Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Design and Evaluation of a Resource Selection Framework for Grid Applications
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MapReduce optimization using regulated dynamic prioritization
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HotCloud'09 Proceedings of the 2009 conference on Hot topics in cloud computing
Purlieus: locality-aware resource allocation for MapReduce in a cloud
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Generalized resource allocation for the cloud
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PIKACHU: how to rebalance load in optimizing mapreduce on heterogeneous clusters
USENIX ATC'13 Proceedings of the 2013 USENIX conference on Annual Technical Conference
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This paper proposes an architecture for optimized resource allocation in Infrastructure-as-a-Service (IaaS)-based cloud systems. Current IaaS systems are usually unaware of the hosted application's requirements and therefore allocate resources independently of its needs, which can significantly impact performance for distributed data-intensive applications. To address this resource allocation problem, we propose 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 a genetic algorithm to find an optimized solution in the large search space. We have built a prototype for Topology-Aware Resource Allocation (TARA) and evaluated it on a 80 server cluster with two representative MapReduce-based benchmarks. Our results show that TARA reduces the job completion time of these applications by up to 59% when compared to application-independent allocation policies.