Scalable blockchain storage systems: research progress and models

Fan, Xing; Niu, Baoning

doi:10.1007/s00607-022-01063-8

Cited by 23 publications

(11 citation statements)

References 21 publications

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“…Data Redundancy in the Blockchain. The scalability issues of BC stem from its inherent append-only data structure, which necessitates increasingly larger storage resources [21]. The practice of maintaining full BC nodes locally results in excessive data redundancy, compelling many nodes to transition into light nodes.…”

Section: A Related Workmentioning

confidence: 99%

“…This transition poses a significant threat to decentralization and hampers the practical application of BC in offloading scenarios. In addition, various strategies have been devised to mitigate the scalability problems of BC, encompassing multiple node cooperative storage (MNCS), data reduction, and sharding [21]. MNCS schemes, such as the full and lightweight node approach, aim to alleviate data redundancy [22,23].…”

Section: A Related Workmentioning

confidence: 99%

“…MNCS schemes, such as the full and lightweight node approach, aim to alleviate data redundancy [22,23]. Data reduction techniques encompass data compression and pruning [24][25][26], while sharding partition nodes enhances throughput and alleviates storage pressure [21]. However, Optimizing the replica copy of a transaction in a BC network for multiuser scenarios is an enormous problem to overcome, given factors like cross-shared communication overhead, shard management, BC costs, and overall offloading expenses.…”

Section: A Related Workmentioning

confidence: 99%

“…Blockchain scalability involves maintaining essential functions as BC data volume grows without compromising BC characteristics. Inspired by previous work [21], which advocates for minimal redundancy, we explored the optimal redundancy level to maintain functional equivalence among nodes and preserve BC decentralization. We introduced the Blockchain Redundancy Factor (BRF), allowing users (or the system) to choose their desired redundancy level via a variable > REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE-CLICK HERE TO EDIT) < called the redundancy factor, 𝑟.…”

Section: A Data Redundancymentioning

confidence: 99%

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Toward a Dynamic Tasks Offloading and Resource Allocation for the Metaverse in In-network Computing

Aliyu,

Oh,

et al. 2023

2023 Fourteenth International Conference on Ubiquitous and Future Networks (ICUFN)

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The computing in the network (COIN) paradigm has emerged as a potential solution for computation-intensive applications like the metaverse by utilizing unused network resources. The blockchain (BC) guarantees task-offloading privacy, but cost reduction, queueing delays, and redundancy elimination remain open problems. This paper presents a redundancy-aware BC-based approach for the metaverse's partial computation offloading (PCO). Specifically, we formulate a joint BC redundancy factor (BRF) and PCO problem to minimize computation costs, maximize incentives, and meet delay and BC offloading constraints. We proved this problem is NP-hard and transformed it into two subproblems based on their temporal correlation: real-time PCO and Markov decision process-based BRF. We formulated the PCO problem as a multiuser game, proposed a decentralized algorithm for Nash equilibrium under any BC redundancy state, and designed a double deep Q-networkbased algorithm for the optimal BRF policy. The BRF strategy is updated periodically based on user computation demand and network status to assist the PCO algorithm. The experimental results suggest that the proposed approach outperforms existing schemes, resulting in a remarkable 47% reduction in cost overhead, delivering approximately 64% higher rewards, and achieving convergence in just a few training episodes.

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Section: A Related Workmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

Section: A Data Redundancymentioning

confidence: 99%

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Toward a Dynamic Tasks Offloading and Resource Allocation for the Metaverse in In-network Computing

Aliyu,

Oh,

et al. 2023

2023 Fourteenth International Conference on Ubiquitous and Future Networks (ICUFN)

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“…Blockchain-enabled IoT networks are becoming more popular, but they are having trouble scaling for greater numbers of devices and extensive data storage requirements [3]. The current state of blockchain presents some issues wherein a greater number of IoT nodes join the network, leading to degradation in performance and e ciency [11], [12]. Also, the network of nodes that agree on the state of the blockchain and verify transactions is complicated, and it cannot handle a large number of transactions at once in most of cases like Proof of Work (PoW), PoS [13], [14], [15], [5].…”

Section: Introductionmentioning

confidence: 99%

A Scalable Blockchain-based Framework for Efficient IoT Data Management Using Lightweight Consensus

Haque,

Shah,

Iqbal

et al. 2024

Preprint

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Recent research has focused on applying blockchain technology to solve security-related problems in Internet of Things (IoT) networks. However, the inherent scalability issues of blockchain technology become apparent in the presence of a vast number of IoT devices and the substantial data generated by these networks. Therefore, in this paper, we use a lightweight consensus algorithm to cater to these problems. We propose a scalable blockchain-based framework for managing IoT data, catering to a large number of devices. This framework utilizes the Delegated Proof of Stake (DPoS) consensus algorithm to ensure enhanced performance and efficiency in resource-constrained IoT networks. DPoS being a lightweight consensus algorithm leverages a selected number of elected delegates to validate and confirm transactions, thus mitigating the performance and efficiency degradation in the blockchain-based IoT networks. In this paper, we implemented an Interplanetary File System (IPFS) for distributed storage, and Docker to evaluate the network’s performance in terms of throughput, latency, and resource utilization. We divided our analysis into four parts: Latency, throughput, resource utilization, and file upload time and speed in distributed storage evaluation. Our empirical findings demonstrate that our framework exhibits low latency, measuring less than 0.976 milliseconds. The proposed technique outperforms Proof of Stake (PoS), representing a state-of-the-art consensus technique. We also demonstrate that the proposed approach is useful in IoT applications where low latency or resource efficiency is required.

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