GoSeed: Optimal Seeding Plan for Deduplicated Storage

Nachman, Aviv; Sheinvald, Sarai; Kolikant, Ariel; Yadgar, Gala

doi:10.1145/3453301

Cited by 7 publications

(13 citation statements)

References 60 publications

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“…When a server is overloaded, part of its files must be migrated out to another server [7], [8]. However, the traditional migration methods are confronted with formidable challenges when data deduplication is incorporated to economize the scarce storage resource.…”

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confidence: 99%

“…Eliminating all redundancies would impact the service adaptability, but too many replicas would bring unnecessary space spending. The current data migration strategies coupled with data deduplication mainly focus on the capacity measurement [11], the space reduction [7], [12], etc. However, they are oblivious to the impact of data replicas.…”

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confidence: 99%

“…1. The illustrative examples of the Jingwei strategy and some existing methods [7], [17]. Four files (f 1 ∼ f 4 ), which are attached with heat degrees (1,8,6,7), are partitioned into five blocks (b 1 ∼ b 5 ).…”

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confidence: 99%

“…The illustrative examples of the Jingwei strategy and some existing methods [7], [17]. Four files (f 1 ∼ f 4 ), which are attached with heat degrees (1,8,6,7), are partitioned into five blocks (b 1 ∼ b 5 ). 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].…”

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confidence: 99%

“…The ambition is to migrate a portion of files from the overloaded Server 1 to the under-utilized Server 2. Scheme (a) [7] minimizes the amount of replicated data by allocating files with more common blocks to one server. Nevertheless, there are no file copies to guarantee the service adaptability.…”

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confidence: 99%

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

Cheng

Luo

Xia

et al. 2023

IEEE Trans. Parallel Distrib. Syst.

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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.

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confidence: 99%

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confidence: 99%

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confidence: 99%

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confidence: 99%

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confidence: 99%

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

Cheng

Luo

Xia

et al. 2023

IEEE Trans. Parallel Distrib. Syst.

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PASE: Pro-active Service Embedding in The Mobile Edge

Kolosov,

Yadgar,

Breitgand

et al. 2024

J Netw Syst Manage

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Mobile edge computing offers ultra-low latency, high bandwidth, and high reliability. Thus, it can support a plethora of emerging services that can be placed in close proximity to the user. One of the fundamental problems in this context is maximizing the benefit from the placement of networked services, while meeting bandwidth and latency constraints. In this study, we propose an adaptive and predictive resource allocation strategy for virtual-network function placement comprising services at the mobile edge. Our study focuses on maximizing the service provider’s benefit under user mobility, i.e., uncertainty. This problem is NP-hard. Therefore, we propose a heuristic solution: we exploit local knowledge about the likely movements of users to speculatively allocate service functions. We allow the service functions to be allocated at different edge nodes, as long as latency and bandwidth constraints are met. We evaluate our proposal against a theoretically optimal algorithm as well as against recent previous work, using widely used simulation tools. Through an extensive simulation study, we demonstrate that under realistic scenarios, an adaptive and proactive strategy coupled with flexible placement can achieve close-to-optimal benefit.

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