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1 code implementation • 11 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.

1 code implementation • 2 Dec 2022 • Shreshth Tuli, Giuliano Casale, Ludmila Cherkasova, Nicholas R. Jennings

The emergence of latency-critical AI applications has been supported by the evolution of the edge computing paradigm.

1 code implementation • 16 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.

1 code implementation • 21 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.

1 code implementation • 21 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.

1 code implementation • 14 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.

1 code implementation • 18 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.

2 code implementations • 16 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.

1 code implementation • 14 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.

1 code implementation • 4 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.

no code implementations • 25 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.

2 code implementations • 11 Oct 2021 • Shreshth Tuli, Sukhpal Singh Gill, Minxian Xu, Peter Garraghan, Rami Bahsoon, Schahram Dustdar, Rizos Sakellariou, Omer Rana, Rajkumar Buyya, Giuliano Casale, Nicholas R. Jennings

The worldwide adoption of cloud data centers (CDCs) has given rise to the ubiquitous demand for hosting application services on the cloud.

2 code implementations • 6 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.

1 code implementation • 14 Nov 2020 • Daniel Lengyel, Janith Petangoda, Isak Falk, Kate Highnam, Michalis Lazarou, Arinbjörn Kolbeinsson, Marc Peter Deisenroth, Nicholas R. Jennings

We propose an efficient algorithm to visualise symmetries in neural networks.

no code implementations • 2 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.

1 code implementation • 28 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.

no code implementations • 27 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.

no code implementations • NeurIPS 2019 • Edoardo Manino, Long Tran-Thanh, Nicholas R. Jennings

Third, SBIC has provable asymptotic guarantees both in the online and offline settings.

no code implementations • 13 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.

no code implementations • 7 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.

no code implementations • 19 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.

no code implementations • 17 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).

no code implementations • 26 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.

no code implementations • 8 Sep 2013 • Feng Wu, Nicholas R. Jennings

Many multi-agent coordination problems can be represented as DCOPs.

1 code implementation • 1 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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