Heterogeneity-Aware Data Placement in Hybrid Clouds

Marquez, Jack; González, Juan de Dios; Mondragon, Oscar H.

doi:10.1007/978-3-030-23502-4_13

Cited by 1 publication

(4 citation statements)

References 24 publications

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“…Each of these policies uses a specified type of storage or a mix of types. Although several of these policies use only one type of storage, none exclusively use RAM_DISK; we therefore created a new policy called ALL_RAM, which allows us to store files only in RAM_DISK [9].…”

Section: Hadoop Heterogeneous Storagementioning

confidence: 99%

“…In the following, we formulate the problem to be solved by our data placement algorithm, which represents an improved version of our solution presented in [9]. In the present work, we have integrated an additional type of storage (ARCHIVE) that can be handled by our algorithm.…”

Section: Tier 2: Data Placement In Heterogeneous Storagementioning

confidence: 99%

“…Algorithm 1 shows the GA that is used to find the best data placement on heterogeneous types of storage. For a more detailed explanation of the GA used for data placement, the reader is referred to our previous paper, in which this was developed [9]. In this work, we use the GA to integrate ARCHIVE into a public cloud, and a third party cluster is responsible for allocating the data.…”

Section: Tier 2: Data Placement In Heterogeneous Storagementioning

confidence: 99%

“…A resource allocation hierarchical framework (Section 3) based on: (i) an improved version of our data placement algorithm previously presented in [9], which can now handle multicloud storage, and (ii) an algorithm for efficient virtual machine allocation that can handle NUMA domains and the shared memory patterns of applications. • A formulation of the problem of virtual machine placement on NUMA nodes as a genetic programming problem, and an implementation of an algorithm based on this formulation that optimizes virtual machine allocation, with the aim of improving the performance of applications running on virtual machines allocated to NUMA nodes (Section 3.1).…”

Section: Introductionmentioning

confidence: 99%

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An Intelligent Approach to Resource Allocation on Heterogeneous Cloud Infrastructures

Marquez¹,

Mondragon²,

González³

2021

Applied Sciences

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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.

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Section: Hadoop Heterogeneous Storagementioning

confidence: 99%

Section: Tier 2: Data Placement In Heterogeneous Storagementioning

confidence: 99%

Section: Tier 2: Data Placement In Heterogeneous Storagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations