The proceedings contain 13 papers. The topics discussed include: a study on the performance of distributed storage systems in edge computing environments;RESCAPE: a resource estimation system for microservices with gr...
ISBN:
(纸本)9798350387339
The proceedings contain 13 papers. The topics discussed include: a study on the performance of distributed storage systems in edge computing environments;RESCAPE: a resource estimation system for microservices with graph neural network and profile engine;PrometheusMigrate: efficient live migration of confidential virtual machine with software abstraction;the cost perspective of adopting large language model-as-a-service;DCSA: the deployment mechanism of chained serverless applications in JointCloud environment;parallel computation in dynamic fog computing networks: a multi-armed bandit learning-based decentralized matching approach;and IBRI: an IoT solution for building collapse risk identification in smart cities.
Workload traces play a crucial role in the design and evaluation of storage systems. They are used for evaluating system performance and optimizing aspects such as data access speeds, energy consumption, and load bala...
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ISBN:
(纸本)9798350361360;9798350361353
Workload traces play a crucial role in the design and evaluation of storage systems. They are used for evaluating system performance and optimizing aspects such as data access speeds, energy consumption, and load balancing. Most publicly available traces were collected in cloud environments, which limits their ability to represent workloads in user-centric and highly disaggregated settings such as edge systems. In this paper, we present WoW-IO, an open-source object trace generator based on the popular video game 'World of Warcraft'. WoW-IO uses, as input, logs of in-game avatar information from the game's servers. We describe the principles and assumptions used in the design of WoW-IO, how the generated traces can be used to evaluate various aspects of an edge storage-system design, and how the trace format can be extended to reflect additional details and complex usage scenarios.
Many parallel and distributedcomputing research results are obtained in simulation, using simulators that mimic real-world executions on some target system. Each such simulator is configured by picking values for par...
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ISBN:
(纸本)9798350364613;9798350364606
Many parallel and distributedcomputing research results are obtained in simulation, using simulators that mimic real-world executions on some target system. Each such simulator is configured by picking values for parameters that define the behavior of the underlying simulation models it implements. The main concern for a simulator is accuracy: simulated behaviors should be as close as possible to those observed in the real-world target system. This requires that values for each of the simulator's parameters be carefully picked, or "calibrated," based on ground truth real-world executions. Examining the current state of the art shows that simulator calibration, at least in the field of parallel and distributedcomputing, is often undocumented (and thus perhaps often not performed) and, when documented, is described as a labor-intensive, manual process. In this work we evaluate the benefit of automating simulation calibration using simple algorithms. Specifically, we use a real-world case study from the field of High Energy Physics and compare automated calibration to calibration performed by a domain scientist. Our main finding is that automated calibration is on par with or significantly outperforms the calibration performed by the domain scientist. Furthermore, automated calibration makes it straightforward to operate desirable trade-offs between simulation accuracy and simulation speed.
This paper proposes a framework that combines Federated Learning (FL) and blockchain technologies in applications where sensitive data need to be analyzed. FL allows exchanging machine learning model parameters instea...
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ISBN:
(纸本)9798350304367;9798350304374
This paper proposes a framework that combines Federated Learning (FL) and blockchain technologies in applications where sensitive data need to be analyzed. FL allows exchanging machine learning model parameters instead of sensitive data, thus ensuring data privacy preservation. Model parameters are ciphered and stored into the InterPlanetary File System (IPFS). Coordination via a dedicated smart contract allows to efficiently handle the parameters update phases, fortifying data security. We validate our approach using an Alzheimer's MRI image dataset, showing the benefits in terms of practical implementation and classification accuracy.
This paper addresses the consistency issue of multi-robot distributed cooperative localization. We introduce a consistent distributed cooperative localization algorithm conducting state estimation in a transformed coo...
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ISBN:
(纸本)9798350377712;9798350377705
This paper addresses the consistency issue of multi-robot distributed cooperative localization. We introduce a consistent distributed cooperative localization algorithm conducting state estimation in a transformed coordinate. The core idea involves a linear time-varying coordinated transformation to render the propagation Jacobian independent of the state and make it suitable for a distributed manner. This transformation is seamlessly integrated into a server-based distributed cooperative localization framework, in which each robot estimates its own state while the server maintains the cross-correlations. The transformation ensures the correct observability property of the entire framework. Moreover, the algorithm accommodates various types of robot-to-robot relative measurements, broadening its applicability. Through simulations and real-world dataset experiments, the proposed algorithm has demonstrated better performance in terms of both consistency and accuracy compared to existing algorithms.
This research presents a novel wavelet transform and machine learning-based method to microgrid protection, with a focus on the Unified Power Flow Controller (UPFC). Microgrid components such as distribution generator...
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ISBN:
(纸本)9798350385939;9798350385922
This research presents a novel wavelet transform and machine learning-based method to microgrid protection, with a focus on the Unified Power Flow Controller (UPFC). Microgrid components such as distribution generators are critical to enhancing the reliability of electricity networks. For different sources and loads to integrate smoothly, effective defect identification is required. The technique uses wavelet transform to extract meaningful information from failure signals, which is then fed into a machine learning model for real-time identification. This method analyzes current signals from both ends of the transmission line using wavelet-based multi-resolution analysis, and then compares the results to preset thresholds to generate fault indices. This approach offers a reliable microgrid protection solution with lower losses and increased dependability.
As deep learning grows rapidly, model training heavily relies on parallel methods and there exist numerous cluster configurations. However, current preferences for parallel training focus on data centers, overlooking ...
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Within the evolving landscape of confidential and trustworthy computing, this paper delves into the prevalent challenges such as secrecy or privacy associated with existing addressing schemes. Taking these issues into...
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ISBN:
(纸本)9798331541378
Within the evolving landscape of confidential and trustworthy computing, this paper delves into the prevalent challenges such as secrecy or privacy associated with existing addressing schemes. Taking these issues into consideration, we propose a novel solution, drawing inspiration from distributed quantum computing algorithms, to generate a secret addressing scheme. The objective is to enhance the confidentiality and reliability of computingsystems. This proposed solution, inspired from quantum phase estimation (QPE), assigns a unique and confidential address to each node in a network. It also dynamically adapts to changes in the network or system configurations by using the proposed QPE-inspired approach for every new incoming node in the network. We have implemented our solution on a quantum network simulator, namely NetSquid, to assess the effectiveness of our proposed solution under varying conditions, including scenarios with and without noise models. The simulation results of our proposed solution demonstrate both scalability (e.g., it takes 34.5ns to address a single computing node with a 3-bit address and 230ns with up to 20-bit addresses) and accuracy (e.g., success probability of intended address distribution without noises is 100% and with mild noises it is roughly 80%). Our proposed solution effectively handles the growth of the network while ensuring a consistently high level of accuracy.
With the rapid development and popularization of the Internet today, mechanical processing enterprises should not only adapt to new situations, but also reorganize various business processes. This article uses the Web...
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In this paper, we propose a method for mobile edge computing (MEC) using unmanned aerial vehicles (UAVs) to enhance wireless connectivity in areas afflicted by natural disasters or obstructed by tall buildings. Despit...
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ISBN:
(纸本)9798350368130
In this paper, we propose a method for mobile edge computing (MEC) using unmanned aerial vehicles (UAVs) to enhance wireless connectivity in areas afflicted by natural disasters or obstructed by tall buildings. Despite the active research on deep reinforcement learning in recent years, challenges persist in its application to the optimization of cooperative and coordinated behavior among multi-agents. MEC aims to improve efficiency and fairness in offloading from user terminals (UTs) to UAVs while minimizing energy consumption. Therefore, UAV groups must operate independently within designated areas to facilitate connections between UTs and servers. We introduce multi-agent deep deterministic policy gradient to MEC and improve the reward design to achieve high-performance MEC with efficient cooperative behavior only using limited local data. Experimental results demonstrate that our approach significantly enhances MEC efficiency through effective cooperative behavior. Specifically, it offloads more tasks/applications to UAVs compared to baseline methods while reducing energy consumption per offloading.
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