CPU Load Predictions on the Computational Grid Using Distance Based Algorithm

Need of computational resources and consumers of these resources are increases rapidly, to fulfill the desired computational demands up-gradation are necessary time to time. Efforts to obtain high computational units using grid computation are possible now in these days. But due to incremental request increases the complexity of grid additionally many problems arises on hardware as well as software level conflicts occurs. To prevent these faults in the complex grid predictive methods are helpful for plan and prevent these problems in advance. This paper provide the CPU load prediction method over multiple clustered grid environments, where more than one CPU is participating on the grid computation and load parameters are calculated, to justify the proposed method and their performance parameters.

[1]  R. Wolski,et al.  Predicting the CPU availability of time‐shared Unix systems on the computational grid , 1999, Proceedings. The Eighth International Symposium on High Performance Distributed Computing (Cat. No.99TH8469).

[2]  Peter A. Dinda,et al.  Size-based scheduling policies with inaccurate scheduling information , 2004, The IEEE Computer Society's 12th Annual International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems, 2004. (MASCOTS 2004). Proceedings..

[3]  Peter A. Dinda,et al.  Host load prediction using linear models , 2000, Cluster Computing.

[4]  Peter A. Dinda The Statistical Properties of Host Load (Extended Version) , 1999 .

[5]  Ian Foster,et al.  The Grid 2 - Blueprint for a New Computing Infrastructure, Second Edition , 1998, The Grid 2, 2nd Edition.

[6]  Peter A. Dinda,et al.  The statistical properties of host load , 1999, Sci. Program..

[7]  Richard Wolski,et al.  Experiences with predicting resource performance on-line in computational grid settings , 2003, PERV.

[8]  Lingyun Yang,et al.  Conservative Scheduling: Using Predicted Variance to Improve Scheduling Decisions in Dynamic Environments , 2003, ACM/IEEE SC 2003 Conference (SC'03).

[9]  Richard Wolski,et al.  Multivariate Resource Performance Forecasting in the Network Weather Service , 2002, ACM/IEEE SC 2002 Conference (SC'02).

[10]  Ian T. Foster,et al.  Homeostatic and tendency-based CPU load predictions , 2003, Proceedings International Parallel and Distributed Processing Symposium.

[11]  Xingfu Wu,et al.  Using Performance Prediction to Allocate Grid Resources , 2004 .

[12]  Peter A. Dinda,et al.  A prediction-based real-time scheduling advisor , 2002, Proceedings 16th International Parallel and Distributed Processing Symposium.

[13]  Alexandru Iosup,et al.  Grid Computing Workloads : Bags of Tasks , Workflows , Pilots , and Others , 2010 .

[14]  Ami Marowka,et al.  The GRID: Blueprint for a New Computing Infrastructure , 2000, Parallel Distributed Comput. Pract..

[15]  Yoichi Muraoka,et al.  Extended forecast of CPU and network load on computational Grid , 2004, IEEE International Symposium on Cluster Computing and the Grid, 2004. CCGrid 2004..

[16]  Richard Wolski,et al.  Dynamically forecasting network performance using the Network Weather Service , 1998, Cluster Computing.

[17]  Manish Parashar,et al.  Grid Computing : Introduction and Overview , .