An Optimized Resource Allocation Approach for Data-Intensive Workloads Using Topology-Aware Resource Allocation

This paper proposes an optimized resource allocation mechanism in Infrastructure-as-a-Service (IaaS)- based cloud systems. Performance of distributed data-intensive applications are impacted significantly as current IaaS systems are usually unaware of the hosted application\´s requirements and hence allocating resources independent of its needs. To address this resource allocation problem and to optimise the allocation, we enhance an architecture that adopts a "what if" methodology to guide allocation decisions taken by the IaaS. The architecture uses a prediction engine with a lightweight simulator to estimate the performance of a given resource allocation and an evolutionary algorithm that includes an evolution strategies algorithm and a genetic algorithm, to find an optimized solution in the large search space.