GPU-aided edge computing for processing the k nearest-neighbor query on SSD-resident data

Velentzas, Polychronis; Vassilakopoulos, Michael; Corral, Antonio

doi:10.1016/j.iot.2021.100428

Cited by 6 publications

(4 citation statements)

References 39 publications

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“…FL avoids uploading training data to the central cloud, allowing ECSs to locally train a shared global model using their own data. It has been applied in various fields [28,29]. For instance, Gholizadeh and Musilek [28] predicted individual and aggregate electrical loads based on FL.…”

Section: Federated Learningmentioning

confidence: 99%

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FedDeep: A Federated Deep Learning Network for Edge Assisted Multi-Urban PM2.5 Forecasting

Hu,

Cao,

Guo

et al. 2024

Applied Sciences

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Accurate urban PM2.5 forecasting serves a crucial function in air pollution warning and human health monitoring. Recently, deep learning techniques have been widely employed for urban PM2.5 forecasting. Unfortunately, two problems exist: (1) Most techniques are focused on training and prediction on a central cloud. As the number of monitoring sites grows and the data explodes, handling a large amount of data on the central cloud can cause tremendous computational pressures and increase the risk of data leakages. (2) Existing methods lack an adaptive layer to capture the varying impacts of different external factors (e.g., weather conditions, temperature, and wind speed). In this paper, a federated deep learning network (FedDeep) is developed for edge-assisted multi-urban PM2.5 forecasting. First, we assign each urban region to an edge cloud server (ECS). An external spatio-temporal network (ESTNet) is then deployed on each ECS. Data from different urban regions are uploaded to the corresponding ECS for training, which avoids processing all the data on the central cloud and effectively alleviates computational pressure and data leakage issues. Second, in ESTNet, we develop a gating fusion layer to adaptively fuse external factors to improve prediction accuracy. Finally, we adopted PM2.5 data collected from air quality monitoring sites in 13 prefecture-level cities, Jiangsu Province for validation. The experimental results proved that FedDeep outperformed the advanced baselines in terms of prediction accuracy and model efficiency.

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Section: Federated Learningmentioning

confidence: 99%

“…It has been applied in various fields [28,29]. For instance, Gholizadeh and Musilek [28] predicted individual and aggregate electrical loads based on FL. In air pollution prediction, FL is feasible because multiple monitoring sensors enable knowledge sharing, leading to improved prediction accuracy.…”

Section: Federated Learningmentioning

confidence: 99%

FedDeep: A Federated Deep Learning Network for Edge Assisted Multi-Urban PM2.5 Forecasting

Hu,

Cao,

Guo

et al. 2024

Applied Sciences

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“…However, these studies focus on secure means of preserving the integrity and privacy of query results against attacks on storage nodes. Recent studies consider energy consumption [26] and query latency [8,24,49,51] in edge-aided spatial queries. Li et al [26] study aggregated multi-attribute queries based on an energy-aware IR-tree.…”

Section: Related Workmentioning

confidence: 99%

“…Lai et al [24] compute probabilistic top-k dominating queries using local k-skybands maintained at edges. Velentzas et al [49] utilize edge nodes with GPUs and SSDs to speed up kNN queries. However, these studies consider neither data fraction estimation nor data sketching for latency optimization.…”

Section: Related Workmentioning

confidence: 99%

Efficient and error-bounded spatiotemporal quantile monitoring in edge computing environments

Tang

et al. 2022

Proc. VLDB Endow.

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Underlying many types of data analytics, a spatiotemporal quantile monitoring (SQM) query continuously returns the quantiles of a dataset observed in a spatiotemporal range. In this paper, we study SQM in an Internet of Things (IoT) based edge computing environment, where concurrent SQM queries share the same infrastructure asynchronously. To minimize query latency while providing result accuracy guarantees, we design a processing framework that virtualizes edge-resident data sketches for quantile computing. In the framework, a coordinator edge node manages edge sketches and synchronizes edge sketch processing and query executions. The co-ordinator also controls the processed data fractions of edge sketches, which helps to achieve the optimal latency with error-bounded results for each single query. To support concurrent queries, we employ a grid to decompose queries into subqueries and process them efficiently using shared edge sketches. We also devise a relaxation algorithm to converge to optimal latencies for those subqueries whose result errors are still bounded. We evaluate our proposals using two high-speed streaming datasets in a simulated IoT setting with edge nodes. The results show that our proposals achieve efficient, scalable, and error-bounded SQM.

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GPU-Based Algorithms for Processing the k Nearest-Neighbor Query on Spatial Data Using Partitioning and Concurrent Kernel Execution

Velentzas

Vassilakopoulos

Corral

et al. 2023

Int J Parallel Prog

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Algorithms for answering the k nearest-neighbor (k-NN) query are widely used for queries in spatial databases and for distance classification of a group of query points against a reference dataset to derive the dominating feature class. GPU devices have significantly more processing cores than CPUs and faster device memory than the main memory accessed by CPUs, thus, providing higher computing power for processing demanding queries like the k-NN. However, since device and/or main memory may not be able to host an entire, rather big, reference and query datasets, storing these datasets in a fast secondary device, like a solid state disk (SSD), and partially retrieve the required, at each stage, partitions is, in many practical cases, a feasible solution. We propose and implement the first GPU-based algorithms for processing the k-NN query for big reference and query spatial data stored on SSDs. Based on 3d synthetic and real big spatial data, we experimentally compare these algorithms and highlight the most efficient algorithmic variation. This variation utilizes a CUDA feature known as Concurrent Kernel Execution, to further improve its performance.

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GPU-aided edge computing for processing the k nearest-neighbor query on SSD-resident data

Cited by 6 publications

References 39 publications

FedDeep: A Federated Deep Learning Network for Edge Assisted Multi-Urban PM2.5 Forecasting

FedDeep: A Federated Deep Learning Network for Edge Assisted Multi-Urban PM2.5 Forecasting

Efficient and error-bounded spatiotemporal quantile monitoring in edge computing environments

GPU-Based Algorithms for Processing the k Nearest-Neighbor Query on Spatial Data Using Partitioning and Concurrent Kernel Execution

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