Nathaniel Hudson, Ph.D.

selected publications

1. Preprint

   “Flight: A FaaS-Based Framework for Complex and Hierarchical Federated Learning”

   Hudson, Nathaniel, Hayot-Sasson, Valerie,  Babuji, Yadu and 5 more authors

   arXiv preprint arXiv:2409.16495 2024

   arXiv Bib

   @article{hudson2024flight,
     title = {Flight: A FaaS-Based Framework for Complex and Hierarchical Federated Learning},
     author = {Hudson, Nathaniel and Hayot-Sasson, Valerie and Babuji, Yadu and Baughman, Matt and Pauloski, J Gregory and Chard, Ryan and Foster, Ian and Chard, Kyle},
     journal = {arXiv preprint arXiv:2409.16495},
     year = {2024}
   }

2. FGCS

   “QoS-aware edge AI placement and scheduling with multiple implementations in FaaS-based edge computing”

   Hudson, Nathaniel, Khamfroush, Hana,  Baughman, Matt and 3 more authors

   Future Generation Computer Systems 2024

   Abs Bib HTML

   Resource constraints on the computing continuum require that we make smart decisions for serving AI-based services at the network edge. AI-based services typically have multiple implementations (e.g., image classification implementations include SqueezeNet, DenseNet, and others) with varying trade-offs (e.g., latency and accuracy). The question then is how should AI-based services be placed across Function-as-a-Service (FaaS) based edge computing systems in order to maximize total Quality-of-Service (QoS). To address this question, we propose a problem that jointly aims to solve (i) edge AI service placement and (ii) request scheduling. These are done across two time-scales (one for placement and one for scheduling). We first cast the problem as an integer linear program. We then decompose the problem into separate placement and scheduling subproblems and prove that both are NP-hard. We then propose a novel placement algorithm that places services while considering device-to-device communication across edge clouds to offload requests to one another. Our results show that the proposed placement algorithm is able to outperform a state-of-the-art placement algorithm for AI-based services, and other baseline heuristics, with regard to maximizing total QoS. Additionally, we present a federated learning-based framework, FLIES, to predict the future incoming service requests and their QoS requirements. Our results also show that our FLIES algorithm is able to outperform a standard decentralized learning baseline for predicting incoming requests and show comparable predictive performance when compared to centralized training.

   @article{hudson2024qos,
     title = {QoS-aware edge AI placement and scheduling with multiple implementations in FaaS-based edge computing},
     journal = {Future Generation Computer Systems},
     volume = {157},
     pages = {250-263},
     year = {2024},
     issn = {0167-739X},
     doi = {https://doi.org/10.1016/j.future.2024.03.035},
     url = {https://www.sciencedirect.com/science/article/pii/S0167739X24001067},
     author = {Hudson, Nathaniel and Khamfroush, Hana and Baughman, Matt and Lucani, Daniel E. and Chard, Kyle and Foster, Ian},
     keywords = {Service placement, Federated learning, Serverless edge computing, Edge intelligence, Quality-of-service},
   }

3. 

   “Smart Edge-Enabled Traffic Light Control: Improving Reward-Communication Trade-offs with Federated Reinforcement Learning”

   Hudson, Nathaniel, Oza, Pratham,  Khamfroush, Hana and 1 more author

   In 2022 IEEE International Conference on Smart Computing (SMARTCOMP) Jul 2022

   Abs Bib PDF

   Traffic congestion is a costly phenomenon of every-day life. Reinforcement Learning (RL) is a promising solution due to its applicability to solving complex decision-making problems in highly dynamic environments. To train smart traffic lights using RL, large amounts of data is required. Recent RL-based approaches consider training to occur on some nearby server or a remote cloud server. However, this requires that traffic lights all communicate their raw data to some central location. For large road systems, communication cost can be impractical, particularly if traffic lights collect heavy data (e.g., video, LIDAR). As such, this work pushes training to the traffic lights directly to reduce communication cost. However, completely independent learning can reduce the performance of trained models. As such, this work considers the recent advent of Federated Reinforcement Learning (FedRL) for edge-enabled traffic lights so they can learn from each other’s experience by periodically aggregating locally-learned policy network parameters rather than share raw data, hence keeping communication costs low. To do this, we propose the SEAL framework which uses an intersection-agnostic representation to support FedRL across traffic lights controlling heterogeneous intersection types. We then evaluate our FedRL approach against Centralized and Decentralized RL strategies. We compare the reward-communication trade-offs of these strategies. Our results show that FedRL is able to reduce the communication costs associated with Centralized training by 36.24%; while only seeing a 2.11% decrease in average reward (i.e., decreased traffic congestion).

   @inproceedings{hudson2022smart,
     title = {Smart Edge-Enabled Traffic Light Control: Improving Reward-Communication Trade-offs with Federated Reinforcement Learning},
     author = {Hudson, Nathaniel and Oza, Pratham and Khamfroush, Hana and Thidapat, Chantem},
     booktitle = {2022 IEEE International Conference on Smart Computing (SMARTCOMP)},
     volume = {18},
     number = {6},
     pages = {403--404},
     month = jul,
     year = {2022},
     publisher = {Institute of Electrical and Electronics Engineers},
   }

4. ICCCN

   “QoS-Aware Placement of Deep Learning Services on the Edge with Multiple Service Implementations”

   Hudson, Nathaniel, Khamfroush, Hana,  and Lucani, Daniel E.

   In 2021 IEEE International Conference on Computer Communications and Networks (ICCCN) Big Data and Machine Learning for Networking (BDMLN) Workshop Jul 2021

   Abs arXiv Bib PDF

   Mobile edge computing pushes computationally-intensive services closer to the user to provide reduced delay due to physical proximity. This has led many to consider deploying deep learning models on the edge – commonly known as edge intelligence (EI). EI services can have many model implementations that provide different QoS. For instance, one model can perform inference faster than another (thus reducing latency) while achieving less accuracy when evaluated. In this paper, we study joint service placement and model scheduling of EI services with the goal to maximize Quality-of-Servcice (QoS) for end users where EI services have multiple implementations to serve user requests, each with varying costs and QoS benefits. We cast the problem as an integer linear program and prove that it is NP-hard. We then prove the objective is equivalent to maximizing a monotone increasing, submodular set function and thus can be solved greedily while maintaining a (1 – 1/e)-approximation guarantee. We then propose two greedy algorithms: one that theoretically guarantees this approximation and another that empirically matches its performance with greater efficiency. Finally, we thoroughly evaluate the proposed algorithm for making placement and scheduling decisions in both synthetic and real-world scenarios against the optimal solution and some baselines. In the real-world case, we consider real machine learning models using the ImageNet 2012 data-set for requests. Our numerical experiments empirically show that our more efficient greedy algorithm is able to approximate the optimal solution with a 0.904 approximation on average, while the next closest baseline achieves a 0.607 approximation on average.

   @inproceedings{hudson2021qos,
     title = {QoS-Aware Placement of Deep Learning Services on the Edge with Multiple Service Implementations},
     author = {Hudson, Nathaniel and Khamfroush, Hana and Lucani, Daniel E.},
     booktitle = {2021 IEEE International Conference on Computer Communications and Networks (ICCCN) Big Data and Machine Learning for Networking (BDMLN) Workshop},
     volume = {18},
     number = {6},
     pages = {403--404},
     year = {2021},
     publisher = {Institute of Electrical and Electronics Engineers},
   }

5. TNSE

   “Behavioral Information Diffusion for Opinion Maximization in Online Social Networks”

   Hudson, Nathaniel,  and Khamfroush, Hana

   IEEE Transactions on Network Science and Engineering (TNSE) Oct 2020

   Abs Bib HTML

   Online social networks provide a platform to diffuse information and influence people’s opinion. Conventional models for information diffusion do not take into account the specifics of each users’ personality, behavior, and their opinion. This work adopts the “Big Five” model from the social sciences to ascribe each user node with a personality. We propose a behavioral independent cascade (BIC) model that considers the personalities and opinions of user nodes when computing propagation probabilities for diffusion. We use this model to study the opinion maximization (OM) problem and prove it is NP-hard under our BIC model. Under the BIC model, we show that the objective function of the proposed OM problem is not submodular. We then propose an algorithm to solve the OM problem in linear-time based on a state-of-the-art influence maximization (IM) algorithm. We run extensive simulations under four cases where initial opinion is distributed in polarized/non-polarized and community/non-community cases. We find that when communities are polarized, activating a large number of nodes is ineffective towards maximizing opinion. Further, we find that our proposed algorithm outperforms state-of-the-art IM algorithms in terms of maximizing opinion in uniform opinion distribution-despite activating fewer nodes to be spreaders.

   @article{hudson2020behavioral,
     author = {Hudson, Nathaniel and Khamfroush, Hana},
     journal = {IEEE Transactions on Network Science and Engineering (TNSE)},
     title = {Behavioral Information Diffusion for Opinion Maximization in Online Social Networks},
     volume = {8},
     number = {2},
     publisher = {Institute of Electrical and Electronics Engineers},
     month = oct,
     year = {2020},
     doi = {10.1109/TNSE.2020.3034094},
     pages = {1259-1268},
     note = {Recipient of the 2021 Outstanding Student Paper award from the University of Kentucky Department of Computer Science.},
   }