Read-Performance Optimization for Deduplication-Based Storage Systems in the Cloud

Mao, Bo; Jiang, Hong; Wu, Shaoen; Fu, Yinjin; Lan, Tian

doi:10.1145/2512348

Cited by 55 publications

(22 citation statements)

References 19 publications

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“…Some other impends of cost curtailment may include supervising the data redundancy in the cloud cluster. As in [28,29] different algorithms have been proposed for intermittently and nonintermittently used data.…”

Section: Cloudthingsmentioning

confidence: 99%

An Efficient Framework for Creating Twitter Mart on a Hybrid Cloud

Khan¹,

Naqvi²,

Alam³

et al. 2017

IJITCS

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Abstract-The contemporary era of technological quest is buzzing with two words -Big Data and Cloud Computing. Digital data is growing rapidly from Gigabytes (GBs), terabytes (TBs) to Petabytes (PBs), and thereby burgeoning data management challenges. Social networking sites like Twitter, Facebook, Google+ etc generate huge data chunks on daily basis. Among them, twitter masks as the largest source of publicly available mammoth data chunks intended for various objectives of research and development. In order to further research in this fast emerging area of managing Big Data, we propose a novel framework for doing analysis on Big Data and show its implementation by creating a "Twitter Mart" which is a compilation of subject specific tweets that address some of the challenges for industries engaged in analyzing subject specific data. In this paper, we adduce algorithms and an holistic model that aids in effective stockpiling and retrieving data in an efficient manner.

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Section: Cloudthingsmentioning

confidence: 99%

An Efficient Framework for Creating Twitter Mart on a Hybrid Cloud

Khan¹,

Naqvi²,

Alam³

et al. 2017

IJITCS

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“…Thus, a subsequent read request to the data will incur many small disk I/O operations, a phenomenon we call read amplification phenomenon [25]. Figure 4 shows the processing workflow in a traditional deduplication storage system.…”

Section: The Read Amplification Problemmentioning

confidence: 99%

“…In SSD/HDD hybrid storage systems, Yongseok et al [30] proposed a dynamic scheme to divide the flash memory cache to cache space and over-provisioned space, thus providing better cache performance and GC efficiency inside SSDs. SAR [24], [25] and Nitro [21] combines the flash-based SSD and data deduplication to improve the performance of primary storage systems. The iCache is inspired by these previous studies and designed to collaborate with Select-Dedupe to further eliminate the redundant write requests and address the read amplification problem in primary storage systems.…”

Section: Data Deduplicationmentioning

confidence: 99%

Leveraging Data Deduplication to Improve the Performance of Primary Storage Systems in the Cloud

Mao

Jiang

et al. 2016

IEEE Trans. Comput.

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With the explosive growth in data volume, the I/O bottleneck has become an increasingly daunting challenge for big data analytics in the Cloud. Recent studies have shown that moderate to high data redundancy clearly exists in primary storage systems in the Cloud. Our experimental studies reveal that data redundancy exhibits a much higher level of intensity on the I/O path than that on disks due to relatively high temporal access locality associated with small I/O requests to redundant data. Moreover, directly applying data deduplication to primary storage systems in the Cloud will likely cause space contention in memory and data fragmentation on disks. Based on these observations, we propose a performance-oriented I/O deduplication, called POD, rather than a capacityoriented I/O deduplication, exemplified by iDedup, to improve the I/O performance of primary storage systems in the Cloud without sacrificing capacity savings of the latter. POD takes a two-pronged approach to improving the performance of primary storage systems and minimizing performance overhead of deduplication, namely, a request-based selective deduplication technique, called Select-Dedupe, to alleviate the data fragmentation and an adaptive memory management scheme, called iCache, to ease the memory contention between the bursty read traffic and the bursty write traffic. We have implemented a prototype of POD as a module in the Linux operating system. The experiments conducted on our lightweight prototype implementation of POD show that POD significantly outperforms iDedup in the I/O performance measure by up to 87.9% with an average of 58.8%. Moreover, our evaluation results also show that POD achieves comparable or better capacity savings than iDedup.

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“…Cloud storage optimization [16][17][18][19][20][21][22] is another issue to improve the quality of service of the cloud computing systems. In [16], optimizations are proposed to reduce the volume of data to be transferred per data access for the respects of privacy and security, which did not consider the storage cost.…”

Section: Related Workmentioning

confidence: 99%

Multiobjective Reliable Cloud Storage with Its Particle Swarm Optimization Algorithm

Liu

Fan

Wei

et al. 2016

Mathematical Problems in Engineering

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Information abounds in all fields of the real life, which is often recorded as digital data in computer systems and treated as a kind of increasingly important resource. Its increasing volume growth causes great difficulties in both storage and analysis. The massive data storage in cloud environments has significant impacts on the quality of service (QoS) of the systems, which is becoming an increasingly challenging problem. In this paper, we propose a multiobjective optimization model for the reliable data storage in clouds through considering both cost and reliability of the storage service simultaneously. In the proposed model, the total cost is analyzed to be composed of storage space occupation cost, data migration cost, and communication cost. According to the analysis of the storage process, the transmission reliability, equipment stability, and software reliability are taken into account in the storage reliability evaluation. To solve the proposed multiobjective model, a Constrained Multiobjective Particle Swarm Optimization (CMPSO) algorithm is designed. At last, experiments are designed to validate the proposed model and its solution PSO algorithm. In the experiments, the proposed model is tested in cooperation with 3 storage strategies. Experimental results show that the proposed model is positive and effective. The experimental results also demonstrate that the proposed model can perform much better in alliance with proper file splitting methods.

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Read-Performance Optimization for Deduplication-Based Storage Systems in the Cloud

Cited by 55 publications

References 19 publications

An Efficient Framework for Creating Twitter Mart on a Hybrid Cloud

An Efficient Framework for Creating Twitter Mart on a Hybrid Cloud

Leveraging Data Deduplication to Improve the Performance of Primary Storage Systems in the Cloud

Multiobjective Reliable Cloud Storage with Its Particle Swarm Optimization Algorithm

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