This paper presents SPARE, a novel serverless platform that supports self-adaptive resource allocation and reconfiguration, thereby increasing the availability of computing resources for time-critical tasks in urgent ...
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ISBN:
(数字)9798331524937
ISBN:
(纸本)9798331524944
This paper presents SPARE, a novel serverless platform that supports self-adaptive resource allocation and reconfiguration, thereby increasing the availability of computing resources for time-critical tasks in urgent events. In emergency scenarios, SPARE reallocates resources by forwarding serverless function invocations to the nearest edge nodes having sufficient capacity. Additionally, the platform employs the use of unikernels and lightweight virtualization through Firecracker, which helps to reduce cold start times and improve function responsiveness. The experimental results demonstrate that SPARE is capable of releasing up to one-third of edge nodes within a serverless edge platform, while only experiencing a mild increase in latency, thus maintaining service continuity.
The integration of distributed Generation (DG) in grid-connected systems presents challenges in maintaining reliable protection schemes, particularly in coordinating Directional Overcurrent Relays (DOCR). This paper p...
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ISBN:
(数字)9798331530402
ISBN:
(纸本)9798331530419
The integration of distributed Generation (DG) in grid-connected systems presents challenges in maintaining reliable protection schemes, particularly in coordinating Directional Overcurrent Relays (DOCR). This paper proposes an Adaptive Protection scheme that optimizes the coordination of DOCRs using a Micro-genetic Algorithm ($\mu \mathbf{G A}$). The proposed method enhances the resilience and adaptability of protection mechanisms against variable fault conditions and DG contributions. Through simulation studies, the performance of the $\mu$ GA-based approach is evaluated against traditional coordination methods, demonstrating improved fault detection and minimized relay operating times. Results indicate significant reductions in coordination time intervals, ensuring reliable operation of protection systems in the presence of DG. This research offers a framework for enhancing the operational reliability of power systems while accommodating the increasing prevalence of distributed energy resources.
Sparse triangular solve (SpTRSV) is a vital component in various scientific applications, and numerous GPU-based SpTRSV algorithms have been proposed. Synchronization-free SpTRSV is currently the mainstream algorithm ...
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This paper studies rule-based blocking in Entity Resolution (ER). We propose Hyper Blocker, a GPU-accelerated system for blocking in ER. As opposed to previous blocking algorithms and parallel blocking solvers, Hyper ...
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This paper proposes a comprehensive solution to combat the growing threat of Deepfake technology, employing Convolutional Neural Networks (CNNs) and Blockchain. CNNs analyze video frames for anomalies indicative of De...
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Artificial Intelligence of Things (AIoT) systems are widely utilized in various domains such as smart logistics and smart health. In AIoT systems, computational tasks earmarked for offloading by IoT devices often have...
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Due to the limited resources of edge networks, the heterogeneity of user content requests, high-cost caching from direct resource hits, and redundancy in resource retention time hinder system performance. Traditional ...
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Multivariate time series anomaly detection (MTAD) poses a challenge due to temporal and feature dependencies. The critical aspects of enhancing the detection performance lie in accurately capturing the dependencies be...
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Self-supervised time series anomaly detection (TSAD) demonstrates remarkable performance improvement by extracting high-level data semantics through proxy tasks. Nonetheless, most existing self-supervised TSAD techniq...
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Sparse Matrix-Dense Matrix Multiplication (SpMM) is a crucial kernel used in a wide range of fields including machine learning and linear algebra solvers. Thus, enhancing the performance of SpMM is essential. The unev...
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