Latency-Sensitive Data Allocation for cloud storage

Yang, Song; Wieder, Philipp; Aziz, Muzzamil; Yahyapour, Ramin; Fu, Xiaoming

doi:10.23919/inm.2017.7987258

Cited by 2 publications

(4 citation statements)

References 16 publications

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“…1) The base HDFS data placement 2) The proposed greedy data placement (GDP) 3) The proposed probabilistic data placement with c = 2 (PDP2) 4) The TSDAT data placement technique [37] 5) The UserRank data placement technique [23] 6) The DDP data placement technique [36] Furthermore, six different scenarios have been intended to study the performance of each data placement mechanism. Table 3 summarizes the details of simulated scenarios in terms of HDFS cluster and file specifications.…”

Section: Evaluation Resultsmentioning

confidence: 99%

“…This goal was achieved by picking the best choices for storing the clients' files based on a predetermined criterion, user concerns, and specifications of data storage nodes [19]- [23]. For instance, there are some data placement strategies to locate files in the storage cluster so that the response time of HDFS clients can be improved [19], [20], [37]. Moreover, different innovative ideas have been proposed to distribute file blocks among data storages to enhance cloud computation time [21], [22], [36].…”

Section: Introductionmentioning

confidence: 99%

“…Some existing data placement strategies focus on enhancing the HDFS metrics respecting the SLA and the client's concerns [19]- [23]. Under this category, there are some data placement strategies to locate files in the DataNode cluster so that the response time of HDFS clients has been improved [19], [20], [37]. For example, there is a technique in the literature that tries to revise the basic pipelined HDFS write scheme to a parallel scheme via placing replicas regarding the proximity to the corresponding client [37].…”

Section: Introductionmentioning

confidence: 99%

“…Under this category, there are some data placement strategies to locate files in the DataNode cluster so that the response time of HDFS clients has been improved [19], [20], [37]. For example, there is a technique in the literature that tries to revise the basic pipelined HDFS write scheme to a parallel scheme via placing replicas regarding the proximity to the corresponding client [37]. Furthermore, some data placement techniques concentrate on preserving data privacy [23].…”

Section: Introductionmentioning

confidence: 99%

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SLA-Aware Multi-Criteria Data Placement in Cloud Storage Systems

2021

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This paper proposes a multi-criteria data placement mechanism for typical cloud storage systems. The primary goal of this study is to place the clients' files in the storage cluster by taking into account the principles of service level agreements and the levels of user support services. For this purpose, a multi-criteria scoring model is defined based on a trade-off between three criteria that can be crucial from the clients' viewpoint: data access latency, data privacy, and computational performance. The second goal of the proposed mechanism is to improve the fairness of the storage allocation mechanism for various kinds of files compared to the conventional mechanisms. In this case, the cloud service provider gives each data storage node a chance to become the host of an already received file using a probabilistic ranking model. The experimental results demonstrate that the proposed data placement mechanism increases the robustness of cloud storage architecture under different file, network, and storage configurations compared to the existing data placement mechanisms.

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Section: Evaluation Resultsmentioning

confidence: 99%