Abstract
Cloud computing systems are rapidly evolving toward multicloud architectures supported on heterogeneous hardware. Cloud service providers are widely offering different types of storage infrastructures and multi-NUMA architecture servers. Existing cloud resource allocation solutions do not comprehensively consider this heterogeneous infrastructure. In this study, we present a novel approach comprised of a hierarchical framework based on genetic programming to solve problems related to data placement and virtual machine allocation for analytics applications running on heterogeneous hardware with a variety of storage types and nonuniform memory access. Our approach optimizes data placement using the Hadoop File System on heterogeneous storage devices on multicloud systems. It guarantees the efficient allocation of virtual machines on physical machines with multiple NUMA (nonuniform memory access) domains by minimizing contention between workloads. We prove that our solutions for data placement and virtual machine allocation outperform other state-of-the-art approaches.
Highlights
We describe the cloud infrastructure for data storage and processing considered for our approach
We propose a hierarchical framework comprised of a set of genetic programming algorithms to solve data placement problems on heterogeneous storage and virtual machine allocation on multi-NUMA servers present in current cloud systems
To validate our genetic programming algorithm for VM mapping, we performed a set of experiments in which we used the algorithm to generate the optimal mappings of virtual machines to NUMA nodes
Summary
We describe the cloud infrastructure for data storage and processing considered for our approach. We describe several optimization methods that have been used to solve the problem of resource allocation in CC environments. Cloud Infrastructure for Data Storage and Processing. The use of a distributed infrastructure in the cloud to support ample data storage and processing is widespread. One of the most commonly used file systems is HDFS. Most cloud providers provide NUMA nodes that can be used to optimize the allocation of virtual machines. Our approach takes advantage of this computational infrastructure and is described below
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