Push message delivery, where a client maintains an “always-on” connection with a server in order to be notified of a (asynchronous) message arrival in real-time, is increasingly being used in Internet services. The key message in this paper is that push message delivery on the World Wide Web is not scalable for servers, intermediate network elements, and battery-operated mobile device clients. We present a measurement analysis of a commercially deployed WWW push email service to highlight some of these issues. Next, we suggest content-based optimization to reduce the always-on connection requirement of push messaging. Our idea is based on exploiting the periodic nature of human-to-human messaging. We show how machine learning can accurately model the times of a day or week when messages are least likely to arrive; and turn off always-on connections these times. We apply our approach to a real email data set and our experiments demonstrate that the number of hours of active always-on connections can be cut by half while still achieving real-time message delivery for up to 90% of all messages.
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