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Adaptive Algorithm for Minimizing Cloud
Task Length with Prediction Errors
Abstract— Compared to traditional distributed computing like grid
system, it is non-trivial to optimize cloud task’s execution
performance due to its more constraints like user payment budget
and divisible resource demand. In this paper, we analyze in-depth
our proposed optimal algorithm minimizing task execution length
with divisible resources and payment budget: 1) We derive the
upper bound of cloud task length, by taking into account both
workload prediction errors and hostload prediction errors. With
such state-of-the-art bounds, the worst-case task execution
performance is predictable, which can improve the quality of service
in turn. 2) We design a dynamic version for the algorithm to adapt to
the load dynamics over task execution progress, further improving
the resource utilization. 3) We rigorously build a cloud prototype
over a real cluster environment with 56 virtual machines, and
evaluate our algorithm with different levels of resource contention.
Cloud users in our cloud system are able to compose various tasks
based on off-the-shelf web services. Experiments show that task
execution lengths under our algorithm are always close to their
theoretical optimal values, even in a competitive situation with
limited available resources. We also observe a high level of fair
treatment on the resource allocation among all tasks.

2. S/W System Configuration:-
 Operating System :Windows95/98/2000/XP
 Application Server : Tomcat5.0/6.X
 Front End : HTML, Java, Jsp
 Scripts : JavaScript.
 Server side Script : Java Server Pages.
 Database : Mysql 5.0
 Database Connectivity : JDBC.