Distributed Databases Research Articles

Modeling of the modern climatic range of <i>Cydalima perspectalis</i> (Lepidoptera, Crambidae) in Eurasia

Modeling of the modern climatic range of a dangerous plant pest of the genus Buxus L. box tree moth Cydalima perspectalis (Walker, 1859) is carried out in order to determine possible territories of its further expansion in Eurasia. Information on the loci of actual C. perspectalis detection both in native (East and South Asia) and invasive (Europe and West Asia) parts of the range was collected from various sources (species distribution databases and publications). Six bioclimatic (three temperature and three humidity) parameters are used as distribution predictors. Original methods for determining the number of pseudo-absence points and their selective generation are developed and applied. The final classification and partitioning of the space of bioclimatic factors is carried out using gradient boosting. The modern Eurasian climatic range of the box tree moth is calculated and mapped. It is shown that the invasion has not yet reached its limits and there are a number of territories in Eurasia where climatic conditions are favorable for the emergence of C. perspectalis populations both in the native part of the range (certain southern and eastern regions of China, the DPRK, the southern foothills of the Himalayas) and in its invasive part (Northern and Eastern Europe, Caucasus, and Turkey). A comparative assessment of the importance of different climatic factors in determining the box tree moth distribution area is given. It has been found out that the sum of the driest month precipitation is of greatest importance for constructing a model of the C. perspectalis climatic range (47.6%). A significant difference in climatic conditions between the native and invasive parts of the range is revealed and assumptions about the possible causes of its occurrence are made.

An Updated Review on the Spatial Distribution of Borrelia burgdorferi Sensu Lato Across Ticks, Animals and Humans in Northeastern China and Adjacent Regions.

Lyme disease is a tick-borne zoonotic disease caused by Borrelia burgdorferi sensu lato and is prevalent in northeastern Asia, particularly in the forested area of Northeastern China. However, a lack of systematic data on the spatial distribution of B. burgdorferi in this region hinders the prediction of its transmission risk across the landscape. To provide an updated overview and establish a comprehensive spatial distribution database, we conducted a systematic review of literature published between 2000 and 2022. We collected and compiled relevant data on B. burgdorferi in Northeastern China and its neighbouring regions, outlining its distribution in ticks, wild animals, livestock and humans. Spatial analysis was performed to identify spatial clusters of tick positivity and host infection rates. From a total of 1823 literature, we selected 110 references to compile 626 detection records of B. burgdorferi, including 288 in ticks, 109 in wildlife, 111 in livestock and domestic animals and 100 in humans. The average detection rate of B. burgdorferi in ticks was approximately 20%, with wildlife, livestock and domestic animal host positivity rates below 50% and human seroprevalence rates varying from 0.94% to 44.18%. The study identified the presence of 17 tick species and ten genotypes of B. burgdorferi in the region, indicating a broad distribution. Notably, B. burgdorferi exhibited notable clustering, particularly in the central and eastern areas of Jilin Province, warranting further investigation.

'TLDetect': AI-Based Application for Detection and Correction of Anomalous TLD Glow Curves.

This research reviews a novel artificial intelligence (AI)-based application called TLDetect, which filters and classifies anomalous glow curves (GCs) of thermoluminescent dosimeters (TLDs). Until recently, GC review and correction in the lab were performed using an old in-house software, which uses the Microsoft Access database and allows the laboratory technician to manually review and correct almost all GCs without any filtering. The newly developed application TLDetect uses a modern SQL database and filters out only the necessary GCs for technician review. TLDetect first uses an artificial neural network (ANN) model to filter out all regular GCs. Afterwards, it automatically classifies the rest of the GCs into five different anomaly classes. These five classes are defined by the typical patterns of GCs, i.e., high noise at either low or high temperature channels, untypical GC width (either wide or narrow), shifted GCs whether to the low or to the high temperatures, spikes, and a last class that contains all other unclassified anomalies. By this automatic filtering and classification, the algorithm substantially reduces the amount of the technician's time spent reviewing the GCs and makes the external dosimetry laboratory dose assessment process more repeatable, more accurate, and faster. Moreover, a database of the class anomalies distribution over time of GCs is saved along with all their relevant statistics, which can later assist with preliminary diagnosis of TLD reader hardware issues.

The potential of federated learning for self-configuring medical object detection in heterogeneous data distributions

Medical Object Detection (MOD) is a clinically relevant image processing method that locates structures of interest in radiological image data at object-level using bounding boxes. High-performing MOD models necessitate large datasets accurately reflecting the feature distribution of the corresponding problem domain. However, strict privacy regulations protecting patient data often hinder data consolidation, negatively affecting the performance and generalization of MOD models. Federated Learning (FL) offers a solution by enabling model training while the data remain at its original source institution. While existing FL solutions for medical image classification and segmentation demonstrate promising performance, FL for MOD remains largely unexplored. Motivated by this lack of technical solutions, we present an open-source, self-configuring and task-agnostic federated MOD framework. It integrates the FL framework Flower with nnDetection, a state-of-the-art MOD framework and provides several FL aggregation strategies. Furthermore, we evaluate model performance by creating simulated Independent Identically Distributed (IID) and non-IID scenarios, utilizing the publicly available datasets. Additionally, a detailed analysis of the distributions and characteristics of these datasets offers insights into how they can impact performance. Our framework’s implementation demonstrates the feasibility of federated self-configuring MOD in non-IID scenarios and facilitates the development of MOD models trained on large distributed databases.

Two-phase flow evolution and interfacial area transport downstream of the mixing-vane spacer grid in rod bundle channels

This study investigated the axial two-phase flow evolution and interfacial area transport characteristics downstream of the mixing vane spacer grid (MVSG) in a tight lattice rod bundle channel with a subchannel hydraulic diameter of Dh = 17.27 mm. The experiment was conducted under the air-water two-phase flow at room temperature and atmospheric pressure. The phase distributions of 6 positions downstream of MVSG (Z/Dh = 9.15, 14.94, 20.73, 26.52, 32.31, and 61.26) were measured utilizing a self-developed double-layer wire-mesh sensor (WMS). 42 cases were obtained, involving the bubbly flow, cap-bubbly flow, and slug flow. Void fraction, bubble velocity, interfacial area concentration (IAC), and bubble size distribution (BSD) databases were built. Under the group-1 (G-1) flow, due to the swirling flow generated by MVSG, the G-1 bubbles were drawn from the bundle surface and the gap region into the subchannel center to form a spindle core-peak distribution. BSD results demonstrate that the swirling flow promotes bubbles’ coalescence. The one-dimensional (1-D) void fraction and IAC downstream of the MVSG decrease first and then recover, which contrasts with the non-mixing vane spacer grid (N-MVSG) effect. It was attributed to the increment of the bubbles’ velocity after MVSG due to the bubbles’ migration to the channel center and coalescence. Under the group-2 (G-2) flow, MVSG intensifies the core peak distribution of void fraction, and the void fraction profile is also spindle-shaped. The obvious bubbles’ break-up occurs at Z/Dh = 14.94 attributed to the swirling decay. The 1-D void fraction and IAC transport indicate, that with the liquid-velocity increment, the MVSG effect is different from the N-MVSG effect, which is mainly achieved by affecting the G-1 bubbles’ dynamic behaviors. The model evaluation utilizing the interfacial area transport equation (IATE) closing bubble interaction source/sink terms indicates that the advection effect dominates the IAC evolution and the contribution of the pressure effect is weak. The remarkable bubbles’ coalescence occurs under the high liquid velocity. In future studies, the additional bubble break-up and coalescence source/sink term model considering the MVSG effect should be developed based on these cases to improve the IATE prediction ability.

Homogenization of fish assemblages in an endemic biodiversity hot spot: Evidence from 70‐year data from the Yun‐Gui Plateau, China

Abstract The combined impacts of anthropogenic activities and global climate changes threaten native fish communities. Such threats are particularly critical in biodiversity hot spot areas with many endemic species, as exemplified by the isolated Yun‐Gui Plateau, China. The risk of biotic homogenization is also expected to be high in such areas, but this risk and its underlying drivers remain poorly studied. Here, we established a database of fish distributions and used environmental filtering for spatial clusters (n = 3759) in river basins located at Yun‐Gui Plateau. This database was based on public data sources and a comprehensive review of relevant literature (n = 481) covering the period from 1950 to 2021. We divided the plateau into nine subbasins for spatial comparison and into two periods for temporal comparison—before and after the start of rapid economic growth development in 1978. We found: (a) a marginal increase in replacement (species exchange in different subbasins) and similarity index but a decrease in richness difference index, indicating homogenization and the loss of natural barriers across the entire plateau; (b) an increase in species richness accompanied by a discernible decline in phylogenetic diversity, also indicating homogenization; (c) the increase in species richness by species loss‐gain analyses was associated with climatic variables (temperature and precipitation), elevation and human activities in all subbasins. Temporal and spatial comparisons indicated that homogenization of fish assemblages in the Yun‐Gui Plateau was exacerbated by climatic and anthropogenic pressures. The observed homogenization of fish species is of great concern, and we call for systematic assessments and precautionary mitigation actions to maintain the uniqueness of biodiversity in the Yun‐Gui Plateau region.

Capability of big data to capture threatened vertebrate diversity in protected areas.

Protected areas (PAs) are an essential tool for conservation amid the global biodiversity crisis. Optimizing PAs to represent species at risk of extinction is crucial. Vertebrate representation in PAs is assessed using species distribution databases from the International Union for Conservation of Nature (IUCN) and the Global Biodiversity Information Facility (GBIF). Evaluating and addressing discrepancies and biases in these data sources are vital for effective conservation strategies. Our objective was to gain insights into the potential constraints (e.g., differences and biases) of these global repositories to objectively depict the diversity of threatened vertebrates in the global system of PAs. We assessed differences in species richness (SR) of threatened vertebrates as reported by IUCN and GBIF in PAs globally and then compared how biased this information was with reports from independent sources for a subset of PAs. Both databases showed substantial differences in SR in PAs (t=-62.35, p≤0.001), but differences varied among regions and vertebrate groups. When these results were compared with data from independent assessments, IUCN overestimated SR by 575% on average and GBIF underestimated SR by 63% on average, again with variable results among regions and groups. Our results indicate the need to improve analyses of the representativeness of threatened vertebrates in PAs such that robust and unbiased assessments of PA effectiveness can be conducted. The scientific community and decision makers should consider these regional and taxonomic disparities when using IUCN and GBIF distributional data sources in PA assessment. Overall, supplementing information in these databases could lead to more robust and reliable analyses. Additional efforts to acquire more comprehensive and unbiased data on species distributions to support conservation decisions are clearly needed.

Predicting undetected native vascular plant diversity at a global scale

Vascular plants are diverse and a major component of terrestrial ecosystems, yet their geographic distributions remain incomplete. Here, I present a global database of vascular plant distributions by integrating species distribution models calibrated to species' dispersal ability and natural habitats to predict native range maps for 201,681 vascular plant species into unsurveyed areas. Using these maps, I uncover unique patterns of native vascular plant diversity, endemism, and phylogenetic diversity revealing hotspots in underdocumented biodiversity-rich regions. These hotspots, based on detailed species-level maps, show a pronounced latitudinal gradient, strongly supporting the theory of increasing diversity toward the equator. I trained random forest models to extrapolate diversity patterns under unbiased global sampling and identify overlaps with modeled estimations but unveiled cryptic hotspots that were not captured by modeled estimations. Only 29% to 36% of extrapolated plant hotspots are inside protected areas, leaving more than 60% outside and vulnerable. However, the unprotected hotspots harbor species with unique attributes that make them good candidates for conservation prioritization.

Native Distributed Databases: Problems, Challenges and Opportunities

Native distributed databases, crucial for scalable applications, offer transactional and analytical prowess but face data intricacies and network challenges. Under the CAP theorem's constraints, latency and replication issues necessitate creative approaches to maintenance, security, and upgrades. Progress in consistency algorithms, network technology, automation, and machine learning for optimization presents significant potential. Embracing hybrid transactional/analytical processing (HTAP), these databases represent an evolutionary leap in data management, aiming to reconcile performance with the complexities inherent in distributed environments. OceanBase is introduced as a case study, and its strong TPC-C and TPC-H benchmark performances underscore Ocean-Base as a top-tier distributed database. We also discuss possible opportunities for native distributed databases.

Galaxybase: A High Performance Native Distributed Graph Database for HTAP

We introduce Galaxybase, a native distributed graph database that addresses the increasing demands for processing large volumes of graph data in diverse industries like finance, manufacturing, and government. Designed to handle the requirements of both transactional and analytical workloads, Galaxybase stands out with its novel data storage and transaction mechanisms. At its core, Galaxybase utilizes a Log-Structured Adjacency List coupled with an Edge Page structure, optimizing read-write operations across a spectrum of tasks such as graph traversals and single edge queries. A notable aspect of Galaxybase is its execution of custom distributed transaction modes tailored for HTAP transactions, allowing for the facilitation of bidirectional and interactive transactions. It ensures data integrity and minimal latency while enabling simultaneous processing of OLTP and OLAP workloads without blocking. Experimental results show that Galaxybase achieves high throughput and low latency in both OLTP and OLAP workloads, across various graph query scenarios and resource conditions. Galaxybase has been deployed in leading banks, education, telecommunication and energy sectors in China, consistently maintaining robust performance for HTAP workloads over the years.

Cymbella stomachsis sp. nov. (Bacillariophyta; Cymbellaceae), a new species from Guangdong Province, China

A new diatom species from the genus Cymbella C. Agardh is described from samples collected during a survey of freshwater diatoms from Modaomen Channel, Guangdong Province, China. With the aid of light microscopy (LM) and scanning electron microscopy (SEM), we give a detailed morphological description of Cymbella stomachsis sp. nov., which is placed in the genus Cymbella according to the shared features of asymmetry about the apical axis, deflection of the external terminal raphe fissures towards the dorsal margin, and the presence of apical pore fields and stigmata. Although it shares some similar structural features with C. tumida, C. stuxbergii, C. stuxbergioides, C. pesudostuxbergii, C. mexicana and C. australica, C. stomachsis sp. nov. is easily distinguished by the valve outline, the shape and density of areola, and the appearance of a lack of an intermissio at the central nodule. Especially, the structure of the intermissio is absent in similar taxa. This work is important for its enrichment of our understanding of the genus Cymbella and improvement of the database of diatom biodiversity and distribution in China.

Gleditsiae Sinensis Fructus ingredients and mechanism in anti-asthmatic bronchitis research

BackgroundGleditsiae Sinensis Fructus (GSF) is commonly used in traditional medicine to treat respiratory diseases such as bronchial asthma. However, there is a lack of research on the chemical composition of GSF and the pharmacological substance and mechanism of action for GSF in treating bronchial asthma. PurposeThe chemical constituents of GSF were analyzed using ultrahigh-performance liquid chromatography-quadrupole-Orbitrap high-resolution mass spectrometry (UHPLC-Q-Orbitrap HRMS). In this study, we combined network pharmacology, molecular docking techniques, and experimental validation to explore the therapeutic efficacy and underlying mechanism of GSF in the treatment of bronchial asthma. MethodsCharacterization of the chemical constituents of GSF was conducted using UHPLC-Q-Orbitrap HRMS. The identified chemical components were subjected to screening for active ingredients in the Swiss Absorption, Distribution, Metabolism, and Excretion (ADME) database. Relevant databases were utilized to retrieve target proteins for the active ingredients and targets associated with bronchial asthma disease, and the common targets between the two were selected. Subsequently, the protein-protein interaction (PPI) network was constructed using the String database and Cytoscape software to identify key targets. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using the Metascape database. The "component-common target" network was constructed using Cytoscape to identify the primary active ingredients. Molecular docking validation was conducted using AutoDock software. The bronchial asthma mouse model was established using ovalbumin (OVA), and the lung organ index of the mice was measured. Lung tissue pathological changes were observed using hematoxylin and eosin (HE), Periodic Acid-Schiff (PAS), and Masson staining. The respiratory resistance (Penh) of the mice was assessed using a pulmonary function test instrument. An enzyme-linked immunosorbent assay (ELISA) was used to determine the levels of IgE, IL-4, IL-5, and IL-13 in the mouse serum. Immunofluorescence staining was performed to detect the protein expression levels of AKT and PI3K in the lung tissues. An in vitro experiment was performed to observe the effects of echinocystic acid (EA) on IL-4 stimulated Human ASMCs (hASMCs). Cell viability was measured using a CCK-8 assay to calculate the IC50 value of the EA. A wound healing test was conducted to observe the effect of EA on degree of healing. RT-qPCR was performed to detect the influence of EA on the mRNA expression levels of ALB, SRC, TNF-α, AKT1, and IL6 in the cells. ResultsA total of 95 chemical constituents were identified from the GSF. Of these, 37 were identified as active ingredients. There were 169 overlapping targets between the active ingredients and the disease targets. A topological analysis of the protein-protein interaction (PPI) network identified the core targets as IL6, TNF, ALB, AKT1, and SRC. An enrichment analysis revealed that the treatment of bronchial asthma with GSF primarily involved the AGE-RAGE signaling pathway and the PI3K-Akt signaling pathway, among others. The primary active ingredients included 13(s)-HOTRE, linolenic acid, and acacetin. The molecular docking results demonstrated a favorable binding activity between the critical components of GSF and the core targets. Animal experimental studies indicated that GSF effectively improved symptoms, lung function, and lung tissue pathological changes in the OVA-induced asthmatic mice, while alleviating inflammatory responses. GSF decreased the fluorescent intensity of the AKT and PI3K proteins. The IC50 value of EA was 30.02μg/ml. EA (30) significantly promoted the proliferation of IL4-stimulated hASMCs cells. EA (30) significantly increased the expression of ALB and SRC mRNA and decreased the expressions of TNF-α, AKT, and IL6 mRNA. ConclusionThe multiple active ingredients found in GSF exerted their anti-inflammatory effects through multiple targets and pathways. This preliminary study revealed the core target and the mechanism of action underlying its treatment of bronchial asthma. These findings provided valuable insights for further research on the pharmacological substances and quality control of GSF.

Optimization of air distribution and coal blending in pulverized coal boilers for high-temperature corrosion prevention based on POD reduced-order modeling

High-temperature corrosion in coal-fired boilers poses a significant threat to safe operation. However, there is currently a lack of effective online monitoring and optimization methods for high-temperature corrosion. Therefore, this study proposes a novel approach to rapidly predict the distribution of chemical species and evaluate high-temperature corrosion degrees, along with optimizing operating conditions to prevent high-temperature corrosion. Firstly, a total of 564 Computational Fluid Dynamics (CFD) simulations are performed on a 330 MW tangentially fired boiler, covering various operating conditions, including coal blending, air distribution, boiler load, etc., to obtain a database of O2, CO, and H2S distributions within the boiler. Then a method based on Proper Orthogonal Decomposition (POD) and Support Vector Regression (SVR) is used to process the database to realize real-time prediction of boiler chemical species distribution, which is next utilized as inputs of a high-temperature corrosion evaluation to obtain in-situ corrosion degree distribution. Finally, Particle Swarm Optimization (PSO) is used to optimize coal blending and air distribution schemes, effectively reducing severe corrosion ratio from 36.34 % to 10.04 % in a typical case by improving the atmosphere near the water walls. This study thus provides a new perspective for online boiler diagnostics and digital twin construction, particularly by achieving online monitoring of high-temperature corrosion and optimizing operating conditions to prevent corrosion.

CaaS-LSM: Compaction-as-a-Service for LSM-based Key-Value Stores in Storage Disaggregated Infrastructure

Optimizing LSM-based Key-Value Stores (LSM-KVS) for disaggregated storage is essential to achieve better resource utilization, performance, and flexibility. Most of the existing studies focus on offloading the compaction to the storage nodes to mitigate the performance penalties caused by heavy network traffic between computing and storage. However, several critical issues are not addressed including the strong dependency between offloaded compaction and LSM-KVS, resource load-balancing, compaction scheduling, and complex transient errors. To address the aforementioned issues and limitations, in this paper, we propose CaaS-LSM, a novel disaggregated LSM-KVS with a new idea of Compaction-as-a-Service. CaaS-LSM brings three key contributions. First, CaaS-LSM decouples the compaction from LSM-KVS and achieves stateless execution to ensure high flexibility and avoid coordination overhead with LSM-KVS. Second, CaaS-LSM introduces a performance- and resource-optimized control plane to guarantee better performance and resource utilization via an adaptive run-time scheduling and management strategy. Third, CaaS-LSM addresses different levels of transient and execution errors via sophisticated error-handling logic. We implement the prototype of CaaS-LSM based on RocksDB and evaluate it with different LSM-based distributed databases (Kvrocks and Nebula). In the storage disaggregated setup, CaaS-LSM achieves up to 8X throughput improvement and reduces the P99 latency up to 98% compared with the conventional LSM-KVS, and up to 61% of improvement compared with state-of-the-art LSM-KVS optimized for disaggregated storage.

Alien plant species distribution in Romania: a nationwide survey following the implementation of the EU Regulation on Invasive Alien Species.

Biological invasions pose an increasing risk to nature, social security and the economy, being ranked amongst the top five threats to biodiversity. Managing alien and invasive species is a priority for the European Union, as outlined in the EU Biodiversity Strategy for 2030 and the Kunming-Montreal Global Biodiversity Framework. Alien plant species are acknowledged to impact the economy and biodiversity; thus, analysing the distribution of such species provides valuable inputs for the management and decision-making processes. The database presented in the current study is the first consolidated checklist of alien plant species that are present in Romania, both of European Union concern and of national interest. This database complements a prior published distribution, based only on records from literature, bringing new information regarding the occurrence of alien plants in Romania, as revealed by a nationwide field survey. We consider this database a valuable instrument for managing biological invasions at both national and regional levels, as it can be utilised in further research studies and in drafting management and action plans, assisting stakeholders in making informed decisions and implementing management actions. We present the results of the first nationwide survey of alien plant species in Romania, conducted between 2019 and 2022, in the framework of a national project coordinated by the Ministry of Environment, Waters and Forests and the University of Bucharest. The present database complements and updates the database published by Sirbu et. al (2022), which included occurrence records published until 2019. The new database includes 98323 occurrence records for 396 alien plant species in 77 families, with most species belonging to the Asteraceae family. One alien plant species in our database, the black locust Robiniapseudoacacia L., had more than 10,000 occurrence records. The distribution database also includes information on newly-reported invasive alien plant species of European Union concern in Romania (i.e. the floating primrose-willow Ludwigiapeploides (Kunth) P.H.Raven) and documents the presence of plants in 44 additional families compared to Sirbu et al. (2022). Each entry includes information on species taxonomy, location, year, person who recorded and identified the alien plant, geographical coordinates and taxon rank.

Column generation-based algorithm for fragment allocation: minimizing query splitting in distributed databases

Column generation-based algorithm for fragment allocation: minimizing query splitting in distributed databases

QOTUM: The Query Optimizer for Distributed Database in Cloud Environment

Abstract: In distributed and cloud database system, large amount of resources are used by complex queries that increase in payment overheads of cloud users. These resource needs can be minimised by evaluating common join operations and caching their results so that they could be applied to the execution of additional queries. In this research, the Query Optimization Technique Using Materialization (QOTUM) system is designed and developed that uses the mechanism of materialized views during query execution at distributed database in cloud environment. Extensive experiments are conducted with the help of standard benchmarks datasets and query workloads in cloud environment. The performance of QOTUM system is studied and evaluated against conventional SQL system based on various performance parameters.

Uncovering Complexities in Horizontal Inequality: A Novel Decomposition of the Gini Index

This study introduces an innovative tool to analyse how various inequality factors, including geography, race, and gender, contribute to overall inequality. Traditional approaches typically partition populations into groups based on a single factor and assess inequality by additively decomposing an inequality measure into within- and between-group components. After discussing the theoretical impossibility of additively decomposing the Gini index into within- and between-group components, in fact, we propose a Gini decomposition into two highly informative within- and between-components, with substantial improvement upon the usual assessment of horizontal inequality. This method represents a significant advancement over the traditional horizontal inequality assessment, which only compares group means and overlooks the complexities of differences between groups. Our approach accurately captures the nuances of group disparities, offering a robust measure of horizontal inequality. Through rigorous simulations and empirical analysis of the OECD Income Distribution Database, we validate the effectiveness of our method in evaluating and understanding inequality. This work enriches the toolkit available to researchers in the field by offering a framework for selecting the most suitable measure of horizontal inequality, along with the code for implementing the proposed decomposition.

Dodo: A scalable optimistic deterministic concurrency control protocol

Deterministic concurrency control is able to avoid the expensive two-phase commit in distributed databases and can solve the single-thread bottleneck of transaction processing in blockchain systems. Most existing deterministic concurrency control protocols rely on prior knowledge of the transaction’s read-write set, which is impractical in most cases. The state-of-the-art deterministic concurrency control protocols Aria and DOCC break this limitation. However, they do not perform well in multi-node and multi-core scalability.To solve the scalability issues, we propose Dodo, a novel deterministic concurrency control protocol. Dodo processes transactions in multi-batches, and each batch is divided into three phases. In the first phase, transactions are executed as read committed and staged. In the second phase, transactions are validated for the read-write conflict. In the third phase, only the continuous un-conflicted transactions at the head of the batch can be committed. For the aborted transaction that will be rerun in the next batch, we utilize its write set in the previous execution to reduce the read-write conflicts in the next batch. In this way, Dodo has the following benefits. First, Dodo does not rely on the prior knowledge of the read-write set. Second, Dodo commits transactions in pre-determined orders (TIDs) providing high multi-node scalability. Third, Dodo runs transactions in each phase concurrently, and the aborted transactions are re-executed in a conflict-less manner, enabling high multi-core scalability. Besides, we propose two optimism-based improvements, lazy decision and early-write visibility, to reduce aborts and blocking. Our evaluation shows that Dodo outperforms Aria and DOCC by up to 16.5x and 8.0x in a single-node setting and scales well in a multi-node setting.

Energy consumption estimation and profiling for queries in distributed database systems based on a bottom-up comprehensive energy model

Quantifying energy consumption of database operations is the foundation of building energy-efficient database systems. Existing approaches only focused on stand-alone database servers, while we are interested in modeling energy consumption of database operations in the distributed environments. In this study, we aim at providing an accurate energy consumption model for queries executed in distributed database systems, profiling energy consumption characteristics of both the individual queries and the system as a whole, to guide the design of green database systems and to reveal opportunities for energy-efficient computing. As the execution of a distributed query is a combination of a set of subqueries that decomposed from it, we start from building energy models for individual subqueries by extracting basic operations that can effectively reflect their energy consumption. Then we use a bottom-up measuring and modeling method as the basis to provide a comprehensive energy estimation model for the entire distributed query. To validate the accuracy of the model, we use queries with a variety complexity that generated from three standard benchmarks (TPC-H, SSB, and Sysbench) on real distributed databases. Extensive experimental results show that our solution can achieve a high average accuracy of 94.63% for distributed queries. More importantly, based on these results we further explored energy consumption patterns for distributed queries and presented several important implications. And finally, a significant role of distributed joins has been discovered in saving both idle and dynamic energy cost of the system. We hope that taking advantage of our observations can help readers who wish to substantially improve energy efficiency for distributed database systems.