Tootfinder

Sources: Oracle is in discussions with Meta to provide computing power for training and deploying AI models, in a deal worth about $20B (Bloomberg)
https://www.bloomberg.com/news/articles/2025-09-19/oracle-in-talks-with-meta-…

Oracle in Talks With Meta on $20 Billion AI Cloud Computing Deal
Oracle Corp. is in discussions with Meta Platforms Inc. for a cloud computing deal worth about $20 billion, providing further evidence that the company has become a significant infrastructure provider.

Application Placement with Constraint Relaxation
Damiano Azzolini, Marco Duca, Stefano Forti, Francesco Gallo, Antonio Ielo
https://arxiv.org/abs/2507.13895

Application Placement with Constraint Relaxation
Novel utility computing paradigms rely upon the deployment of multi-service applications to pervasive and highly distributed cloud-edge infrastructure resources. Deciding onto which computational nodes to place services in cloud-edge networks, as per their functional and non-functional constraints, can be formulated as a combinatorial optimisation problem. Most existing solutions in this space are not able to deal with \emph{unsatisfiable} problem instances, nor preferences, i.e. requirements t…

Nice,
"Europe Launches Its First Federated AI Factory Based on a Sovereign Multi-Cloud Infrastructure"
#ai #cloud #eu

Sources: Meta has poached Frank Chu, an Apple exec who led AI teams focused on cloud infrastructure, training and search, despite Meta's plans to slow hiring (Mark Gurman/Bloomberg)
https://www.bloomberg.com/news/articles/20

#European #Cloud Modules
https://berthub.eu/articles/posts/european-cloud-modules/…

So I had this terrible idea a few years ago to write some infrastructure automation that provisions a new compute instance, sets up secrets storage, configures IAM roles, authorizes the new instance to be able to provision new instance and roles via infra-as-code automation, and then the new instance tears down the instance and roles that created it, before then creating its own new compute instance, etc
Like a self-propagating glider in Conway's Game of Life, except with cloud inf…

Responding to the growing demand from organisations worldwide to reduce their dependency on US cloud providers, German software giant SAP has announced plans to invest more than €20 billion over the next decade in sovereign cloud infrastructure, research and development and personnel.

SAP promises €20bn+ to expand its sovereign cloud services
Responding to the growing demand from organisations worldwide to reduce their dependency on US cloud providers, German software giant SAP has announced plans to invest ...

Multi-IaC-Eval: Benchmarking Cloud Infrastructure as Code Across Multiple Formats
Sam Davidson, Li Sun, Bhavana Bhasker, Laurent Callot, Anoop Deoras
https://arxiv.org/abs/2509.05303

Multi-IaC-Eval: Benchmarking Cloud Infrastructure as Code Across Multiple Formats
Infrastructure as Code (IaC) is fundamental to modern cloud computing, enabling teams to define and manage infrastructure through machine-readable configuration files. However, different cloud service providers utilize diverse IaC formats. The lack of a standardized format requires cloud architects to be proficient in multiple IaC languages, adding complexity to cloud deployment. While Large Language Models (LLMs) show promise in automating IaC creation and maintenance, progress has been limite…

Helsinki-based DataCrunch, which aims to become Europe's first AI cloud hyperscaler, raised a €55M Series A, bringing its total funding to €76.5M (Tamara Djurickovic/Tech.eu)
https://tech.eu/2025/09/08/datacrunch-raises-eur5…

DataCrunch raises €55M to boost EU AI sovereignty with green cloud infrastructure
Helsinki-based DataCrunch, a provider of high-performance AI infrastructure, has raised €55 million in a Series A round to scale its compute platform and advance its ambition to become Europe’s first AI cloud hyperscaler.The round was led by byFounders, Skaala, Varma pension fund, and Tesi, with participation from J12 Ventures and angel investors. Debt financing came from Nordea and Armada Credit Partners, alongside Danske Bank, Norion Bank, and Local Tapiola. With this raise, DataCru…

I continue to be amazed that anyone uses Amazon AWS, or Microsoft Azure, or Google Cloud Services. Only someone who's very happy to completely lock their business into a 3rd party proprietary stack they don't control, and loves paying 20x (2000%!) more than reasonable commodity infrastructure rates would do so. I'm amazed that these businesses are even viable. Just goes to show how much flab most corporations have. They're ripe for disruption and displacement.

Learning-Enabled Adaptive Power Capping Scheme for Cloud Data Centers
Yimeng Sun, Zhaohao Ding, Payman Dehghanian, Fei Teng
https://arxiv.org/abs/2508.06994 https://

Learning-Enabled Adaptive Power Capping Scheme for Cloud Data Centers
The rapid growth of the digital economy and artificial intelligence has transformed cloud data centers into essential infrastructure with substantial energy consumption and carbon emission, necessitating effective energy management. However, existing methods face challenges such as incomplete information, uncertain parameters, and dynamic environments, which hinder their real-world implementation. This paper proposes an adaptive power capping framework tailored to cloud data centers. By dynamic…

A Dynamic Approach to Load Balancing in Cloud Infrastructure: Enhancing Energy Efficiency and Resource Utilization
Shadman Sakib, Ajay Katangur, Rahul Dubey
https://arxiv.org/abs/2508.05821

A Dynamic Approach to Load Balancing in Cloud Infrastructure: Enhancing Energy Efficiency and Resource Utilization
Cloud computing has grown rapidly in recent years, mainly due to the sharp increase in data transferred over the internet. This growth makes load balancing a key part of cloud systems, as it helps distribute user requests across servers to maintain performance, prevent overload, and ensure a smooth user experience. Despite its importance, managing server resources and keeping workloads balanced over time remains a major challenge in cloud environments. This paper introduces a novel Score-Based …

Google plans to spend $9B in Oklahoma over two years on cloud and AI infrastructure, including a new data center campus and an expansion of its Pryor facility (Jaspreet Singh/Reuters)
https://www.reuters.com/business/google-pledge…

Comparative Studies: Cloud-Enabled Adaptive Learning System for Scalable Education in Sub-Saharan
Israel Fianyi, Soonja Yeom, Ju-Hyun Shin
https://arxiv.org/abs/2506.23851

Comparative Studies: Cloud-Enabled Adaptive Learning System for Scalable Education in Sub-Saharan
The integration of cloud computing in education can revolutionise learning in advanced (Australia & South Korea) and middle-income (Ghana & Nigeria) countries, while offering scalable, cost-effective and equitable access to adaptive learning systems. This paper explores how cloud computing and adaptive learning technologies are deployed across different socio-economic and infrastructure contexts. The study identifies enabling factors and systematic challenges, providing insights into how cloud-…

A Survey on Task Scheduling in Carbon-Aware Container Orchestration
Jialin Yang, Zainab Saad, Jiajun Wu, Xiaoguang Niu, Henry Leung, Steve Drew
https://arxiv.org/abs/2508.05949 …

A Survey on Task Scheduling in Carbon-Aware Container Orchestration
The soaring energy demands of large-scale software ecosystems and cloud data centers, accelerated by the intensive training and deployment of large language models, have driven energy consumption and carbon footprint to unprecedented levels. In response, both industry and academia are increasing efforts to reduce the carbon emissions associated with cloud computing through more efficient task scheduling and infrastructure orchestration. In this work, we present a systematic review of various Ku…

3. Multiple Access Methods 🌐
• Cloud API: https://platform.moonshot.ai (OpenAI/Anthropic compatible) • Self-hosting: Deploy on your own infrastructure • Inference engines: vLLM, SGLang, KTransformers, TensorRT-LLM supported

Energy-Aware Data Center Management: A Sustainable Approach to Reducing Carbon Footprint
Rabab Khan Rongon, Krishna Das
https://arxiv.org/abs/2509.10462 https://

Energy-Aware Data Center Management: A Sustainable Approach to Reducing Carbon Footprint
The rapid expansion of cloud computing and data center infrastructure has led to significant energy consumption, posing environmental challenges due to the growing carbon footprint. This research explores energy-aware management strategies aimed at creating sustainable data center operations. By integrating advanced energy-efficient technologies and optimizing resource utilization, this study proposes a framework to minimize power usage while maintaining high performance. Key elements include d…

On The Road - To Xi’An/ Silhouette 👤
在路上 - 去西安/ 轮廓 👤
📷 Pentax MX
🎞️Lucky SHD 400
#filmphotography #Photography #blackandwhite

Cross-Service Token: Finding Attacks in 5G Core Networks
Anqi Chen, Riccardo Preatoni, Alessandro Brighente, Mauro Conti, Cristina Nita-Rotaru
https://arxiv.org/abs/2509.08992 h…

Cross-Service Token: Finding Attacks in 5G Core Networks
5G marks a major departure from previous cellular architectures, by transitioning from a monolithic design of the core network to a Service-Based Architecture (SBA) where services are modularized as Network Functions (NFs) which communicate with each other via standard-defined HTTP-based APIs called Service-Based Interfaces (SBIs). These NFs are deployed in private and public cloud infrastructure, and an access control framework based on OAuth restricts how they communicate with each other and …

Accelerating Edge Inference for Distributed MoE Models with Latency-Optimized Expert Placement
Tian Wu, Liming Wang, Zijian Wen, Xiaoxi Zhang, Jingpu Duan, Xianwei Zhang, Jinhang Zuo
https://arxiv.org/abs/2508.12851

Accelerating Edge Inference for Distributed MoE Models with Latency-Optimized Expert Placement
Mixture-of-Experts (MoE) have become a cornerstone for training and scaling large language models (LLMs), offering substantial gains in model capacity and efficiency through sparse expert activation. However, serving these models remains challenging in practice, particularly in resource-constrained edge environments, due to their large memory footprint and complex communication demands. While centralized cloud inference is common, it incurs high infrastructure costs, along with latency and priv…

Towards On-Device Personalization: Cloud-device Collaborative Data Augmentation for Efficient On-device Language Model
Zhaofeng Zhong, Wei Yuan, Liang Qu, Tong Chen, Hao Wang, Xiangyu Zhao, Hongzhi Yin
https://arxiv.org/abs/2508.21313

Towards On-Device Personalization: Cloud-device Collaborative Data Augmentation for Efficient On-device Language Model
With the advancement of large language models (LLMs), significant progress has been achieved in various Natural Language Processing (NLP) tasks. However, existing LLMs still face two major challenges that hinder their broader adoption: (1) their responses tend to be generic and lack personalization tailored to individual users, and (2) they rely heavily on cloud infrastructure due to intensive computational requirements, leading to stable network dependency and response delay. Recent research h…

Looks like EU funding of #FLOSS will continue: "Open digital infrastructure, from search and identity to cloud and software governance, is now a strategic European priority."

NGI Forum 2025 marks a strategic shift from Next Generation Internet toward the Open Internet Stack
The Next Generation Internet (NGI) Forum 2025, the flagship event of the European Commission’s NGI initiative, took place in Brussels on 19-20 June, with over 200 on-site participants and many more via web stream. The Forum focused on accelerating the shift from research and experimentation to the deployment of sovereign, open digital infrastructures.

M$^2$-MFP: A Multi-Scale and Multi-Level Memory Failure Prediction Framework for Reliable Cloud Infrastructure
Hongyi Xie, Min Zhou, Qiao Yu, Jialiang Yu, Zhenli Sheng, Hong Xie, Defu Lian
https://arxiv.org/abs/2507.07144

M$^2$-MFP: A Multi-Scale and Multi-Level Memory Failure Prediction Framework for Reliable Cloud Infrastructure
As cloud services become increasingly integral to modern IT infrastructure, ensuring hardware reliability is essential to sustain high-quality service. Memory failures pose a significant threat to overall system stability, making accurate failure prediction through the analysis of memory error logs (i.e., Correctable Errors) imperative. Existing memory failure prediction approaches have notable limitations: rule-based expert models suffer from limited generalizability and low recall rates, whil…

E2B, formerly FoundryLabs, which is developing an open-source, sandboxed cloud infrastructure for AI agents, raised a $21M Series A led by Insight Partners (Mike Wheatley/SiliconANGLE)
https://siliconangle.com/2025/07/28/e2

E2B shares its vision of sandboxed, cloud environments for every AI agent after raising $21M in funding - SiliconANGLE
E2B shares its vision of sandboxed, cloud environments for every AI agent after raising $21M in funding - SiliconANGLE

"The Seven Capital Sins of Open Science"
1. Worshiping the 'age factor'
2. Ignoring the value of data reuse and complexity
3. Disrespecting other disciplines
4. Publishing data without a supplementary paper
5. Creating and maintaining a nightmare for machines
6. Refusing to support investment in general infrastructure
7. Creating data without a FAIR and explicit data stewardship plan.

The Seven Capital Sins of Open Science
Years ago I made a set of cartoons depicting what I consider the seven major roadblocks to effectively realise an Open Science (rather FAIR science actually). These were distilled from my intensive work on the European Open Science Cloud, GO FAIR and also my presidency of CODATA.

RoCE BALBOA: Service-enhanced Data Center RDMA for SmartNICs
Maximilian Jakob Heer, Benjamin Ramhorst, Yu Zhu, Luhao Liu, Zhiyi Hu, Jonas Dann, Gustavo Alonso
https://arxiv.org/abs/2507.20412

RoCE BALBOA: Service-enhanced Data Center RDMA for SmartNICs
Data-intensive applications in data centers, especially machine learning (ML), have made the network a bottleneck, which in turn has motivated the development of more efficient network protocols and infrastructure. For instance, remote direct memory access (RDMA) has become the standard protocol for data transport in the cloud as it minimizes data copies and reduces CPU-utilization via host-bypassing. Similarly, an increasing amount of network functions and infrastructure have moved to accelera…

A Fragmentation-Aware Adaptive Bilevel Search Framework for Service Mapping in Computing Power Networks
Jingzhao Xie, Zhenglian Li, Gang Sun, Long Luo, Hongfang Yu, Dusit Niyato
https://arxiv.org/abs/2507.07535

A Fragmentation-Aware Adaptive Bilevel Search Framework for Service Mapping in Computing Power Networks
Computing Power Network (CPN) unifies wide-area computing resources through coordinated network control, while cloud-native abstractions enable flexible resource orchestration and on-demand service provisioning atop the elastic infrastructure CPN provides. However, current approaches fall short of fully integrating computing resources via network-enabled coordination as envisioned by CPN. In particular, optimally mapping services to an underlying infrastructure to maximize resource efficiency a…

From Cloud-Native to Trust-Native: A Protocol for Verifiable Multi-Agent Systems
Muyang Li
https://arxiv.org/abs/2507.22077 https://arxiv.org/pdf/2507.2207…

From Cloud-Native to Trust-Native: A Protocol for Verifiable Multi-Agent Systems
As autonomous agents powered by large language models (LLMs) proliferate in high-stakes domains -- from pharmaceuticals to legal workflows -- the challenge is no longer just intelligence, but verifiability. We introduce TrustTrack, a protocol that embeds structural guarantees -- verifiable identity, policy commitments, and tamper-resistant behavioral logs -- directly into agent infrastructure. This enables a new systems paradigm: trust-native autonomy. By treating compliance as a design constra…

Composable OS Kernel Architectures for Autonomous Intelligence
Rajpreet Singh, Vidhi Kothari
https://arxiv.org/abs/2508.00604 https://arxiv.org/pdf/2508.00…

Composable OS Kernel Architectures for Autonomous Intelligence
As intelligent systems permeate edge devices, cloud infrastructure, and embedded real-time environments, this research proposes a new OS kernel architecture for intelligent systems, transforming kernels from static resource managers to adaptive, AI-integrated platforms. Key contributions include: (1) treating Loadable Kernel Modules (LKMs) as AI-oriented computation units for fast sensory and cognitive processing in kernel space; (2) expanding the Linux kernel into an AI-native environment with…

Towards System-Level Quantum-Accelerator Integration
Ralf Ramsauer, Wolfgang Mauerer
https://arxiv.org/abs/2507.19212 https://arxiv.org/pdf/2507.19212

Towards System-Level Quantum-Accelerator Integration
Quantum computers are often treated as experimental add-ons that are loosely coupled to classical infrastructure through high-level interpreted languages and cloud-like orchestration. However, future deployments in both, high-performance computing (HPC) and embedded environments, will demand tighter integration for lower latencies, stronger determinism, and architectural consistency, as well as to implement error correction and other tasks that require tight quantum-classical interaction as gen…

Rigid Body Localization and Tracking for 6G V2X: Algorithms, Applications, and Road to Adoption
Niclas F\"uhrling, Hyeon Seok Rou, Giuseppe Thadeu Freitas de Abreu, David Gonz\'alez G., Gonzalo Seco-Granados, Osvaldo Gonsa
https://arxiv.org/abs/2509.01208

Rigid Body Localization and Tracking for 6G V2X: Algorithms, Applications, and Road to Adoption
Vehicle-to-everything (V2X) perception refers to a suite of technologies that empower vehicles to sense their environment and communicate with other entities, including surrounding vehicles, infrastructure, and cloud/edge networks. With the growing demands of autonomous driving, V2X perception has gained significant attention, particularly through the emergence of integrated sensing and communication (ISAC) frameworks. Within this landscape, rigid body localization (RBL) has emerged as a promis…

Over-the-Top Resource Broker System for Split Computing: An Approach to Distribute Cloud Computing Infrastructure
Ingo Friese, Jochen Klaffer, Mandy Galkow-Schneider, Sergiy Melnyk, Qiuheng Zhou, Hans Dieter Schotten
https://arxiv.org/abs/2508.07744

Over-the-Top Resource Broker System for Split Computing: An Approach to Distribute Cloud Computing Infrastructure
6G network architectures will usher in a wave of innovative services and capabilities, introducing concepts like split computing and dynamic processing nodes. This implicates a paradigm where accessing resources seamlessly aligns with diverse processing node characteristics, ensuring a uniform interface. In this landscape, the identity of the operator becomes inconsequential, paving the way for a collaborative ecosystem where multiple providers contribute to a shared pool of resources. At the c…

Distilling On-device Language Models for Robot Planning with Minimal Human Intervention
Zachary Ravichandran, Ignacio Hounie, Fernando Cladera, Alejandro Ribeiro, George J. Pappas, Vijay Kumar
https://arxiv.org/abs/2506.17486

Distilling On-device Language Models for Robot Planning with Minimal Human Intervention
Large language models (LLMs) provide robots with powerful contextual reasoning abilities and a natural human interface. Yet, current LLM-enabled robots typically depend on cloud-hosted models, limiting their usability in environments with unreliable communication infrastructure, such as outdoor or industrial settings. We present PRISM, a framework for distilling small language model (SLM)-enabled robot planners that run on-device with minimal human supervision. Starting from an existing LLM-ena…

Bridging Cloud Convenience and Protocol Transparency: A Hybrid Architecture for Ethereum Node Operations on Amazon Managed Blockchain
S M Mostaq Hossain, Amani Altarawneh, Maanak Gupta
https://arxiv.org/abs/2507.18774

Bridging Cloud Convenience and Protocol Transparency: A Hybrid Architecture for Ethereum Node Operations on Amazon Managed Blockchain
As blockchain technologies are increasingly adopted in enterprise and research domains, the need for secure, scalable, and performance-transparent node infrastructure has become critical. While self-hosted Ethereum nodes offer operational control, they often lack elasticity and require complex maintenance. This paper presents a hybrid, service-oriented architecture for deploying and monitoring Ethereum full nodes using Amazon Managed Blockchain (AMB), integrated with EC2-based observability, IA…

Microsoft and the war in Gaza.
Thanks to the control it exerts over Palestinian telecommunications infrastructure, Israel has long intercepted phone calls in the occupied territories. But the indiscriminate new systemin the Azure cloud of Microsoft allows Israeli intelligence officers to play back the content of cellular calls made by Palestinians, capturing the conversations of a much larger pool of ordinary civilians.
https://www.theguardian.com/world/2025/aug/06/microsoft-israeli-military-palestinian-phone-calls-cloud?CMP=Share_iOSApp_Other

Google plans to invest an additional $9B in Virginia through 2026 to boost its cloud and AI infrastructure, including a new data center in Chesterfield County (Emily Forgash/Bloomberg)
https://www.bloomberg.com/news/articles/20

Google to Invest Additional $9 Billion in Virginia Data Centers
Alphabet Inc.’s Google is investing an additional $9 billion in Virginia through 2026 to enhance cloud and AI infrastructure across the state, marking the latest in a series of big tech investments in US data centers.

OpenLambdaVerse: A Dataset and Analysis of Open-Source Serverless Applications
Angel C. Chavez-Moreno, Cristina L. Abad
https://arxiv.org/abs/2508.01492 https://

OpenLambdaVerse: A Dataset and Analysis of Open-Source Serverless Applications
Function-as-a-Service (FaaS) is at the core of serverless computing, enabling developers to easily deploy applications without managing computing resources. With an Infrastructure-as-Code (IaC) approach, frameworks like the Serverless Framework use YAML configurations to define and deploy APIs, tasks, workflows, and event-driven applications on cloud providers, promoting zero-friction development. As with any rapidly evolving ecosystem, there is a need for updated insights into how these tools …

Offloading tracing for real-time systems using a scalable cloud infrastructure
David Jannis Schmidt, Grigory Fridman, Florian von Zabiensky
https://arxiv.org/abs/2507.19953 http…

Offloading tracing for real-time systems using a scalable cloud infrastructure
Real-time embedded systems require precise timing and fault detection to ensure correct behavior. Traditional tracing tools often rely on local desktops with limited processing and storage capabilities, which hampers large-scale analysis. This paper presents a scalable, cloud-based architecture for software tracing in real-time systems based on microservices and edge computing. Our approach shifts the trace processing workload from the developer's machine to the cloud, using a dedicated tracing…

Locked In, Leaked Out: Measuring Isolation via Kernel Locks
Anjali, Michael M. Swift
https://arxiv.org/abs/2507.21248 https://arxiv.org/pdf/2507.21248

Locked In, Leaked Out: Measuring Isolation via Kernel Locks
Isolation is a critical property for shared infrastructure to limit exposure and interference among simultaneous running workloads. Cloud providers use different isolation mechanisms such as full Virtual Machines, microVMs, Linux containers, secure containers, etc., to confine workloads running in a multi-tenant environment. We propose a novel way to understand and measure performance interference and isolation at the system software layer that occurs due to shared access to data structures. …

A deep dive into Oracle's data center strategy and its emergence as an AI computing powerhouse, amid questions about the sustainability of the GPU business (SemiAnalysis)
https://semianalysis.com/2025/06/30/how-oracle-is-winning-the-ai-compute-…

How Oracle Is Winning the AI Compute Market
Oracle’s Cloud Infrastructure business is firing on all cylinders and is greatly outpacing expectations. All eyes are on the high-profile Stargate JV and the massive Abilene, Texas datacenter…

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…

NYC-based Carbyne, which offers cloud-based tools for emergency services, raised $100M from AT&T Ventures, Axon Enterprises, Cox Enterprises, and others (Meir Orbach/CTech)
https://www.calcalistech.com/ctechnews/article/skhy4rdvgg

Carbyne raises $100 million to modernize emergency call infrastructure
With deployment in 23 U.S. states, the company is reshaping the way dispatchers respond to crisis situations.

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…

SuperSONIC: Cloud-Native Infrastructure for ML Inferencing
Dmitry Kondratyev, Benedikt Riedel, Yuan-Tang Chou, Miles Cochran-Branson, Noah Paladino, David Schultz, Mia Liu, Javier Duarte, Philip Harris, Shih-Chieh Hsu
https://arxiv.org/abs/2506.20657

SuperSONIC: Cloud-Native Infrastructure for ML Inferencing
The increasing computational demand from growing data rates and complex machine learning (ML) algorithms in large-scale scientific experiments has driven the adoption of the Services for Optimized Network Inference on Coprocessors (SONIC) approach. SONIC accelerates ML inference by offloading it to local or remote coprocessors to optimize resource utilization. Leveraging its portability to different types of coprocessors, SONIC enhances data processing and model deployment efficiency for cuttin…

Rethinking Denial-of-Service: A Conditional Taxonomy Unifying Availability and Sustainability Threats
Mark Dorsett, Scott Man, Tim Koussas
https://arxiv.org/abs/2508.19283 https…

Rethinking Denial-of-Service: A Conditional Taxonomy Unifying Availability and Sustainability Threats
This paper proposes a unified, condition-based framework for classifying both legacy and cloud-era denial-of-service (DoS) attacks. The framework comprises three interrelated models: a formal conditional tree taxonomy, a hierarchical lattice structure based on order theory, and a conceptual Venn diagram. At its core, the taxonomy introduces six observable conditions (C0-C5) grounded in real-world attack behaviours, including source distribution, traffic volume, infrastructure targeting, and fin…

Capacity Planning and Scheduling for Jobs with Uncertainty in Resource Usage and Duration
Sunandita Patra, Mehtab Pathan, Mahmoud Mahfouz, Parisa Zehtabi, Wided Ouaja, Daniele Magazzeni, Manuela Veloso
https://arxiv.org/abs/2507.01225

Capacity Planning and Scheduling for Jobs with Uncertainty in Resource Usage and Duration
Organizations around the world schedule jobs (programs) regularly to perform various tasks dictated by their end users. With the major movement towards using a cloud computing infrastructure, our organization follows a hybrid approach with both cloud and on-prem servers. The objective of this work is to perform capacity planning, i.e., estimate resource requirements, and job scheduling for on-prem grid computing environments. A key contribution of our approach is handling uncertainty in both re…

SecureV2X: An Efficient and Privacy-Preserving System for Vehicle-to-Everything (V2X) Applications
Joshua Lee, Ali Arastehfard, Weiran Liu, Xuegang Ban, Yuan Hong
https://arxiv.org/abs/2508.19115

SecureV2X: An Efficient and Privacy-Preserving System for Vehicle-to-Everything (V2X) Applications
Autonomous driving and V2X technologies have developed rapidly in the past decade, leading to improved safety and efficiency in modern transportation. These systems interact with extensive networks of vehicles, roadside infrastructure, and cloud resources to support their machine learning capabilities. However, the widespread use of machine learning in V2X systems raises issues over the privacy of the data involved. This is particularly concerning for smart-transit and driver safety application…

Silent Failures in Stateless Systems: Rethinking Anomaly Detection for Serverless Computing
Chanh Nguyen, Erik Elmroth, Monowar Bhuyan
https://arxiv.org/abs/2507.04969

Silent Failures in Stateless Systems: Rethinking Anomaly Detection for Serverless Computing
Serverless computing has redefined cloud application deployment by abstracting infrastructure and enabling on-demand, event-driven execution, thereby enhancing developer agility and scalability. However, maintaining consistent application performance in serverless environments remains a significant challenge. The dynamic and transient nature of serverless functions makes it difficult to distinguish between benign and anomalous behavior, which in turn undermines the effectiveness of traditional …

KubeIntellect: A Modular LLM-Orchestrated Agent Framework for End-to-End Kubernetes Management
Mohsen Seyedkazemi Ardebili, Andrea Bartolini
https://arxiv.org/abs/2509.02449 htt…

KubeIntellect: A Modular LLM-Orchestrated Agent Framework for End-to-End Kubernetes Management
Kubernetes has become the foundation of modern cloud-native infrastructure, yet its management remains complex and fragmented. Administrators must navigate a vast API surface, manage heterogeneous workloads, and coordinate tasks across disconnected tools - often requiring precise commands, YAML configuration, and contextual expertise. This paper presents KubeIntellect, a Large Language Model (LLM)-powered system for intelligent, end-to-end Kubernetes control. Unlike existing tools that focus …

GlideinBenchmark: collecting resource information to optimize provisioning
Marco Mambelli, Shrijan Swaminathan
https://arxiv.org/abs/2507.21472 https://arx…

GlideinBenchmark: collecting resource information to optimize provisioning
Choosing the right resource can speed up job completion, better utilize the available hardware, and visibly reduce costs, especially when renting computers in the cloud. This was demonstrated in earlier studies on HEPCloud. However, the benchmarking of the resources proved to be a laborious and time-consuming process. This paper presents GlideinBenchmark, a new Web application leveraging the pilot infrastructure of GlideinWMS to benchmark resources, and it shows how to use the data collected an…

C-Koordinator: Interference-aware Management for Large-scale and Co-located Microservice Clusters
Shengye Song, Minxian Xu, Zuowei Zhang, Chengxi Gao, Fansong Zeng, Yu Ding, Kejiang Ye, Chengzhong Xu
https://arxiv.org/abs/2507.18005

C-Koordinator: Interference-aware Management for Large-scale and Co-located Microservice Clusters
Microservices transform traditional monolithic applications into lightweight, loosely coupled application components and have been widely adopted in many enterprises. Cloud platform infrastructure providers enhance the resource utilization efficiency of microservices systems by co-locating different microservices. However, this approach also introduces resource competition and interference among microservices. Designing interference-aware strategies for large-scale, co-located microservice clus…