  Scheduling of service requests in Cloud computing has traditionally focused
on the reduction of pre-service wait, generally termed as waiting time. Under
certain conditions such as peak load, however, it is not always possible to
give reasonable response times to all users. This work explores the fact that
different users may have their own levels of tolerance or patience with
response delays. We introduce scheduling strategies that produce better
assignment plans by prioritising requests from users who expect to receive the
results earlier and by postponing servicing jobs from those who are more
tolerant to response delays. Our analytical results show that the behaviour of
users' patience plays a key role in the evaluation of scheduling techniques,
and our computational evaluation demonstrates that, under peak load, the new
algorithms typically provide better user experience than the traditional FIFO
strategy.
