Tootfinder

I always feel soooooo slow when I'm starting a new IaC project or doing a major refactor.
In this case there's a lot of orchestration that I'm the tying to leave room for future enhancements. Anyway it always takes long time
I'm re-working my lanecloud Ansible provisioning to better use netbox as a source of truth... And also switching to provisioning VMs with libvirt instead of nomad

Blockchain-Driven Federation for Distributed Edge Systems: Design and Experimental Validation
Adam Zahir, Milan Groshev, Carlos J. Bernardos, Antonio de la Oliva
https://arxiv.org/abs/2509.24846

Blockchain-Driven Federation for Distributed Edge Systems: Design and Experimental Validation
Edge computing brings computation near end users, enabling the provisioning of novel use cases. To satisfy end-user requirements, the concept of edge federation has recently emerged as a key mechanism for dynamic resources and services sharing across edge systems managed by different administrative domains. However, existing federation solutions often rely on pre-established agreements and face significant limitations, including operational complexity, delays caused by manual operations, high o…

The AI_INFN Platform: Artificial Intelligence Development in the Cloud
Lucio Anderlini, Giulio Bianchini, Diego Ciangottini, Stefano Dal Pra, Diego Michelotto, Rosa Petrini, Daniele Spiga
https://arxiv.org/abs/2509.22117

The AI_INFN Platform: Artificial Intelligence Development in the Cloud
Machine Learning (ML) is driving a revolution in the way scientists design, develop, and deploy data-intensive software. However, the adoption of ML presents new challenges for the computing infrastructure, particularly in terms of provisioning and orchestrating access to hardware accelerators for development, testing, and production. The INFN-funded project AI_INFN (Artificial Intelligence at INFN) aims at fostering the adoption of ML techniques within INFN use cases by providing support on mu…

Multiple STAR-RISs-Empowered Multi-User Communications with Diversified QoS Provisioning
Junfeng Wang, Xiao Tang, Jinxin Liu, Zhi Zhai, Qinghe Du, Naijin Liu
https://arxiv.org/abs/2508.17769

Multiple STAR-RISs-Empowered Multi-User Communications with Diversified QoS Provisioning
This paper proposes a quality-of-service (QoS)-aware multi-user communication framework facilitated by multiple simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs). The user groups are established based on their QoS requirements specified by the minimum data rate, which is provisioned by the optimized transmission and reflection configurations of the STAR-RISs. Particularly, we formulate an optimization problem to maximize the aggregate link rate across al…

well I managed to add the datastore and multi tenancy plumbing for lanecloud v2. Basically some custom fields and better lookups with netbox...
I'll have to do another pass on the VM registration playbook too.. but the provisioning playbook works great so far.

Having a meeting going over a "IT Resource Provisioning Guidelines" document.
Feeling a very strong urge to play a gummy game. Or scream. The extent of bikeshedding is painful.
It's especially annoying how the bullet points which came from somewhere generic really do not work for a team of our scale with the sorts of systems we manage. e.g. on most systems the only network-login-capable accounts are sysadmins, most of whom rarely log in but all of whom need work…

Introducing Dev Services for #SpringBoot using #Arconia by @…

Towards Reliable Service Provisioning for Dynamic UAV Clusters in Low-Altitude Economy Networks
Yanwei Gong, Ruichen Zhang, Xiaoqing Wang, Xiaolin Chang, Bo Ai, Junchao Fan, Bocheng Ju, Dusit Niyato
https://arxiv.org/abs/2509.06112

Towards Reliable Service Provisioning for Dynamic UAV Clusters in Low-Altitude Economy Networks
Unmanned Aerial Vehicle (UAV) cluster services are crucial for promoting the low-altitude economy by enabling scalable, flexible, and adaptive aerial networks. To meet diverse service demands, clusters must dynamically incorporate a New UAVs (NUAVs) or an Existing UAV (EUAV). However, achieving sustained service reliability remains challenging due to the need for efficient and scalable NUAV authentication, privacy-preserving cross-cluster authentication for EUAVs, and robust protection of the c…

QoS-based Intelligent multi-connectivity for B5G networks
Ali Parsa, Neda Moghim, Sachin Shetty
https://arxiv.org/abs/2508.16816 https://arxiv.org/pdf/2508…

QoS-based Intelligent multi-connectivity for B5G networks
The rapid advancement of communication technologies has established cellular networks as the backbone for diverse applications, each with distinct quality of service requirements. Meeting these varying demands within a unified infrastructure presents a critical challenge that can be addressed through advanced techniques such as multi-connectivity. Multiconnectivity enables User equipments to connect to multiple BSs simultaneously, facilitating QoS differentiation and provisioning. This paper pr…

Intelligent Edge Resource Provisioning for Scalable Digital Twins of Autonomous Vehicles
Mohammad Sajid Shahriar, Suresh Subramaniam, Motoharu Matsuura, Hiroshi Hasegawa, Shih-Chun Lin
https://arxiv.org/abs/2508.11574

Intelligent Edge Resource Provisioning for Scalable Digital Twins of Autonomous Vehicles
The next generation networks offers significant potential to advance Intelligent Transportation Systems (ITS), particularly through the integration of Digital Twins (DTs). However, ensuring the uninterrupted operation of DTs through efficient computing resource management remains an open challenge. This paper introduces a distributed computing archi tecture that integrates DTs and Mobile Edge Computing (MEC) within a software-defined vehicular networking framework to enable intelligent, low-lat…

Artificial Intelligence for Cost-Aware Resource Prediction in Big Data Pipelines
Harshit Goyal
https://arxiv.org/abs/2510.05127 https://arxiv.org/pdf/2510.…

Artificial Intelligence for Cost-Aware Resource Prediction in Big Data Pipelines
Efficient resource allocation is a key challenge in modern cloud computing. Over-provisioning leads to unnecessary costs, while under-provisioning risks performance degradation and SLA violations. This work presents an artificial intelligence approach to predict resource utilization in big data pipelines using Random Forest regression. We preprocess the Google Borg cluster traces to clean, transform, and extract relevant features (CPU, memory, usage distributions). The model achieves high predi…

Hierarchical Placement Learning for Network Slice Provisioning
Jesutofunmi Ajayi, Antonio Di Maio, Torsten Braun
https://arxiv.org/abs/2508.06432 https://a…

Hierarchical Placement Learning for Network Slice Provisioning
In this work, we aim to address the challenge of slice provisioning in edge-based mobile networks. We propose a solution that learns a service function chain placement policy for Network Slice Requests, to maximize the request acceptance rate, while minimizing the average node resource utilization. To do this, we consider a Hierarchical Multi-Armed Bandit problem and propose a two-level hierarchical bandit solution which aims to learn a scalable placement policy that optimizes the stated object…

Games Are Not Equal: Classifying Cloud Gaming Contexts for Effective User Experience Measurement
Yifan Wang, Minzhao Lyu, Vijay Sivaraman
https://arxiv.org/abs/2509.19669 https:…

Games Are Not Equal: Classifying Cloud Gaming Contexts for Effective User Experience Measurement
To tap into the growing market of cloud gaming, whereby game graphics is rendered in the cloud and streamed back to the user as a video feed, network operators are creating monetizable assurance services that dynamically provision network resources. However, without accurately measuring cloud gaming user experience, they cannot assess the effectiveness of their provisioning methods. Basic measures such as bandwidth and frame rate by themselves do not suffice, and can only be interpreted in the …

GPU Programming for AI Workflow Development on AWS SageMaker: An Instructional Approach
Sriram Srinivasan, Hamdan Alabsi, Rand Obeidat, Nithisha Ponnala, Azene Zenebe
https://arxiv.org/abs/2509.13703

GPU Programming for AI Workflow Development on AWS SageMaker: An Instructional Approach
We present the design, implementation, and comprehensive evaluation of a specialized course on GPU architecture, GPU programming, and how these are used for developing AI agents. This course is offered to undergraduate and graduate students during Fall 2024 and Spring 2025. The course began with foundational concepts in GPU/CPU hardware and parallel computing and progressed to develop RAG and optimizing them using GPUs. Students gained experience provisioning and configuring cloud-based GPU ins…

Towards a Decentralized IoT Onboarding for Smart Homes Using Consortium Blockchain
Narges Dadkhah, Khan Reaz, Gerhard Wunder
https://arxiv.org/abs/2508.21480 https://

Towards a Decentralized IoT Onboarding for Smart Homes Using Consortium Blockchain
The increasing adoption of smart home devices and IoT-based security systems presents significant opportunities to enhance convenience, safety, and risk management for homeowners and service providers. However, secure onboarding-provisioning credentials and establishing trust with cloud platforms-remains a considerable challenge. Traditional onboarding methods often rely on centralized Public Key Infrastructure (PKI) models and manufacturer-controlled keys, which introduce security risks and li…

Towards Energy-Efficient Serverless Computing with Hardware Isolation
Natalie Carl, Tobias Pfandzelter, David Bermbach
https://arxiv.org/abs/2510.08180 https://

Towards Energy-Efficient Serverless Computing with Hardware Isolation
Serverless computing provides just-in-time infrastructure provisioning with rapid elasticity and a finely-grained pricing model. As full control of resource allocation is in the hands of the cloud provider and applications only consume resources when they actually perform work, we believe that serverless computing is uniquely positioned to maximize energy efficiency. However, the focus of current serverless platforms is to run hundreds or thousands of serverless functions from different tenan…

Taming Cold Starts: Proactive Serverless Scheduling with Model Predictive Control
Chanh Nguyen, Monowar Bhuyan, Erik Elmroth
https://arxiv.org/abs/2508.07640 https://

Taming Cold Starts: Proactive Serverless Scheduling with Model Predictive Control
Serverless computing has transformed cloud application deployment by introducing a fine-grained, event-driven execution model that abstracts away infrastructure management. Its on-demand nature makes it especially appealing for latency-sensitive and bursty workloads. However, the cold start problem, i.e., where the platform incurs significant delay when provisioning new containers, remains the Achilles' heel of such platforms. This paper presents a predictive serverless scheduling framework b…

QoE-Aware Service Provision for Mobile AR Rendering: An Agent-Driven Approach
Conghao Zhou, Lulu Sun, Xiucheng Wang, Peng Yang, Feng Lyu, Sihan Lu, Xuemin Shen
https://arxiv.org/abs/2508.08627

QoE-Aware Service Provision for Mobile AR Rendering: An Agent-Driven Approach
Mobile augmented reality (MAR) is envisioned as a key immersive application in 6G, enabling virtual content rendering aligned with the physical environment through device pose estimation. In this paper, we propose a novel agent-driven communication service provisioning approach for edge-assisted MAR, aiming to reduce communication overhead between MAR devices and the edge server while ensuring the quality of experience (QoE). First, to address the inaccessibility of MAR application-specific inf…

Block: Balancing Load in LLM Serving with Context, Knowledge and Predictive Scheduling
Wei Da, Evangelia Kalyvianaki
https://arxiv.org/abs/2508.03611 https://

Block: Balancing Load in LLM Serving with Context, Knowledge and Predictive Scheduling
This paper presents Block, a distributed scheduling framework designed to optimize load balancing and auto-provisioning across instances in large language model serving frameworks by leveraging contextual information from incoming requests. Unlike popular model serving systems that rely on monolithic and heuristic task schedulers, Block operates as a fully distributed, stateless, and predictive scheduling system to achieve low overhead, reliability, and scalability. It leverages the determinist…

An Online Multi-dimensional Knapsack Approach for Slice Admission Control
Jesutofunmi Ajayi, Antonio Di Maio, Torsten Braun, Dimitrios Xenakis
https://arxiv.org/abs/2508.06468 h…

An Online Multi-dimensional Knapsack Approach for Slice Admission Control
Network Slicing has emerged as a powerful technique to enable cost-effective, multi-tenant communications and services over a shared physical mobile network infrastructure. One major challenge of service provisioning in slice-enabled networks is the uncertainty in the demand for the limited network resources that must be shared among existing slices and potentially new Network Slice Requests. In this paper, we consider admission control of Network Slice Requests in an online setting, with the g…

ElasticMoE: An Efficient Auto Scaling Method for Mixture-of-Experts Models
Gursimran Singh (Huawei Technologies Canada), Timothy Yu (Huawei Technologies Canada), Haley Li (Huawei Technologies Canada), Cheng Chen (Huawei Technologies Canada), Hanieh Sadri (Huawei Technologies Canada), Qintao Zhang (Huawei Technologies China), Yu Zhang (Huawei Technologies China), Ying Xiong (Huawei Technologies Canada), Yong Zhang (Huawei Technologies Canada), Zhenan Fan (Huawei Technologies Canada)

ElasticMoE: An Efficient Auto Scaling Method for Mixture-of-Experts Models
Mixture-of-Experts (MoE) models promise efficient scaling of large language models (LLMs) by activating only a small subset of experts per token, but their parallelized inference pipelines make elastic serving challenging. Existing strategies fall short: horizontal scaling provisions entire replicas of the current configuration, often tens to hundreds of accelerators, leading to coarse granularity, long provisioning delays, and costly overprovisioning. Vertical scaling offers finer adjustments …

A QoS Framework for Service Provision in Multi-Infrastructure-Sharing Networks
Quang Minh Nguyen, Eytan Modiano
https://arxiv.org/abs/2509.01694 https://ar…

A QoS Framework for Service Provision in Multi-Infrastructure-Sharing Networks
We propose a framework for resource provisioning with QoS guarantees in shared infrastructure networks. Our novel framework provides tunable probabilistic service guarantees for throughput and delay. Key to our approach is a Modified Dirft-plus-Penalty (MDP) policy that ensures long-term stability while capturing short-term probabilistic service guarantees using linearized upper-confidence bounds. We characterize the feasible region of service guarantees and show that our MDP procedure achieves…

Wireless Laser Power Transfer for Low-altitude Uncrewed Aerial Vehicle-assisted Internet of Things: Paradigms, Challenges, and Solutions
Chengzhen Li, Likun Zhang, Chuang Zhang, Jiahui Li, Changyuan Zhao, Ruichen Zhang, Geng Sun
https://arxiv.org/abs/2510.00477

Wireless Laser Power Transfer for Low-altitude Uncrewed Aerial Vehicle-assisted Internet of Things: Paradigms, Challenges, and Solutions
Low-altitude uncrewed aerial vehicles (UAVs) have become integral enablers for the Internet of Things (IoT) by offering enhanced coverage, improved connectivity and access to remote areas. A critical challenge limiting their operational capacity lies in the energy constraints of both aerial platforms and ground-based sensors. This paper explores WLPT as a transformative solution for sustainable energy provisioning in UAV-assisted IoT networks. We first systematically investigate the fundamental…