Towards Optimal Data Replication Across Data Centers

Ping, Fan; Xiao-hu, LI; McConnell, Christopher; Vabbalareddy, Rohini; Hwang, Jeong-Hyon

doi:10.1109/icdcsw.2011.49

Cited by 11 publications

(7 citation statements)

References 12 publications

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“…In [13], data replication across datacenters with the objective of reducing access delay is proposed. The Optimal replication site is selected based on the access history of the data.…”

Section: B Data Replicationmentioning

confidence: 99%

“…For better reliability and high performance low latency service provisioning, the data resources can be brought closer (replicated) to the physical infrastructure, where the cloud applications are executed. Therefore, a large number of replication strategies for data centers have been proposed [3], [11], [12], [13], [14]. These strategies optimize system bandwidth and data availability providing replication strategies between geographically distributed data centers.…”

Section: Introductionmentioning

confidence: 99%

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Energy-efficient data replication in cloud computing datacenters

Boru

Kliazovich

Granelli

et al. 2013

2013 IEEE Globecom Workshops (GC Wkshps)

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Abstract-Cloud computing is an emerging paradigm that provides computing resources as a service over a network. Communication resources often become a bottleneck in service provisioning for many cloud applications. Therefore, data replication, which brings data (e.g., databases) closer to data consumers (e.g., cloud applications), is seen as a promising solution. It allows minimizing network delays and bandwidth usage. In this paper we study data replication in cloud computing data centers. Unlike other approaches available in the literature, we consider both energy efficiency and bandwidth consumption of the system, in addition to the improved Quality of Service (QoS) as a result of the reduced communication delays. The evaluation results obtained during extensive simulations help to unveil performance and energy efficiency tradeoffs and guide the design of future data replication solutions.

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“…In [13], data replication across datacenters with the objective of reducing access delay is proposed. The Optimal replication site is selected based on the access history of the data.…”

Section: B Data Replicationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy-efficient data replication in cloud computing datacenters

Boru

Kliazovich

Granelli

et al. 2013

2013 IEEE Globecom Workshops (GC Wkshps)

View full text Add to dashboard Cite

show abstract

“…A file is shared by several tasks, while a task takes a set of files distributed on data centers as input. For all tasks, the set of input files can be denoted as {f 1 of tasks or task lengths (or task weights) are described as {te 1 , te 2 , ..., te N }. We use F (t i ) to denote the input files required by task t i .…”

Section: B Task Modelmentioning

confidence: 99%

“…The workload of each task t i is generated by α (S(F (t i )))/CCR, where α is a scalar weight factor and CCR denotes the Communication-to-Computation Ratios. The input files of a task t i are randomly selected and the number |F (t i )| is selected randomly from a uniform distribution with range [1,20].…”

Section: Performance Evaluation a Simulation Settingsmentioning

confidence: 99%

“…To meet enormous storage and computing requirements, major Internet companies, such as Google, Amazon, operate hundreds of thousands of servers in multiple geographic locations, and they continue to build more [1]. In scientific field, like astronomy, data centers that are closely connected to different telescopes, have been built in wide-area, distributed environments as well [2].…”

Section: Introductionmentioning

confidence: 99%

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Joint Scheduling of Data and Computation in Geo-Distributed Cloud Systems

Yin

Sun

Zhao

et al. 2015

2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

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Recent trends show that cloud computing is growing to span more and more globally distributed data centers. For geo-distributed data centers, there is an increasing need for scheduling algorithms to place tasks across data centers, by jointly considering data and computation. This scheduling must deal with situations such as wide-area distributed data, data sharing, WAN bandwidth costs and data center capacity limits, while also minimizing completion time. However, this kind of scheduling problems is known to be NP-Hard. In this paper, inspired by real applications in astronomy field, we propose a two-phase scheduling algorithm that addresses these challenges. The mapping phase groups tasks considering the data-sharing relations, and dispatches groups to data centers by way of one-to-one correspondence. The reassigning phase balances the completion time across data centers according to relations between tasks and groups. We utilize the real China-Astronomy-Cloud model and typical applications to evaluate our proposal. Simulations show that our algorithm obtains up to 22% better completion time and effectively reduces the amount of data transfers compared with other similar scheduling algorithms. 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing 978-1-4799-8006-2/15 $31.00

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Fault Aware Improved Clustering Algorithm to Improve Execution Time of the Cloudlets

Kaur

2018

Communications in Computer and Information Science

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Towards Optimal Data Replication Across Data Centers

Cited by 11 publications

References 12 publications

Energy-efficient data replication in cloud computing datacenters

Energy-efficient data replication in cloud computing datacenters

Joint Scheduling of Data and Computation in Geo-Distributed Cloud Systems

Fault Aware Improved Clustering Algorithm to Improve Execution Time of the Cloudlets

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