Data popularity measurements in distributed systems: Survey and design directions

Hamdeni, C.; Hamrouni, Tarek; Charrada, Faouzi Ben

doi:10.1016/j.jnca.2016.06.002

Cited by 16 publications

(5 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides, the data popularity plays a vital role in optimizing the file assignment [17], replication management [13], [14], [15], and load balancing [18] in the intelligent data management systems. It measures the frequency of data access and correlates closely with service-related objectives, such as the hit ratio and the request throughput [19], [20].…”

Section: Related Workmentioning

confidence: 99%

“…It measures the frequency of data access and correlates closely with service-related objectives, such as the hit ratio and the request throughput [19], [20]. A highlighted solution to avoid service overload is replication management [13], [14], [15], [16]. Hamdeni et al [13] provide a comprehensive survey on the data popularity and emphasize the importance of data replicas.…”

Section: Related Workmentioning

confidence: 99%

“…A highlighted solution to avoid service overload is replication management [13], [14], [15], [16]. Hamdeni et al [13] provide a comprehensive survey on the data popularity and emphasize the importance of data replicas. Literature [14] increases the replicas for hot data and allocates them evenly across the storage system for convenient data retrieval.…”

Section: Related Workmentioning

confidence: 99%

“…The ambition is to migrate a part of the four files from server 1 to server 2. hot files, may impact the service adaptability significantly in practice [2]. On the other hand, the popularity-aware replication managements [13], [14], [15], [16] or file assignments [17] improve the service performance undoubtedly. However, they are currently not incorporated with the space reduction technologies in the deduplicated storage systems.…”

mentioning

confidence: 99%

See 3 more Smart Citations

When Deduplication Meets Migration: An Efficient and Adaptive Strategy in Distributed Storage Systems

Cheng

Luo

Xia

et al. 2023

IEEE Trans. Parallel Distrib. Syst.

View full text Add to dashboard Cite

The traditional migration methods are confronted with formidable challenges when data deduplication technologies are incorporated. First, the deduplication creates data-sharing dependencies in the stored files; breaking such dependencies in migration may attach extra space overhead. Second, the redundancy elimination makes the storage system reserves only one copy for each storage file, and heightens the risk of data unavailability. The existing methods fail to tackle them in one shot. To this end, we propose Jingwei, an efficient and adaptive data migration strategy for deduplicated storage systems. To be specific, Jingwei tries to minimize the extra space cost in migration for space efficiency. Meanwhile, Jingwei realizes the service adaptability by encouraging replicas of hot files to spread out their data access requirements. We first model such a problem as an integer linear programming (ILP) and solve it with a commercial solver when only one empty migration target server is allowed. We then extend this problem to a scenario wherein multiple non-empty target servers are available for migration. We solve it by effective heuristic algorithms based on the Bloom Filter-based data sketches. The Jingwei strategy can suffer from performance degradation when the heat degree varies significantly. Therefore, we further present incremental adjustment strategies for the two scenarios, which adjust the number of block replicas and their locations in an incremental manner. The mathematical analyses and trace-driven experiments show the effectiveness of our Jingwei strategy. To be specific, Jingwei fortifies the file replicas by 25% with only 5.7% of the extra storage space, compared with the latest "Goseed" method. With the small extra space cost, the file retrieval throughput of Jingwei can reach up to 333.5 Mbps, which is 12.3% higher than that of the Random method.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

When Deduplication Meets Migration: An Efficient and Adaptive Strategy in Distributed Storage Systems

Cheng

Luo

Xia

et al. 2023

IEEE Trans. Parallel Distrib. Syst.

View full text Add to dashboard Cite

show abstract

“…One of these factors is the popularity of files. It reflects the importance of a file in the system 1,10 . In this regard, previous studies 11‐13 have shown that certain files are preferred and have a much greater access bias than other files in the system.…”

Section: Introductionmentioning

confidence: 98%

Popularity and correlation aware data replication strategy based on half‐life concept and clustering in cloud system

Chellouf

Hamrouni

2020

Concurrency and Computation

View full text Add to dashboard Cite

Summary In the new millennium, a myriad of large‐scale applications (e.g., social networks, ecommerce, internet of things, and scientific experiments) often generate large volumes of data. Given their volumes, their heterogeneous and distributed nature, the management of such data constitutes a challenge for distributed systems, particularly cloud computing. In this regard, data replication is a well‐known and effective data management technique that consists in creating multiple copies of the same data in different storage resources. In this article, we propose a popularity and correlation based data replication strategy, called PCDR. The main idea of our strategy is to replicate a set of the most popular correlated file groups based on files access history analysis. In this respect, the popularity of the files in each group is determined while taking into consideration temporal locality. Moreover, a clustering technique is used to find groups of correlated files. Using the CloudSim simulator, extensive experimentations show that our proposed strategy outperforms other strategies for several evaluation metrics.

show abstract

Peer-to-Peer Lookup Process Based on Data Popularity

Cherbal

Lamraoui

2020

Learning and Analytics in Intelligent Systems

View full text Add to dashboard Cite

Data popularity measurements in distributed systems: Survey and design directions

Cited by 16 publications

References 44 publications

When Deduplication Meets Migration: An Efficient and Adaptive Strategy in Distributed Storage Systems

When Deduplication Meets Migration: An Efficient and Adaptive Strategy in Distributed Storage Systems

Popularity and correlation aware data replication strategy based on half‐life concept and clustering in cloud system

Peer-to-Peer Lookup Process Based on Data Popularity

Contact Info

Product

Resources

About