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IEEE 2014 JAVA CLOUD COMPUTING PROJECTS Adaptive algorithm for minimizing cloud task length with prediction errors
The document presents an adaptive algorithm designed to minimize cloud task execution length, taking into account resource demand and user payment budget. It outlines the development of a cloud prototype with 56 virtual machines to evaluate the algorithm's performance, which shows favorable results even under resource contention. Additionally, it provides software configuration details required for implementation.
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IEEE 2014 JAVA CLOUD COMPUTING PROJECTS Adaptive algorithm for minimizing cloud task length with prediction errors
- 1. GLOBALSOFT TECHNOLOGIES IEEEPROJECTS & SOFTWARE DEVELOPMENTS IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS CELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401 Visit: www.finalyearprojects.org Mail to:ieeefinalsemprojects@gmail.com 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.