Task Aware Resource Allocation for Maximizing Throughput in Cloud Environment with Heuristic Knowledgebase Approach
Keywords:
Artificial Neural Network, QSAR, DFT, HUMO, LUMO, NClAbstract
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.
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