The Age-of-Information (AoI) has recently been proposed as an important metric for investigating the timeliness performance in information-update systems In this talk, we will first give a brief overview about the state-of-the-art research on this emerging topic Note that prior studies on AoI optimization often consider a Push model, which is concerned about when and how to "push " (i e , generate and transmit) the updated information to the user In stark contrast, we introduce a new Pull model, which is more relevant for certain applications (such as the real-time stock quotes service), where a user sends requests to the servers to proactively "pull " the information of interest Moreover, we will talk about how to employ request replication to reduce the AoI Interestingly, we find that under this new Pull model, replication schemes capture a novel tradeoff between different levels of information freshness and different response times across the servers, which can be exploited to minimize the expected AoI at the user s side Specifically, assuming Poisson updating process at the servers and exponentially distributed response time, we derive a closed-form formula for computing the expected AoI and obtain the optimal number of responses to wait for to minimize the expected AoI Finally, we use numerical simulations to elucidate our theoretical results Our findings show that waiting for more than one response can significantly reduce the AoI in most scenarios

Bo Ji received his B.E. and M.E. degrees in Information Science and Electronic Engineering from Zhejiang University, Hangzhou,China, in 2004 and 2006, respectively, and his Ph.D. degree in Electrical and Computer Engineering from The Ohio State University, Columbus, OH,USA, in 2012. Dr. Ji joined Department of Computer and Information Sciences (CIS) at Temple University in July 2014, where he is currently an assistant professor. He is also a faculty member of the Center for Networked Computing (CNC) at Temple. Prior to joining Temple University, he was a Senior Member of Technical Staff with AT&T Labs, San Ramon, CA, from January 2013 to June 2014. His research interests are in the modeling, analysis, control, and optimization of computer and networking systems, such as communication networks, information-update systems, cloud/datacenter networks, software-defined networks, and cyber-physical systems. Applying statistical and machine learning to networking research is his recent interest. Dr. Ji received National Science Foundation (NSF) CAREER Award and NSF CISE Research Initiation Initiative (CRII) Award both in 2017.