This paper implements Timing-Go, a distributed timed task scheduling system based on a loosely coupled architecture, which solves the problems of poor manageability, poor parallel processing capability, poor task sche...
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The digital twin platform builds a twin model database through data aggregation, achieving centralized storage and management of multi-dimensional, cross professional, and format normalized twin data. By associating v...
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Annealing computation has recently attracted attention as it can efficiently solve various combinatorial optimization problems using an Ising model. Stochastic cellular automata annealing (SCA) is a promising algorith...
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ISBN:
(纸本)9781665497473
Annealing computation has recently attracted attention as it can efficiently solve various combinatorial optimization problems using an Ising model. Stochastic cellular automata annealing (SCA) is a promising algorithm that can realize fast spin-update by utilizing its parallelcomputing capability. However, in SCA, preparing an appropriate control of the pinning parameter is a hard task, which degrades its usability. This paper proposes a novel approach called APC-SCA (Autonomous Pinning effect Control SCA) where the spin pinning parameter can be controlled autonomously by observing individual spin flips. The evaluation results using max-cut and N-queen problems demonstrate that the proposed approach can obtain better solutions than the conventional approach with a grid search of optimal pinning parameter control.
Client selection (a.k.a., client sampling) is one of the hot topics in Federated Learning (FL). In each communication round, selecting some clients to participate in aggregation can effectively reduce the communicatio...
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ISBN:
(纸本)9798350302936
Client selection (a.k.a., client sampling) is one of the hot topics in Federated Learning (FL). In each communication round, selecting some clients to participate in aggregation can effectively reduce the communication overhead caused by exchanging model parameters. However, due to statistical heterogeneity in FL, selecting clients randomly may affect the performance of aggregated global models. existing approaches regarding client selection firstly cluster clients and then sample(select) some representative clients from each cluster. However, these clusteringbased approaches may be either time-intensive or high complexity. To address these issues, In this paper, we introduce Bi-level Sampling, a clustering-based approach for client selection. After multinomial distribution sampling, Bi-level Sampling clusters clients based on weighted per-label mean class scores and then selects participating clients for federated learning in each round. Bi-level Sampling can lead to better client representativity and the reduced variance of the client's stochastic aggregation weights in FL. Our approach can be integrated into typical FL frameworks. Experimental results show that, compared with state-of-the-art approaches, our approach demonstrates significantly more stable and accurate convergence behavior-getting higher test accuracy and less training time, especially in highly Non-IID settings.
Over the last few years, the number of IoT devices in daily use has increased, as they come in many sizes and of different types. In addition to this, these devices have become cheaper, which has led to many more peop...
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ISBN:
(纸本)9781665469586
Over the last few years, the number of IoT devices in daily use has increased, as they come in many sizes and of different types. In addition to this, these devices have become cheaper, which has led to many more people being able to use them. These devices are capable of both creating and processing information, thus reducing network overload. However, in Cloud or Edge computing environments, it is useful to know where these devices are located, in order to better distribute the information among the servers and further reduce the network load, allowing users to get the data faster. Therefore, there are simulators capable of analyzing Cloud infrastructures, but most of them fail to offer the possibility of including mobility in the sensors. For these reasons, in this paper we detail an API extension developed on the Simgrid toolkit to add mobility to IoT sensors and, in addition, it integrates with an API called Folium for the visualization of the mobility of these elements.
This work proposes an adaptive PI-based dynamic droop gain adjustment approach for parallel converters in DC microgrids. An adaptive droop control has been introduced with distributed consistency secondary control to ...
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The proceedings contain 295 papers. The topics discussed include: deep learning & computer vision integrated smart voting system;performance analysis of machine learning algorithms with hyperparameter tuning for d...
ISBN:
(纸本)9798350347456
The proceedings contain 295 papers. The topics discussed include: deep learning & computer vision integrated smart voting system;performance analysis of machine learning algorithms with hyperparameter tuning for diabetes prediction;transient analysis of motor terminal voltage, common mode voltage and bearing voltage in 2-level and multilevel PWM inverter fed induction motor with long cable;comparative analysis of phase/frequency detector in a complete PLL system;a research on various security aware mechanisms in multi-cloud environment for improving data security;network traffic virtualization using Wireshark and Google maps;cluster enabled routing algorithm for wireless sensor networks: an optimization and future prospective;detection and recognition of animals using Yolo algorithm;voltage improvement and loss reduction by placement and sizing of DG using grid oriented multi objective particle swarm optimization;prediction of heart disease using naive bayes and particle swarm optimization (PSO) method;lane detection using video processing for robot cars;convolutional neural network for printed circuit board verification;and multiple disease prediction based on user symptoms using machine learning algorithms.
Aiming at the typical characteristics of constant changes in the power grid topology caused by multiple factors such as large-scale access of distributed power supply and diversified loads, this paper analyzes the sho...
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ZooKeeper Atomic Broadcast (Zab) is a high-performance atomic broadcast protocol, which is a key component of Apache ZooKeeper. By ensuring strong consistency and fault tolerance, the Zab protocol plays a crucial role...
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As the Function-as-a-Service (FaaS) paradigm enjoys growing popularity within Cloud-based systems, there is increasing interest in moving serverless functions towards the Edge, to better support geo-distributed and pe...
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ISBN:
(纸本)9781665453783
As the Function-as-a-Service (FaaS) paradigm enjoys growing popularity within Cloud-based systems, there is increasing interest in moving serverless functions towards the Edge, to better support geo-distributed and pervasive applications. However, enjoying both the reduced latency of Edge and the scalability of FaaS requires new architectures and implementations to cope with typical Edge challenges (e.g., nodes with limited computational capacity). While first solutions have been proposed for Edge-based FaaS, including light function sandboxing techniques, we lack a platform with the ability to span both Edge and Cloud and adaptively exploit both. In this paper, we present Serverledge, a FaaS platform designed for the Edge-to-Cloud continuum. Serverledge adopts a decentralized architecture, where function invocation requests can be fully served within Edge nodes. To cope with load peaks, Serverledge also supports vertical (i.e., from Edge to Cloud) and horizontal (i.e., among Edge nodes) computation offloading. Our evaluation shows that Serverledge outperforms Apache OpenWhisk in an Edge-like scenario and has competitive performance with state-of-the-art frameworks optimized for the Edge, with the advantage of built-in support for vertical and horizontal offloading.
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