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Maintaining Information Freshness in Power-Efficient Status Update Systems
arXiv - CS - Networking and Internet Architecture Pub Date : 2020-03-30 , DOI: arxiv-2003.13577 Parisa Rafiee and Peng Zou and Omur Ozel and Suresh Subramaniam
arXiv - CS - Networking and Internet Architecture Pub Date : 2020-03-30 , DOI: arxiv-2003.13577 Parisa Rafiee and Peng Zou and Omur Ozel and Suresh Subramaniam
This paper is motivated by emerging edge computing systems which consist of
sensor nodes that acquire and process information and then transmit status
updates to an edge receiver for possible further processing. As power is a
scarce resource at the sensor nodes, the system is modeled as a tandem
computation-transmission queue with power-efficient computing. Jobs arrive at
the computation server with rate $\lambda$ as a Poisson process with no
available data buffer. The computation server can be in one of three states:
(i) OFF: the server is turned off and no jobs are observed or processed, (ii)
ON-Idle: the server is turned on but there is no job in the server, (iii)
ON-Busy: the server is turned on and a job is processed in the server. These
states cost zero, one and $p_c$ units of power, respectively. Under a long-term
power constraint, the computation server switches from one state to another in
sequence: first a deterministic $T_o$ time units in OFF state, then waiting for
a job arrival in ON-Idle state and then in ON-Busy state for an independent
identically distributed compute time duration. The transmission server has a
single unit data buffer to save incoming packets and applies last come first
serve with discarding as well as a packet deadline to discard a sitting packet
for maintaining information freshness, which is measured by the Age of
Information (AoI). Additionally, there is a monotonic functional relation
between the mean time spent in ON-Busy state and the mean transmission time. We
obtain closed-form expressions for average AoI and average peak AoI. Our
numerical results illustrate various regimes of operation for best AoI
performances optimized over packet deadlines with relation to power efficiency.
中文翻译:
在节能状态更新系统中保持信息的新鲜度
本文受新兴边缘计算系统的启发,该系统由传感器节点组成,这些传感器节点获取和处理信息,然后将状态更新传输到边缘接收器以进行可能的进一步处理。由于功率是传感器节点的稀缺资源,因此该系统被建模为具有节能计算的串联计算-传输队列。作业作为泊松过程以 $\lambda$ 的速率到达计算服务器,没有可用的数据缓冲区。计算服务器可以处于以下三种状态之一:(i) OFF:服务器关闭,没有观察或处理任何作业,(ii) ON-Idle:服务器打开但服务器中没有作业, (iii) ON-Busy:服务器开启,在服务器中处理作业。这些状态分别消耗 0、1 和 $p_c$ 单位的功率。在长期的权力约束下,计算服务器按顺序从一种状态切换到另一种状态:首先确定性的 $T_o$ 时间单位处于 OFF 状态,然后在 ON-Idle 状态等待作业到达,然后在 ON-Busy 状态中等待一个独立的同分布计算持续时间. 传输服务器具有单个单元数据缓冲区来保存传入的数据包,并应用后到先服务和丢弃以及数据包截止时间来丢弃现有数据包以保持信息新鲜度,这是由信息年龄 (AoI) 衡量的。此外,处于 ON-Busy 状态的平均时间与平均传输时间之间存在单调函数关系。我们获得了平均 AoI 和平均峰值 AoI 的封闭形式表达式。
更新日期:2020-03-31
中文翻译:
在节能状态更新系统中保持信息的新鲜度
本文受新兴边缘计算系统的启发,该系统由传感器节点组成,这些传感器节点获取和处理信息,然后将状态更新传输到边缘接收器以进行可能的进一步处理。由于功率是传感器节点的稀缺资源,因此该系统被建模为具有节能计算的串联计算-传输队列。作业作为泊松过程以 $\lambda$ 的速率到达计算服务器,没有可用的数据缓冲区。计算服务器可以处于以下三种状态之一:(i) OFF:服务器关闭,没有观察或处理任何作业,(ii) ON-Idle:服务器打开但服务器中没有作业, (iii) ON-Busy:服务器开启,在服务器中处理作业。这些状态分别消耗 0、1 和 $p_c$ 单位的功率。在长期的权力约束下,计算服务器按顺序从一种状态切换到另一种状态:首先确定性的 $T_o$ 时间单位处于 OFF 状态,然后在 ON-Idle 状态等待作业到达,然后在 ON-Busy 状态中等待一个独立的同分布计算持续时间. 传输服务器具有单个单元数据缓冲区来保存传入的数据包,并应用后到先服务和丢弃以及数据包截止时间来丢弃现有数据包以保持信息新鲜度,这是由信息年龄 (AoI) 衡量的。此外,处于 ON-Busy 状态的平均时间与平均传输时间之间存在单调函数关系。我们获得了平均 AoI 和平均峰值 AoI 的封闭形式表达式。