“不满意”程度，并制定了平均年龄惩罚最小化问题作为一个受约束的半马尔可夫决策过程（SMDP）与不可数的状态空间。尽管这个问题的困难，我们开发了有效的算法来找到最佳的状态更新策略。我们表明，在许多情况下，最优策略是等待一定的时间，然后提交新的更新。特别地，如果（i）惩罚函数相对于年龄快速增长，并且（ii）更新服务时间是高度随机的并且正相关，则零等待策略可能远离最优。据我们所知，这是第一个最优控制策略，被证明是最小化年龄的信息状态更新系统。
In this work we study how to manage the freshness of status updates sent from a source to a remote monitor via a network server. A proper metric of data freshness at the monitor is the age-of-information, which is defined as how old the freshest update is since the moment this update was generated at the source. A logical policy is the zero-wait policy, i.e., the source submits a fresh update once the server is free, which achieves the maximum throughput and the minimum average delay. Surprisingly, this zero-wait policy does not always minimize the average age. This motivates us to study how to optimally control the status updates to keep data fresh and to understand when the zero-wait policy is optimal. We introduce a 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 process (SMDP) with an uncountable state space. Despite of the difficulty of this problem, we develop efficient algorithms to find the optimal status update policy. We show that, in many scenarios, the optimal policy is to wait for a certain amount of time before submitting a new update. In particular, the zero-wait policy can be far from the optimum if (i) the penalty function grows quickly with respect to the age, and (ii) the update service times are highly random and positive correlated. To the best of our knowledge, this is the first optimal control policy which is proven to minimize the age-of-information in status update systems.