  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.
