A dynamic, cost-aware, optimized data replication strategy for heterogeneous cloud data centers

Gill, Navneet Kaur; Singh, Sarbjeet

doi:10.1016/j.future.2016.05.016

Cited by 71 publications

(63 citation statements)

References 30 publications

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“…Data replication is the strategy of cre-ating multiple data copies and storing the copies in multiple sites [11,18]. Data replication can help users save cost [7] and response time [13] when tasks are being processed, and improve the data availability [3,9,17] and reliability [6].…”

Section: Related Workmentioning

confidence: 99%

“…An average access frequency is used to compare with the access frequency of targeted datasets to find the popular file and rank the file. In [3], authors propose a dynamic, cost-aware data replication strategy by identifying the minimum number of replicas in order to satisfy the desired availability, get the maximum value and keep the total weight less than or equal to the peak budget at the same time.…”

Section: Related Workmentioning

confidence: 99%

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A Data Dependency and Access Threshold Based Replication Strategy for Multi-cloud Workflow Applications

Xie

Yan

Shen

2019

Lecture Notes in Computer Science

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Data replication is one of the significant sub-areas of data management in cloud based workflows. Dataintensive workflow applications can gain great benefits from cloud environments and usually need data management strategies to manage large amounts of data. At the same time, multi-cloud environments become more and more popular. We propose a cost-effective and threshold-based data replication strategy with the consideration of both data dependency and data access times for data-intensive workflows in the multi-cloud environment. Finally, the simulation results show that our approach can greatly reduce total cost of dataintensive workflow applications by considering both of data dependency and data access times in multi-cloud environments.Abstract. Data replication is one of the significant sub-areas of data management in cloud based workflows. Data-intensive workflow applications can gain great benefits from cloud environments and usually need data management strategies to manage large amounts of data. At the same time, multi-cloud environments become more and more popular. We propose a cost-effective and threshold-based data replication strategy with the consideration of both data dependency and data access times for data-intensive workflows in the multi-cloud environment. Finally, the simulation results show that our approach can greatly reduce total cost of data-intensive workflow applications by considering both of data dependency and data access times in multi-cloud environments.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Data Dependency and Access Threshold Based Replication Strategy for Multi-cloud Workflow Applications

Xie

Yan

Shen

2019

Lecture Notes in Computer Science

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“…They have taken into account failure rate, size of the partitions, link capacity and the distance in replication cost calculation. [21] presented a dynamic replication algorithm for heterogeneous cloud data centers that ensures minimum number of replicas while maintaining desired level of data availability. Their algorithm works based on the concept of knapsack to minimize the replication cost while considering re-replication by moving replicas from high-cost data centers to low-cost ones.…”

Section: Related Workmentioning

confidence: 99%

Leveraging A Multi-Objective Approach to Data Replication in Cloud Computing Environment to Support Big Data Applications

Masud¹

2019

ijacsa

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Increased data availability and high data access performance are of utmost importance in a large-scale distributed system such as data cloud. To address these issues data can be replicated in various locations in the system where applications are executed. Replication not only improves data availability and access latency but also improves system load balancing. While data replication in distributed cloud storage is addressed in the literature, majority of the current techniques do not consider different costs and benefits of replication from a comprehensive perspective. In this paper, we investigate replica management problem (which is formulated using dynamic programming) in cloud computing environments to support big data applications. To this end, we propose a new highly distributed replica placement algorithm that provides costeffective replication of huge amount of geographically distributed data into the cloud to meet the quality of service (QoS) requirements of data-intensive (big data) applications while ensuring that the workload among the replica data centers is balanced. In addition, the algorithm takes into account the consistency among replicas due to update propagation. Thus, we build up a multi-objective optimization approach for replica management in cloud that seeks near optimal solution by balancing the trade-offs among the stated issues. For verifying the effectiveness of the algorithm, we evaluated the performance of the algorithm and compared it with two baseline approaches from the literature. The evaluation results demonstrate the usefulness and superiority of the presented algorithm for conditions of interest.

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“…Geo-replication [21], [37]: the process of maintaining multiple copies of objects on multiple sites (CSPs) for a better performance, availability, and reliability.…”

Section: ) a Requested Qos (Sla) In Terms Of Iops And Latencymentioning

confidence: 99%

A Cost Model for Hybrid Storage Systems in a Cloud Federations

Chikhaoui

Boukhalfa

Boukhobza

2018

Annals of Computer Science and Information Systems

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A cloud federation gives to cloud service providers (CSP) the opportunity to collaborate in order to offer a better QoS to customers at a lower cost. To do so, CSPs make some spare resources available to others at a reduced cost. One of the most critical resources is the storage system as it represents the main system bottleneck. From this point of view, how to efficiently place data in a federation of Clouds with heterogeneous storage systems is a real challenge. To address this issue, one needs to accurately estimate the data placement cost. In this paper, we propose a cost model for hybrid storage systems in a cloud federation for a Database as a Service (DBaaS) application. It takes into account the storage system characteristics, customers I/O workloads and SLA. The proposed cost model considers both 1) Internal customers data placement cost including local placement, outsourcing, back-migration and penalty costs, and 2) External customers data placement cost including insourcing and geo-migration costs. It can be used to help in the decision-making process which aims to enhance customers QoS and reduce CSPs costs in a federation. Simulation results showed the relevance of the considered costs. We have shown that mis-considering some sub-costs may lead to a 95% cost error for external customers data placement and 80% for outsourcing customers. This may cause significant financial loss.

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A dynamic, cost-aware, optimized data replication strategy for heterogeneous cloud data centers

Cited by 71 publications

References 30 publications

A Data Dependency and Access Threshold Based Replication Strategy for Multi-cloud Workflow Applications

A Data Dependency and Access Threshold Based Replication Strategy for Multi-cloud Workflow Applications

Leveraging A Multi-Objective Approach to Data Replication in Cloud Computing Environment to Support Big Data Applications

A Cost Model for Hybrid Storage Systems in a Cloud Federations

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