Knowledge-based Resource Allocation Policy for Data Intensive Computing
In this paper,architecture for optimized resource allocation in Infrastructure-as-a-Service(IaaS)-based cloud systems was presented and described.This architecture used a knowledge-based approach,which derives application-specific information with little manual input and can find an optimized allocation with low latency and high confidence.The prototype of this policy is composed of a prediction engine which uses a lightweight simulator to estimate the performance of a given resource allocation and a genetic algorithm to find an optimized solution in the large search space.During the experiments,it focus on MapReduce-based data-intensive computing problems,the results show that our approach can reduce completion time by up to 30% when compared to simple knowledge-free allocation policies.