A Dynamic Convergent Replica Selection Strategy Based on Cloud Storage

Zhao, Pu; Sun, Xin; Shang, Jingtao; Lin, Jianjun; Dong, Mingming; Bo, Li

doi:10.1109/aiam48774.2019.00100

Cited by 3 publications

(1 citation statement)

References 5 publications

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“…They proposed a cloud storage framework that analyzes an HDFS storage system, an architecture over multiple data centers, and a selection strategy based on a replica layout mechanism and data consistency maintenance strategies. Regarding the former, Zhao et al [ 39 ] also presented data placement and retrieval methods for an HDFS storage system. They studied the data replica selection and replication strategies by taking into consideration bandwidth, the performance of peers, and the replica’s history request.…”

Section: Related Workmentioning

confidence: 99%

Smart Data Placement Using Storage-as-a-Service Model for Big Data Pipelines

Khan

Nikolov

Matskin

et al. 2023

Sensors

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Big data pipelines are developed to process data characterized by one or more of the three big data features, commonly known as the three Vs (volume, velocity, and variety), through a series of steps (e.g., extract, transform, and move), making the ground work for the use of advanced analytics and ML/AI techniques. Computing continuum (i.e., cloud/fog/edge) allows access to virtually infinite amount of resources, where data pipelines could be executed at scale; however, the implementation of data pipelines on the continuum is a complex task that needs to take computing resources, data transmission channels, triggers, data transfer methods, integration of message queues, etc., into account. The task becomes even more challenging when data storage is considered as part of the data pipelines. Local storage is expensive, hard to maintain, and comes with several challenges (e.g., data availability, data security, and backup). The use of cloud storage, i.e., storage-as-a-service (StaaS), instead of local storage has the potential of providing more flexibility in terms of scalability, fault tolerance, and availability. In this article, we propose a generic approach to integrate StaaS with data pipelines, i.e., computation on an on-premise server or on a specific cloud, but integration with StaaS, and develop a ranking method for available storage options based on five key parameters: cost, proximity, network performance, server-side encryption, and user weights/preferences. The evaluation carried out demonstrates the effectiveness of the proposed approach in terms of data transfer performance, utility of the individual parameters, and feasibility of dynamic selection of a storage option based on four primary user scenarios.

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Section: Related Workmentioning

confidence: 99%

Smart Data Placement Using Storage-as-a-Service Model for Big Data Pipelines

Khan

Nikolov

Matskin

et al. 2023

Sensors

View full text Add to dashboard Cite

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A Fuzzy Logic-Based Method for Replica Placement in the Peer to Peer Cloud Using an Optimization Algorithm

Mohammadi

Navimipour

2021

Wireless Pers Commun

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Cloud storage cost: a taxonomy and survey

Khan,

Matskin,

Prodan

et al. 2024

World Wide Web

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Cloud service providers offer application providers with virtually infinite storage and computing resources, while providing cost-efficiency and various other quality of service (QoS) properties through a storage-as-a-service (StaaS) approach. Organizations also use multi-cloud or hybrid solutions by combining multiple public and/or private cloud service providers to avoid vendor lock-in, achieve high availability and performance, and optimise cost. Indeed cost is one of the important factors for organizations while adopting cloud storage; however, cloud storage providers offer complex pricing policies, including the actual storage cost and the cost related to additional services (e.g., network usage cost). In this article, we provide a detailed taxonomy of cloud storage cost and a taxonomy of other QoS elements, such as network performance, availability, and reliability. We also discuss various cost trade-offs, including storage and computation, storage and cache, and storage and network. Finally, we provide a cost comparison across different storage providers under different contexts and a set of user scenarios to demonstrate the complexity of cost structure and discuss existing literature for cloud storage selection and cost optimization. We aim that the work presented in this article will provide decision-makers and researchers focusing on cloud storage selection for data placement, cost modelling, and cost optimization with a better understanding and insights regarding the elements contributing to the storage cost and this complex problem domain.

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A Dynamic Convergent Replica Selection Strategy Based on Cloud Storage

Cited by 3 publications

References 5 publications

Smart Data Placement Using Storage-as-a-Service Model for Big Data Pipelines

Smart Data Placement Using Storage-as-a-Service Model for Big Data Pipelines

A Fuzzy Logic-Based Method for Replica Placement in the Peer to Peer Cloud Using an Optimization Algorithm

Cloud storage cost: a taxonomy and survey

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