  Cloud computing is a newly emerging distributed computing which is evolved
from Grid computing. Task scheduling is the core research of cloud computing
which studies how to allocate the tasks among the physical nodes so that the
tasks can get a balanced allocation or each task's execution cost decreases to
the minimum or the overall system performance is optimal. Unlike the previous
task slices' sequential execution of an independent task in the model of which
the target is processing time, we build a model that targets at the response
time, in which the task slices are executed in parallel. Then we give its
solution with a method based on an improved adjusting entropy function. At
last, we design a new task scheduling algorithm. Experimental results show that
the response time of our proposed algorithm is much lower than the
game-theoretic algorithm and balanced scheduling algorithm and compared with
the balanced scheduling algorithm, game-theoretic algorithm is not necessarily
superior in parallel although its objective function value is better.
