5G mobile networks will soon be available to handle all types of applications and to provide services to massive numbers of users. In this complex and dynamic network ecosystem, an end-to-end performance analysis and ...
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Modeling the performance of real-world applications at scale is essential for designing next-generation platforms and shaping the development of future algorithms. However, accurately capturing the complexity of appli...
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Paxos, the de facto standard approach to solving distributed consensus, operates in two phases, each of which requires an intersecting quorum of nodes. Multi-Paxos reduces this to one phase by electing a leader but th...
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ISBN:
(纸本)9781450389334
Paxos, the de facto standard approach to solving distributed consensus, operates in two phases, each of which requires an intersecting quorum of nodes. Multi-Paxos reduces this to one phase by electing a leader but this leader is also a performance bottleneck. Fast Paxos bypasses the leader but has stronger quorum intersection requirements. In this paper we observe that Fast Paxos' intersection requirements can be safely relaxed, reducing to just one additional intersection requirement between phase-1 quorums and any pair of fast round phase-2 quorums. We thus find that the quorums used with Fast Paxos are larger than necessary, allowing alternative quorum systems to obtain new tradeoffs between performance and fault-tolerance.
Convolutional neural networks (CNNs) have achieved immense success in computer vision and other field of science. Despite the achievements, state-of-the-art CNN models have grown to gigantic sizes that demand a lot of...
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A key feature of cloud computing is elasticity, which enables resources to be allocated and de-allocated according to dynamic application demands. The evolution of Industrial Control Systems (ICS) is moving towards a ...
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Federated learning (FL) is a distributed Machine Learning (ML) framework that is capable of training a new global model by aggregating clients' locally trained models without sharing users' original data. Fede...
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ISBN:
(纸本)9789819608041;9789819608058
Federated learning (FL) is a distributed Machine Learning (ML) framework that is capable of training a new global model by aggregating clients' locally trained models without sharing users' original data. Federated learning as a service (FLaaS) offers a privacy-preserving approach for training machine learning models on devices with various computational resources. Most proposed FL-based methods train the same model in all client devices regardless of their computational resources. However, in practical Internet of Things (IoT) scenarios, IoT devices with limited computational resources may not be capable of training models that client devices with greater hardware performance hosted. Most of the existing FL frameworks that aim to solve the problem of aggregating heterogeneous models are designed for Independent and Identical distributed (IID) data, which may make it hard to reach the target algorithm performance when encountering non-IID scenarios. To address these problems in hierarchical networks, in this paper, we propose a heterogeneous aggregation framework for hierarchical edge systems called HAF-Edge. In our proposed framework, we introduce a communication-efficient model aggregation method designed for FL systems with two-level model aggregations running at the edge and cloud levels. This approach enhances the convergence rate of the global model by leveraging selective knowledge transfer during the aggregation of heterogeneous models. To the best of our knowledge, this work is pioneering in addressing the problem of aggregating heterogeneous models within hierarchical FL systems spanning IoT, edge, and cloud environments. We conducted extensive experiments to validate the performance of our proposed method. The evaluation results demonstrate that HAF-Edge significantly outperforms state-of-the-art methods.
Compared with the traditional network, Software-Defined networking (SDN) provides a more convenient network paradigm to build Access Control List (ACL) application. There has been a few studies focusing on ACL applica...
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ISBN:
(纸本)9783030967727;9783030967710
Compared with the traditional network, Software-Defined networking (SDN) provides a more convenient network paradigm to build Access Control List (ACL) application. There has been a few studies focusing on ACL application in SDN up to now, but most of the existing work adopts a reactive way to enforce ACL, resulting in new ACL update can not take effect immediately. In this paper, we propose CLACK, an approach for user-driven centralized ACL in SDN. We implement CLACK on both Floodlight and ONOS controller. The experimental results show that CLACK has a better performance than the existing Floodlight firewall application.
A type of wireless communication network called a mobile ad hoc network (MANET) is composed of numerous flexible components that can move in any of the network's many directions. The Intrusion Detection System (ID...
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A communication network is a graph in which each node has only local information about the graph and nodes communicate by passing messages along its edges. Here, we consider the geometric communication network where t...
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ISBN:
(纸本)9781450389334
A communication network is a graph in which each node has only local information about the graph and nodes communicate by passing messages along its edges. Here, we consider the geometric communication network where the nodes also occupy points in space and the distance between points is the Euclidean distance. Our goal is to understand the communication cost needed to solve several fundamental geometry problems, including Convex Hull, Diameter, Closest Pair, and approximations of these problems, in the asynchronous CONGEST KT1 model. This extends the 2011 result of Rajsbaum and Urrutia for finding a convex hull of a planar geometric communication network to networks of arbitrary topology.
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