Search Results for author: Nicholas R. Jennings

Found 25 papers, 15 papers with code

CILP: Co-simulation based Imitation Learner for Dynamic Resource Provisioning in Cloud Computing Environments

1 code implementation11 Feb 2023 Shreshth Tuli, Giuliano Casale, Nicholas R. Jennings

CILP leverages a neural network as a surrogate model to predict future workload demands with a co-simulated digital-twin of the infrastructure to compute QoS scores.

Imitation Learning

DRAGON: Decentralized Fault Tolerance in Edge Federations

1 code implementation16 Aug 2022 Shreshth Tuli, Giuliano Casale, Nicholas R. Jennings

Edge Federation is a new computing paradigm that seamlessly interconnects the resources of multiple edge service providers.

Edge-computing Fault Detection

SplitPlace: AI Augmented Splitting and Placement of Large-Scale Neural Networks in Mobile Edge Environments

1 code implementation21 May 2022 Shreshth Tuli, Giuliano Casale, Nicholas R. Jennings

This makes the problem of deploying such large-scale neural networks challenging in resource-constrained mobile edge computing platforms, specifically in mission-critical domains like surveillance and healthcare.

Edge-computing Multi-Armed Bandits

MetaNet: Automated Dynamic Selection of Scheduling Policies in Cloud Environments

1 code implementation21 May 2022 Shreshth Tuli, Giuliano Casale, Nicholas R. Jennings

Task scheduling is a well-studied problem in the context of optimizing the Quality of Service (QoS) of cloud computing environments.

Management Scheduling

CAROL: Confidence-Aware Resilience Model for Edge Federations

1 code implementation14 Mar 2022 Shreshth Tuli, Giuliano Casale, Nicholas R. Jennings

To address this, we present a confidence aware resilience model, CAROL, that utilizes a memory-efficient generative neural network to predict the Quality of Service (QoS) for a future state and a confidence score for each prediction.

TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data

1 code implementation18 Jan 2022 Shreshth Tuli, Giuliano Casale, Nicholas R. Jennings

Efficient anomaly detection and diagnosis in multivariate time-series data is of great importance for modern industrial applications.

Anomaly Detection Meta-Learning +1

GOSH: Task Scheduling Using Deep Surrogate Models in Fog Computing Environments

2 code implementations16 Dec 2021 Shreshth Tuli, Giuliano Casale, Nicholas R. Jennings

Advances like deterministic surrogate models, deep neural networks (DNN) and gradient-based optimization allow low energy consumption and response times to be reached.


MCDS: AI Augmented Workflow Scheduling in Mobile Edge Cloud Computing Systems

1 code implementation14 Dec 2021 Shreshth Tuli, Giuliano Casale, Nicholas R. Jennings

Workflow scheduling is a long-studied problem in parallel and distributed computing (PDC), aiming to efficiently utilize compute resources to meet user's service requirements.

Distributed Computing Edge-computing +1

PreGAN: Preemptive Migration Prediction Network for Proactive Fault-Tolerant Edge Computing

1 code implementation4 Dec 2021 Shreshth Tuli, Giuliano Casale, Nicholas R. Jennings

Building a fault-tolerant edge system that can quickly react to node overloads or failures is challenging due to the unreliability of edge devices and the strict service deadlines of modern applications.

Edge-computing Fault Detection

Optimal Auction Design for the Gradual Procurement of Strategic Service Provider Agents

no code implementations25 Oct 2021 Farzaneh Farhadi, Maria Chli, Nicholas R. Jennings

The service consumer requires a procurement strategy that achieves the optimal balance between success probability and invocation cost.

Generative Optimization Networks for Memory Efficient Data Generation

2 code implementations6 Oct 2021 Shreshth Tuli, Shikhar Tuli, Giuliano Casale, Nicholas R. Jennings

In standard generative deep learning models, such as autoencoders or GANs, the size of the parameter set is proportional to the complexity of the generated data distribution.

Anomaly Detection Time Series Analysis

On Population-Based Algorithms for Distributed Constraint Optimization Problems

no code implementations2 Sep 2020 Saaduddin Mahmud, Md. Mosaddek Khan, Nicholas R. Jennings

The main characteristic of these algorithms is that they maintain a population of candidate solutions of a given problem and use this population to cover a large area of the search space and to avoid local-optima.

Real-Time Detection of Dictionary DGA Network Traffic using Deep Learning

1 code implementation28 Mar 2020 Kate Highnam, Domenic Puzio, Song Luo, Nicholas R. Jennings

Botnets and malware continue to avoid detection by static rules engines when using domain generation algorithms (DGAs) for callouts to unique, dynamically generated web addresses.

Learning Optimal Temperature Region for Solving Mixed Integer Functional DCOPs

no code implementations27 Feb 2020 Saaduddin Mahmud, Md. Mosaddek Khan, Moumita Choudhury, Long Tran-Thanh, Nicholas R. Jennings

Distributed Constraint Optimization Problems (DCOPs) are an important framework for modeling coordinated decision-making problems in multi-agent systems with a set of discrete variables.

Decision Making

AED: An Anytime Evolutionary DCOP Algorithm

no code implementations13 Sep 2019 Saaduddin Mahmud, Moumita Choudhury, Md. Mosaddek Khan, Long Tran-Thanh, Nicholas R. Jennings

Evolutionary optimization is a generic population-based metaheuristic that can be adapted to solve a wide variety of optimization problems and has proven very effective for combinatorial optimization problems.

Combinatorial Optimization

Selling Multiple Items via Social Networks

no code implementations7 Mar 2019 Dengji Zhao, Bin Li, Junping Xu, Dong Hao, Nicholas R. Jennings

We consider a market where a seller sells multiple units of a commodity in a social network.

Efficiency of active learning for the allocation of workers on crowdsourced classification tasks

no code implementations19 Oct 2016 Edoardo Manino, Long Tran-Thanh, Nicholas R. Jennings

Crowdsourcing has been successfully employed in the past as an effective and cheap way to execute classification tasks and has therefore attracted the attention of the research community.

Active Learning General Classification

Efficient Task Collaboration with Execution Uncertainty

no code implementations17 Sep 2015 Dengji Zhao, Sarvapali D. Ramchurn, Nicholas R. Jennings

We study a general task allocation problem, involving multiple agents that collaboratively accomplish tasks and where agents may fail to successfully complete the tasks assigned to them (known as execution uncertainty).

Learning Periodic Human Behaviour Models from Sparse Data for Crowdsourcing Aid Delivery in Developing Countries

no code implementations26 Sep 2013 James McInerney, Alex Rogers, Nicholas R. Jennings

In many developing countries, half the population lives in rural locations, where access to essentials such as school materials, mosquito nets, and medical supplies is restricted.

A Linear Approximation Method for the Shapley Value

1 code implementation1 Sep 2008 S. Shaheen Fatima, Michael Wooldridge, Nicholas R. Jennings

This method has time complexity linear in the number of players, but has an approximation error that is, on average, lower than Owen's.

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