The increasing demand for energy-efficient solutions in IoT devices and edge computing calls for novel methodologies to generate accurate power models for diverse devices, enabling sustainable growth and optimized per...
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ISBN:
(纸本)9783031506833;9783031506840
The increasing demand for energy-efficient solutions in IoT devices and edge computing calls for novel methodologies to generate accurate power models for diverse devices, enabling sustainable growth and optimized performance. This paper presents a methodology for creating power models for edge devices and their embedded components. The proposed methodology collects power and resource utilization measurements from the edge device and generates both additive and regression models. The methodology is evaluated on a Raspberry Pi 4 device using a smart plug for power monitoring and various benchmarking tools for CPU and network sub-components. The evaluation shows that the generated models achieve low error, demonstrating the effectiveness of the proposed approach. Our methodology can be applied to any edge device, providing insights into the most efficient power consumption model. The heterogeneity of edge devices poses a challenge to creating a global power model, and our approach provides a solution for developing device-specific power models. Our results indicate that the generated models for Raspberry Pi 4 scored a maximum of 8% MAPE.
The paper proposed an approach to building a scalable service for spatiotemporal data storage to be used in applications that require searching for localized data and possibility of scaling up the storage resources. T...
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ISBN:
(纸本)9798350367607;9798350367591
The paper proposed an approach to building a scalable service for spatiotemporal data storage to be used in applications that require searching for localized data and possibility of scaling up the storage resources. The motivation for proposing this approach was enabling the localized data being overlayed over the map, while transferring only required data and minimize the latency time. The paper describes the architecture combining the R-star tree index and Log Structured Merge (LSM) tree methods. The implementation framework based on Erlang OTP is proposed to provide a basis for sustainability and resiliency.
For the needs of real-time parallel processing of critical task and real-time state control in muti-node computing system, a lightweight distributed processing computer mechanism is designed for iterative computing ty...
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The rapid evolution of mobile networks presents challenges for devices with limited computing power. Mobile or multi-access edge computing (MEC) addresses this by providing computing resources in proximity to end devi...
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With the advances in positioning techniques, trajectories are emerged with semantic information such as location-based activities and sign-ins. Essential for applications such as trip recommendations, range queries on...
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Deep Learning Neural Networks (DLNN) require an immense amount of computation, especially in the training phase when multiple layers of intermediate neurons need to be built. The situation is even more dramatic today ...
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ISBN:
(纸本)9798400718021
Deep Learning Neural Networks (DLNN) require an immense amount of computation, especially in the training phase when multiple layers of intermediate neurons need to be built. The situation is even more dramatic today with the proliferation of applications with intelligence at the edge, not just in the cloud. Therefore, to meet the new requirements of edge computing, it is imperative to accelerate the execution phases of neural networks as much as possible. In this paper, we will focus on the algorithm known as Particle Swarm Optimization (PSO). It is a bio-inspired, stochastic optimization approach whose goal is to iteratively improve the solution to a given (usually complex) problem by attempting to approximate a given objective. The use of PSO in an edge computing environment has the potential to make the training of the DLNN there without the need to transfer resource-intensive tasks to the cloud. However, implementing an efficient PSO is not a straightforward process due to the complexity of the computations performed on the swarm of particles and the iterative execution until until a near-target solution with minimal error is achieved. In the present work, two parallelizations of the PSO algorithm have been implemented, both designed for a distributed execution environment (Apache Spark). The first PSO parallelization follows a synchronous scheme;i.e., the best global position found by particles is globally updated before the execution of the next iteration of the algorithm. This implementation proved to be more efficient for medium-sized datasets (<40000 data points). In contrast, the second implementation is an asynchronous parallel variant of the PSO algorithm, which showed lower execution time for large datasets (> 170,000 data points) compared to the first one. Additionally, it exhibits better scalability and elasticity with respect to increasing dataset size. Both variants of the PSO have been implemented to distribute the computational load (particle fit
We present the first GPU-based parallel algorithm to efficiently update vertex coloring on large dynamic networks. For single GPU, we introduce the concept of loosely maintained vertex color update that reduces comput...
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ISBN:
(纸本)9781665494236
We present the first GPU-based parallel algorithm to efficiently update vertex coloring on large dynamic networks. For single GPU, we introduce the concept of loosely maintained vertex color update that reduces computation and memory requirements. For multiple GPUs, in distributed environments, we propose priority-based ordering of vertices to reduce the communication time. We prove the correctness of our algorithms and experimentally demonstrate that for graphs of over 16 million vertices and over 134 million edges on a single GPU, our dynamic algorithm is as much as 20x faster than state-of-the-art algorithm on static graphs. For larger graphs with over 130 million vertices and over 260 million edges, our distributed implementation with 8 GPUs produces updated color assignments within 160 milliseconds. In all cases, the proposed parallel algorithms produce comparable or fewer colors than state-of-the-art algorithms.
Federated Learning (FL) is a collaborative model training approach that protects data privacy while allowing for model updates and optimization. However, FL is vulnerable to poisoning attacks due to its distributed na...
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Terrain parameters such as slope, aspect, and hillshading are essential in various applications, including agriculture, forestry, and hydrology. However, generating high-resolution terrain parameters is computationall...
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ISBN:
(纸本)9798400701559
Terrain parameters such as slope, aspect, and hillshading are essential in various applications, including agriculture, forestry, and hydrology. However, generating high-resolution terrain parameters is computationally intensive, making it challenging to provide these value-added products to communities in need. We present a scalable workflow called GEOtiled that leverages data partitioning to accelerate the computation of terrain parameters from digital elevation models, while preserving accuracy. We assess our workflow in terms of its accuracy and wall time by comparing it to SAGA, which is highly accurate but slow to generate results, and to GDAL, which supports memory optimizations but not data parallelism. We obtain a coefficient of determination (R-2) between GEOtiled and SAGA of 0.794, ensuring accuracy in our terrain parameters. We achieve an X6 speedup compared to GDAL when generating the terrain parameters at a high-resolution (10 m) for the Contiguous United States (CONUS).
Although traditional 3D terrain algorithms can improve the rendering efficiency of the terrain, they often ignore the performance of the terrain itself. The use of four textures is not sufficient to deal with complex ...
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