Task aware resource allocation for maximizing throughput in cloud environment with heuristic knowledgebase approach

In Cloud Environment, Resources available to the client on demand with pay per usage. Higher throughput with minimal execution time can reduce the budget cost for client as well as can never violate Service Level Agreement (SLA). Specific Task should be allocated to proper Virtual Machine can generate efficient result. Our study suggests a better approach to achieve this efficiency using empirical analysis for task by generating knowledgebase heuristic task database. In first step our approach suggest, before allocating a task for execution on Virtual Machine, find out task characteristic, estimate execution time by matching with self-generated heuristic database. During second step find out efficient virtual machine who is capable to do this task with higher throughput in minimum execution time. Better enhancement should be achieved using adaptive threshold value to compare task with heuristic database. This approach can optimize tradeoff between Quality of Service for task and resource utilization.

[1]  Francine Berman,et al.  Adaptive Computing on the Grid Using AppLeS , 2003, IEEE Trans. Parallel Distributed Syst..

[2]  Archana Ganapathi,et al.  Statistics-driven workload modeling for the Cloud , 2010, 2010 IEEE 26th International Conference on Data Engineering Workshops (ICDEW 2010).

[3]  Francisco Vilar Brasileiro,et al.  Trading Cycles for Information: Using Replication to Schedule Bag-of-Tasks Applications on Computational Grids , 2003, Euro-Par.

[4]  Hui Li,et al.  Predicting job start times on clusters , 2004, IEEE International Symposium on Cluster Computing and the Grid, 2004. CCGrid 2004..

[5]  Francine Berman,et al.  Application-Level Scheduling on Distributed Heterogeneous Networks , 1996, Proceedings of the 1996 ACM/IEEE Conference on Supercomputing.

[6]  Randy H. Katz,et al.  Above the Clouds: A Berkeley View of Cloud Computing , 2009 .

[7]  Eran Chinthaka Withana,et al.  Usage Patterns to Provision for Scientific Experimentation in Clouds , 2010, 2010 IEEE Second International Conference on Cloud Computing Technology and Science.

[8]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[9]  Richard McClatchey,et al.  Predicting the Resource Requirements of a Job Submission , 2004 .

[10]  Richard Wolski,et al.  QBETS: queue bounds estimation from time series , 2007, SIGMETRICS '07.

[11]  Warren Smith,et al.  Predicting Application Run Times Using Historical Information , 1998, JSSPP.

[12]  Francisco Vilar Brasileiro,et al.  Investigating Business-Driven Cloudburst Schedulers for E-Science Bag-of-Tasks Applications , 2010, 2010 IEEE Second International Conference on Cloud Computing Technology and Science.

[13]  Francine Berman,et al.  The GrADS Project: Software Support for High-Level Grid Application Development , 2001, Int. J. High Perform. Comput. Appl..