
Additive Link Metrics Identification: Proof of Selected Lemmas and Propositions
This is a technical report, containing all the lemma and proposition pro...
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Identification of Additive Link Metrics: Proof of Selected Theorems
This is a technical report, containing all the theorem proofs in the fol...
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Efficient Identification of Additive Link Metrics: Theorem Proof and Evaluations
This is a technical report, containing all the theorem proofs and additi...
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Partial Network Identifiability: Theorem Proof and Evaluation
This is a technical report, containing all the theorem proofs and additi...
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Link Identifiability with Two Monitors: Proof of Selected Theorems
This is a technical report, containing all the theorem proofs in paper "...
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Fundamental Theories in Node Failure Localization
This is a technical report, containing all the theorem proofs in paper "...
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Failure Localization Capability: Theorem Proof and Evaluation
This is a technical report, containing all the theorem proofs and additi...
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Node Failure Localization: Theorem Proof
This is a technical report, containing all the theorem proofs in paper "...
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Resource Allocation in Onedimensional Distributed Service Networks with Applications
We consider assignment policies that allocate resources to users, where ...
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On the Analysis of Spatially Constrained Power of Two Choice Policies
We consider a class of power of two choice based assignment policies for...
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Joint StateAction Embedding for Efficient Reinforcement Learning
While reinforcement learning has achieved considerable successes in rece...
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EnergyEfficient Resource Management for Federated Edge Learning with CPUGPU Heterogeneous Computing
Edge machine learning involves the deployment of learning algorithms at ...
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State Action Separable Reinforcement Learning
Reinforcement Learning (RL) based methods have seen their paramount succ...
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Data Selection for Federated Learning with Relevant and Irrelevant Data at Clients
Federated learning is an effective way of training a machine learning mo...
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Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approach
Federated learning (FL) is an emerging technique for training machine le...
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FastFourierForecasting Resource Utilisation in Distributed Systems
Distributed computing systems often consist of hundreds of nodes, execut...
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Resource Sharing in the Edge: A Distributed BargainingTheoretic Approach
The growing demand for edge computing resources, particularly due to inc...
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Let's Share: A GameTheoretic Framework for Resource Sharing in Mobile Edge Clouds
Mobile edge computing seeks to provide resources to different delaysens...
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MACS: Deep Reinforcement Learning based SDN Controller Synchronization Policy Design
In distributed softwaredefined networks (SDN), multiple physical SDN co...
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EnergyEfficient Radio Resource Allocation for Federated Edge Learning
Edge machine learning involves the development of learning algorithms at...
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Online Collection and Forecasting of Resource Utilization in LargeScale Distributed Systems
Largescale distributed computing systems often contain thousands of dis...
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A GameTheoretic Framework for Resource Sharing in Clouds
Providing resources to different users or applications is fundamental to...
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Learning the Optimal Synchronization Rates in Distributed SDN Control Architectures
Since the early development of SoftwareDefined Network (SDN) technology...
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Resource Allocation in Onedimensional Distributed Service Networks
We consider assignment policies that allocate resources to users, where ...
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A queueingtheoretic model for resource allocation in onedimensional distributed service network
We consider assignment policies that allocate resources to requesting us...
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DQ Scheduler: Deep Reinforcement Learning Based Controller Synchronization in Distributed SDN
In distributed softwaredefined networks (SDN), multiple physical SDN co...
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A GameTheoretic Approach to MultiObjective Resource Sharing and Allocation in Mobile Edge Clouds
Mobile edge computing seeks to provide resources to different delaysens...
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When Edge Meets Learning: Adaptive Control for ResourceConstrained Distributed Machine Learning
Emerging technologies and applications including Internet of Things (IoT...
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Joint Data Compression and Caching: Approaching Optimality with Guarantees
We consider the problem of optimally compressing and caching data across...
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How Better is Distributed SDN? An Analytical Approach
Distributed softwaredefined networks (SDN), consisting of multiple inte...
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Optimal Energy Consumption with Communication, Computation, Caching and QoIGuarantee
Energy efficiency is a fundamental requirement of modern data communicat...
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Optimal Energy Tradeoff among Communication, Computation and Caching with QoIGuarantee
Many applications must ingest and analyze data that are continuously gen...
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Kin K. Leung
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