CLeveldb: Coalesced Leveldb for Small Data

As a large number of mobile terminals are connected to the IoT, the security problem of IoT is a challenge to the IoT technology. Blockchain technology has the characteristics of decentralization, data encryption, smart contract, and so on, especially suitable in the complex heterogeneous network. However, sequential access based on block files in the blockchain hinders efficient query processing. The problem is due to current blockchain solutions do not support temporal data processing. In this paper, we propose two index building methods (TISD and TIF) to address this issue in Hyperledger Fabric System. TISD (temporal index based on state databases) segments the historical data by time interval in the time dimension and indexes events at the same time interval. TIF (temporal index based on files) builds the index of files by the block transaction data, which is arranged in chronological order and is stored at a certain time interval. In the experimental part, we compare the query time on two datasets and analyse the query performance. Experiments demonstrated that our two methods are relatively stable in overall time performance on different datasets in the Hyperledger Fabric System.

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“…In Fabric, there are four kinds of nodes participating in the network: Peer node, Orderer node, Certificate Authority node, and Client node [32].…”

Section: Hyperledger Fabricmentioning

confidence: 99%

Temporal Index Scheme of Hyperledger Fabric System in IoT

Lü

Liu

Wang

et al. 2021

Wireless Communications and Mobile Computing

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“…In-Memory Data Scheduler Key-value (KV) stores are suitable for latency-sensitive internet services, and they have been widely used in large-scale data-intensive internet applications [17]. High demand from users has increased the need for fast read/write performance in accessing databases [18][19][20].…”

Section: Edge-based Data Scheduling For Fcn-lstm Driver Profilingmentioning

confidence: 99%

“…High demand from users has increased the need for fast read/write performance in accessing databases [18][19][20]. Sensor-based key-value data consist of many small files, which create a latency bottleneck from low I/O performance in the system [17,21].…”

Section: Edge-based Data Scheduling For Fcn-lstm Driver Profilingmentioning

confidence: 99%

A Distributed Edge-Based Scheduling Technique with Low-Latency and High-Bandwidth for Existing Driver Profiling Algorithms

2021

Self Cite

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The gradual increase in latency-sensitive, real-time applications for embedded systems encourages users to share sensor data simultaneously. Streamed sensor data have deficient performance. In this paper, we propose a new edge-based scheduling method with high-bandwidth for decreasing driver-profiling latency. The proposed multi-level memory scheduling method places data in a key-value storage, flushes sensor data when the edge memory is full, and reduces the number of I/O operations, network latency, and the number of REST API calls in the edge cloud. As a result, the proposed method provides significant read/write performance enhancement for real-time embedded systems. In fact, the proposed application improves the number of requests per second by 3.5, 5, and 4 times, respectively, compared with existing light-weight FCN-LSTM, FCN-LSTM, and DeepConvRNN Attention solutions. The proposed application also improves the bandwidth by 5.89, 5.58, and 4.16 times respectively, compared with existing light-weight FCN-LSTM, FCN-LSTM, and DeepConvRNN Attention solutions.

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“…This problem considerably lowers the performance of embedded boards that lack connectivity with external hard drives. Several studies have been conducted to improve the performance of LevelDB [2], [8], [9], [11]. For example, WiscKey [19] separates the keys from their values and implements the LSM-tree only on the keys.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, DVC continuously checks for data duplication on values and performs unscheduled compaction (if necessary) to avoid space amplification. Using a memory component coalescing buffer (CBuffer) [2], CaseDB avoids small writes and guarantees a low percentage of metadata share for all workloads. By introducing CaseDB, we claim four major contributions as mentioned below:…”

Section: Introductionmentioning

confidence: 99%

CaseDB: Lightweight Key-Value Store for Edge Computing Environment

Tulkinbekov

Kim

2020

IEEE Access

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Key-value stores based on a log-structured merge (LSM) tree have emerged in big data systems because of their scalability and reliability. An LSM-tree offers a multilevel data structure with a simple interface. However, it performs file rewrites at the disk level, which causes write amplification. This study is concerned with this problem in relation to an embedded board environment, which can be used in edge computing. Addressing the major problems associated with an LSM-tree, we propose a new key-value store named CaseDB, which aggressively separates keys and bloom filters on the non-volatile memory express (NVMe) drive and stores the values on the SSD. Our solution reduces the I/O cost and enhances the overall performance in a cost-efficient manner. CaseDB employs a memory component, CBuffer, to avoid small write operations, and a delayed value compaction technique that guarantees the sorted order for both keys and values. CaseDB also utilizes deduction-based data deduplication to prevent space amplification in the values layer. The experiments show that CaseDB outperforms LevelDB and WiscKey 5.7 and 1.8 times, respectively, with respect to data writes, and additionally improves the read performance by 1.5 times. CaseDB also avoids the space amplification of WiscKey.

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CLeveldb: Coalesced Leveldb for Small Data

Cited by 5 publications

References 5 publications

Temporal Index Scheme of Hyperledger Fabric System in IoT

Temporal Index Scheme of Hyperledger Fabric System in IoT

A Distributed Edge-Based Scheduling Technique with Low-Latency and High-Bandwidth for Existing Driver Profiling Algorithms

CaseDB: Lightweight Key-Value Store for Edge Computing Environment

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