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.

A User-centric Kubernetes-based Architecture for Green Cloud Computing
Matteo Zanotto, Leonardo Vicentini, Redi Vreto, Francesco Lumpp, Diego Braga, Sandro Fiore
https://arxiv.org/abs/2509.13325

A User-centric Kubernetes-based Architecture for Green Cloud Computing
To meet the increasing demand for cloud computing services, the scale and number of data centers keeps increasing worldwide. This growth comes at the cost of increased electricity consumption, which directly correlates to CO2 emissions, the main driver of climate change. As such, researching ways to reduce cloud computing emissions is more relevant than ever. However, although cloud providers are reportedly already working near optimal power efficiency, they fail in providing precise sustainabi…

PP-STAT: An Efficient Privacy-Preserving Statistical Analysis Framework using Homomorphic Encryption
Hyunmin Choi
https://arxiv.org/abs/2508.12093 https://…

PP-STAT: An Efficient Privacy-Preserving Statistical Analysis Framework using Homomorphic Encryption
With the widespread adoption of cloud computing, the need for outsourcing statistical analysis to third-party platforms is growing rapidly. However, handling sensitive data such as medical records and financial information in cloud environments raises serious privacy concerns. In this paper, we present PP-STAT, a novel and efficient Homomorphic Encryption (HE)-based framework for privacy-preserving statistical analysis. HE enables computations to be performed directly on encrypted data without …

Source: Nvidia is scaling back DGX Cloud to primarily internal R&D use; DGX Cloud was initially envisioned to compete with major cloud providers like AWS (Anissa Gardizy/The Information)
https://www.theinformation.com/articles/nvidia-steps-back-cloud-ef…

Nvidia Steps Back From Cloud Effort to Compete with AWS
Nvidia is stepping back from its nascent cloud computing business, which had put it in quasi-competition with Amazon Web Services. It has lessened its efforts to attract businesses to the cloud service, dubbed DGX Cloud, said a person with direct knowledge of the situation. Instead, Nvidia plans ...

Most cloud companies have pledged to return more water to communities than they consume by 2030. But datacentre water consumption is rising inline with corporate thirst for cloud services. How are CSPs doing at making targets for water conservation a reality?
https://www.computing.co.uk/research/2025/

Cooling the Cloud: Is ‘water positive by 2030’ just a pipedream?
Most cloud companies have pledged to return more water to communities than they consume by 2030. But datacentre water consumption is rising inline with corporate thirst for cloud services. How are CSPs doing at making targets for water conservation a reality?

Substituting Proof of Work in Blockchain with Training-Verified Collaborative Model Computation
Mohammad Ishzaz Asif Rafid, Morsalin Sakib
https://arxiv.org/abs/2508.12138 https…

Substituting Proof of Work in Blockchain with Training-Verified Collaborative Model Computation
Bitcoin's Proof of Work (PoW) mechanism, while central to achieving decentralized consensus, has long been criticized for excessive energy use and hardware inefficiencies \cite{devries2018bitcoin, truby2018decarbonizing}. This paper introduces a hybrid architecture that replaces Bitcoin's traditional PoW with a centralized, cloud-based collaborative training framework. In this model, miners contribute computing resources to train segments of horizontally scaled machine learning models on prepro…

LLM Agents for Interactive Workflow Provenance: Reference Architecture and Evaluation Methodology
Renan Souza, Timothy Poteet, Brian Etz, Daniel Rosendo, Amal Gueroudji, Woong Shin, Prasanna Balaprakash, Rafael Ferreira da Silva
https://arxiv.org/abs/2509.13978

LLM Agents for Interactive Workflow Provenance: Reference Architecture and Evaluation Methodology
Modern scientific discovery increasingly relies on workflows that process data across the Edge, Cloud, and High Performance Computing (HPC) continuum. Comprehensive and in-depth analyses of these data are critical for hypothesis validation, anomaly detection, reproducibility, and impactful findings. Although workflow provenance techniques support such analyses, at large scale, the provenance data become complex and difficult to analyze. Existing systems depend on custom scripts, structured quer…

A Scalable Architecture for Efficient Multi-bit Fully Homomorphic Encryption
Jiaao Ma, Ceyu Xu, Lisa Wu Wills
https://arxiv.org/abs/2509.12676 https://arxi…

A Scalable Architecture for Efficient Multi-bit Fully Homomorphic Encryption
In the era of cloud computing, privacy-preserving computation offloading is crucial for safeguarding sensitive data. Fully Homomorphic Encryption (FHE) enables secure processing of encrypted data, but the inherent computational complexity of FHE operations introduces significant computational overhead on the server side. FHE schemes often face a tradeoff between efficiency and versatility. While the CKKS scheme is highly efficient for polynomial operations, it lacks the flexibility of the binar…

TCDRM: A Tenant Budget-Aware Data Replication Framework for Multi-Cloud Computing
Santatra Hagamalala Bernardin (IRIT-PYRAMIDE, IRIT), Riad Mokadem (IRIT-PYRAMIDE, IRIT), Franck Morvan (IRIT-PYRAMIDE, IRIT), Hasinarivo Ramanana, Hasimandimby Rakotoarivelo
https://arxiv.org/abs/2510.07833

TCDRM: A Tenant Budget-Aware Data Replication Framework for Multi-Cloud Computing
Multi-cloud computing systems face significant challenges in ensuring acceptable performance while adhering to tenant budget requirements. This paper proposes a tenant budget-aware (tenant-centric) data replication framework for Multi-Cloud Computing (TCDRM). The proposed strategy dynamically creates data replicas based on predefined thresholds for response time, economic budget of the tenant and data popularity. TCDRM employs a heuristic replica placement algorithm that leverages the diverse p…

Ratio1 -- AI meta-OS
Andrei Damian, Petrica Butusina, Alessandro De Franceschi, Vitalii Toderian, Marius Grigoras, Cristian Bleotiu
https://arxiv.org/abs/2509.12223 https://

Ratio1 -- AI meta-OS
We propose the Ratio1 AI meta-operating system (meta-OS), a decentralized MLOps protocol that unifies AI model development, deployment, and inference across heterogeneous edge devices. Its key innovation is an integrated blockchain-based framework that transforms idle computing resources (laptops, smartphones, cloud VMs) into a trustless global supercomputer. The architecture includes novel components: a decentralized authentication layer (dAuth), an in-memory state database (CSTORE), a distrib…

Autonomous Navigation of Cloud-Controlled Quadcopters in Confined Spaces Using Multi-Modal Perception and LLM-Driven High Semantic Reasoning
Shoaib Ahmmad, Zubayer Ahmed Aditto, Md Mehrab Hossain, Noushin Yeasmin, Shorower Hossain
https://arxiv.org/abs/2508.07885

Autonomous Navigation of Cloud-Controlled Quadcopters in Confined Spaces Using Multi-Modal Perception and LLM-Driven High Semantic Reasoning
This paper introduces an advanced AI-driven perception system for autonomous quadcopter navigation in GPS-denied indoor environments. The proposed framework leverages cloud computing to offload computationally intensive tasks and incorporates a custom-designed printed circuit board (PCB) for efficient sensor data acquisition, enabling robust navigation in confined spaces. The system integrates YOLOv11 for object detection, Depth Anything V2 for monocular depth estimation, a PCB equipped with Ti…

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…

CoreWeave says it has signed a new $6.3B order with Nvidia, which guarantees that Nvidia will purchase any residual cloud capacity not sold to customers (Harshita Mary Varghese/Reuters)
https://www.reuters.com/business/coreweave-nv…

An efficient grey theory-driven path selection for energy efficiency control in the Internet of Things using fog and cloud computing
Mohammad Reza Akbari, Hamid Barati, Ali Barati
https://arxiv.org/abs/2510.03533

An efficient grey theory-driven path selection for energy efficiency control in the Internet of Things using fog and cloud computing
Due to the big data exchange on the Internet of Things, proper routing and selecting the best routes for fast data transmission improve network performance. There are major challenges, like high delay, when cloud computing is used. Therefore, one solution is to use other schemes, such as fog computing. In fog computing, all data is not sent to the cloud and the fog nodes close to objects are used for data processing. This reduces the network delay. In this paper, we propose an overlapping clust…

Exploiting Timing Side-Channels in Quantum Circuits Simulation Via ML-Based Methods
Ben Dong, Hui Feng, Qian Wang
https://arxiv.org/abs/2509.12535 https://…

Exploiting Timing Side-Channels in Quantum Circuits Simulation Via ML-Based Methods
As quantum computing advances, quantum circuit simulators serve as critical tools to bridge the current gap caused by limited quantum hardware availability. These simulators are typically deployed on cloud platforms, where users submit proprietary circuit designs for simulation. In this work, we demonstrate a novel timing side-channel attack targeting cloud- based quantum simulators. A co-located malicious process can observe fine-grained execution timing patterns to extract sensitive informati…

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 ...

Dynamic Function Configuration and its Management in Serverless Computing: A Taxonomy and Future Directions
Siddharth Agarwal, Maria A. Rodriguez, Rajkumar Buyya
https://arxiv.org/abs/2510.02404

Dynamic Function Configuration and its Management in Serverless Computing: A Taxonomy and Future Directions
The serverless cloud computing model offers a framework where the service provider abstracts the underlying infrastructure management from developers. In this serverless model, FaaS provides an event-driven, function-oriented computing service characterised by fine-grained, usage-based pricing that eliminates cost for idle resources. Platforms like AWS Lambda, Azure Functions, and Cloud Run Functions require developers to configure their function(s) with minimum operational resources for its su…

Towards Efficient and Secure Cloud Control Systems: Advances, Challenges, and Future Directions
Yasir Ali, Tayyab Manzoor, Huan Yang, Asif Ali, Yuanqing Xia
https://arxiv.org/abs/2509.09299

Towards Efficient and Secure Cloud Control Systems: Advances, Challenges, and Future Directions
Networked Control Systems (NCSs) have been instrumental in realizing fully connected and responsive intelligent environments within the context of real-time virtual control and management. However, traditional NCSs face considerable challenges in handling the vast amounts of data generated by large-scale control applications, particularly in terms of data acquisition, storage, and computational processing. To address these challenges, the emergence of cloud computing and advancements in control…

"Research on the construction and application of problem-method-oriented academic graph empowered by LLM" https://doi.org/10.1007/s10791-025-09675-2
"Nowadays, the volume of literature in each field is huge and is growing rapidly, which posts challenge to researchers’ lit…

Nagare Media Ingest: A System for Multimedia Ingest Workflows
Matthias Neugebauer
https://arxiv.org/abs/2509.11972 https://arxiv.org/pdf/2509.11972

Nagare Media Ingest: A System for Multimedia Ingest Workflows
Ingesting multimedia data is usually the first step of multimedia workflows. For this purpose, various streaming protocols have been proposed for live and file-based content. For instance, SRT, RIST, DASH-IF Live Media Ingest Protocol and MOQT have been introduced in recent years. At the same time, the number of use cases has only proliferated by the move to cloud- and edge-computing environments. Multimedia systems now have to handle this complexity in order to stay relevant for today's workfl…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/4]:
- Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey
Liu, Du, Yang, Wu, Wang, Hu, Wang, Liu, Sun, Boukerche, Leung

Proactive and Reactive Autoscaling Techniques for Edge Computing
Suhrid Gupta, Muhammed Tawfiqul Islam, Rajkumar Buyya
https://arxiv.org/abs/2510.10166 https://

Proactive and Reactive Autoscaling Techniques for Edge Computing
Edge computing allows for the decentralization of computing resources. This decentralization is achieved through implementing microservice architectures, which require low latencies to meet stringent service level agreements (SLA) such as performance, reliability, and availability metrics. While cloud computing offers the large data storage and computation resources necessary to handle peak demands, a hybrid cloud and edge environment is required to ensure SLA compliance. Several auto-scaling a…

Privacy-Preserving Approximate Nearest Neighbor Search on High-Dimensional Data
Yingfan Liu, Yandi Zhang, Jiadong Xie, Hui Li, Jeffrey Xu Yu, Jiangtao Cui
https://arxiv.org/abs/2508.10373

Privacy-Preserving Approximate Nearest Neighbor Search on High-Dimensional Data
In the era of cloud computing and AI, data owners outsource ubiquitous vectors to the cloud, which furnish approximate $k$-nearest neighbors ($k$-ANNS) services to users. To protect data privacy against the untrusted server, privacy-preserving $k$-ANNS (PP-ANNS) on vectors has been a fundamental and urgent problem. However, existing PP-ANNS solutions fall short of meeting the requirements of data privacy, efficiency, accuracy, and minimal user involvement concurrently. To tackle this challenge,…

Welcome to Computing's weekly roundup of tech news in Asia. This time we look at China’s datacentre glut, a near-zero-carbon facility and a serious cyberattack in Singapore.
https://www.computing.co.uk/news/2025/cloud/china-centralise-cloud…

China tries to centralise cloud computing - Asian Tech Roundup
Welcome to Computing's weekly roundup of tech news in Asia. This time we look at China’s datacentre glut, a near-zero-carbon facility and a serious cyberattack ...

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…

Closing the HPC-Cloud Convergence Gap: Multi-Tenant Slingshot RDMA for Kubernetes
Philipp A. Friese, Ahmed Eleliemy, Utz-Uwe Haus, Martin Schulz
https://arxiv.org/abs/2508.09663

Closing the HPC-Cloud Convergence Gap: Multi-Tenant Slingshot RDMA for Kubernetes
Converged HPC-Cloud computing is an emerging computing paradigm that aims to support increasingly complex and multi-tenant scientific workflows. These systems require reconciliation of the isolation requirements of native cloud workloads and the performance demands of HPC applications. In this context, networking hardware is a critical boundary component: it is the conduit for high-throughput, low-latency communication and enables isolation across tenants. HPE Slingshot is a high-speed network …

Cloud-Assisted Remote Control for Aerial Robots: From Theory to Proof-of-Concept Implementation
Achilleas Santi Seisa, Viswa Narayanan Sankaranarayanan, Gerasimos Damigos, Sumeet Gajanan Satpute, George Nikolakopoulos
https://arxiv.org/abs/2509.04095

Cloud-Assisted Remote Control for Aerial Robots: From Theory to Proof-of-Concept Implementation
Cloud robotics has emerged as a promising technology for robotics applications due to its advantages of offloading computationally intensive tasks, facilitating data sharing, and enhancing robot coordination. However, integrating cloud computing with robotics remains a complex challenge due to network latency, security concerns, and the need for efficient resource management. In this work, we present a scalable and intuitive framework for testing cloud and edge robotic systems. The framework co…

Tigris, which is building a network of localized data storage centers that it claims can meet the compute needs of AI workloads, raised a $25M Series A (Rebecca Bellan/TechCrunch)
https://techcrunch.com/2025/10/09/this-distributed-data-stor…

This distributed data storage startup wants to take on Big Cloud | TechCrunch
Tigris has raised $25 million to expand its growing network of localized data storage centers — the storage layer for decentralized computing infrastructure.

Microsoft Is an AI Darling, but Its Core Businesses Are Booming Too: Company’s non-AI businesses, including productivity software and cloud computing, are going strong
https://www.wsj.com/tech/ai/microsoft-is-an-ai-darling-but-its-core-…

The Data Enclave Advantage: A New Paradigm for Least-Privileged Data Access in a Zero-Trust World
Nico Bistolfi, Andreea Georgescu, Dave Hodson
https://arxiv.org/abs/2510.09494 …

The Data Enclave Advantage: A New Paradigm for Least-Privileged Data Access in a Zero-Trust World
As cloud infrastructure evolves to support dynamic and distributed workflows, accelerated now by AI-driven processes, the outdated model of standing permissions has become a critical vulnerability. Based on the Cloud Security Alliance (CSA) Top Threats to Cloud Computing Deep Dive 2025 Report, our analysis details how standing permissions cause catastrophic cloud breaches. While current security tools are addressing network and API security, the challenge of securing granular data access remain…

Wer Cloud-Dienste bei Amazon, Microsoft & Co. abonniert, bucht die Möglichkeit des US-Regierungszugriffs automatisch mit. Das Whitepaper des @… deckt auf, wie echtes souveränes Cloud-Computing heute aussehen muss – und warum wir mehr Transparenz brauchen. #DigitaleSouveränität

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…

Google and Cisco, have disclosed separate data breaches stemming from voice phishing (vishing) attacks that compromised customer information stored in cloud-based CRM systems.
https://www.computing.co.uk/news/2025/security/google-and-ci…

Google and Cisco confirm data breaches linked to vishing attacks
Google and Cisco, have disclosed separate data breaches stemming from voice phishing (vishing) attacks that compromised customer information stored in cloud-based CRM systems.

Plugging Leaks in Fault-Tolerant Quantum Computation and Verification
Theodoros Kapourniotis, Dominik Leichtle, Luka Music, Harold Ollivier
https://arxiv.org/abs/2510.03227 http…

Plugging Leaks in Fault-Tolerant Quantum Computation and Verification
With the advent of quantum cloud computing, the security of delegated quantum computation has become of utmost importance. While multiple statistically secure blind verification schemes in the prepare-and-send model have been proposed, none of them achieves full quantum fault-tolerance, a prerequisite for useful verification on scalable quantum computers. In this paper, we present the first fault-tolerant blind verification scheme for universal quantum computations able to handle secret-depende…

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…

Toward Energy and Location-Aware Resource Allocation in Next Generation Networks
Mandar Datar (CEA-LETI), Mattia Merluzzi (CEA-LETI)
https://arxiv.org/abs/2508.05109 https://

Toward Energy and Location-Aware Resource Allocation in Next Generation Networks
Wireless networks are evolving from radio resource providers to complex systems that also involve computing, with the latter being distributed across edge and cloud facilities. Also, their optimization is shifting more and more from a performance to a value-oriented paradigm. The two aspects shall be balanced continuously, to maximize the utilities of Services Providers (SPs), users quality of experience and fairness, while meeting global constraints in terms of energy consumption and carbon fo…

Comparative Performance Analysis of Modern NoSQL Data Technologies: Redis, Aerospike, and Dragonfly
Deep Bodra, Sushil Khairnar
https://arxiv.org/abs/2510.08863 https://

Comparative Performance Analysis of Modern NoSQL Data Technologies: Redis, Aerospike, and Dragonfly
The rise of distributed applications and cloud computing has created a demand for scalable, high-performance key-value storage systems. This paper presents a performance evaluation of three prominent NoSQL key-value stores: Redis, Aerospike, and Dragonfly, using the Yahoo! Cloud Serving Benchmark (YCSB) framework. We conducted extensive experiments across three distinct workload patterns (read-heavy, write-heavy), and balanced while systematically varying client concurrency from 1 to 32 clients…

Alibaba's shares jumped 19% in Hong Kong on Monday, following strong quarterly results from its cloud computing unit and reports of its new AI chip on Friday (Arjun Kharpal/CNBC)
https://www.cnbc.com/2025/09/01/alibaba-shares-hong-kong-today.html

Alibaba shares jump 19% on cloud unit acceleration, report of new AI chip
Alibaba's Hong Kong-listed shares jumped 19% on strong results at its cloud computing division driven by its AI investments.

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…

Tesco is suing Broadcom and technology reseller Computacenter in a dispute over VMware software licences that it says could disrupt its ability to keep food on shelves.
https://www.computing.co.uk/news/2025/cloud/tesco-sues…

Tesco sues Broadcom over VMware licensing, warns food supply could be hit
Tesco is suing Broadcom and technology reseller Computacenter in a dispute over VMware software licences that it says could disrupt its ability to keep food ...

Governed By Agents: A Survey On The Role Of Agentic AI In Future Computing Environments
Nauman Ali Murad, Safia Baloch
https://arxiv.org/abs/2509.16676 https://

Governed By Agents: A Survey On The Role Of Agentic AI In Future Computing Environments
The emergence of agentic Artificial Intelligence (AI), which can operate autonomously, demonstrate goal-directed behavior, and adaptively learn, indicates the onset of a massive change in today's computing infrastructure. This study investigates how agentic AI models' multiple characteristics may impact the architecture, governance, and operation under which computing environments function. Agentic AI has the potential to reduce reliance on extremely large (public) cloud environments due to res…

SEC filing: Microsoft strikes a deal worth up to $19.4B through 2031 to get AI cloud computing power from Nebius Group; NBIS jumps 50% after hours (Bloomberg)
https://www.bloomberg.com/news/articles/2025-09-08/microsoft-signs-…

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 …

Snap, Segment, Deploy: A Visual Data and Detection Pipeline for Wearable Industrial Assistants
Di Wen, Junwei Zheng, Ruiping Liu, Yi Xu, Kunyu Peng, Rainer Stiefelhagen
https://arxiv.org/abs/2507.21072

Snap, Segment, Deploy: A Visual Data and Detection Pipeline for Wearable Industrial Assistants
Industrial assembly tasks increasingly demand rapid adaptation to complex procedures and varied components, yet are often conducted in environments with limited computing, connectivity, and strict privacy requirements. These constraints make conventional cloud-based or fully autonomous solutions impractical for factory deployment. This paper introduces a mobile-device-based assistant system for industrial training and operational support, enabling real-time, semi-hands-free interaction through …

DarwinGame: Playing Tournaments for Tuning Applications in Noisy Cloud Environments
Rohan Basu Roy, Vijay Gadepally, Devesh Tiwari
https://arxiv.org/abs/2509.25090 https://

DarwinGame: Playing Tournaments for Tuning Applications in Noisy Cloud Environments
This work introduces a new subarea of performance tuning -- performance tuning in a shared interference-prone computing environment. We demonstrate that existing tuners are significantly suboptimal by design because of their inability to account for interference during tuning. Our solution, DarwinGame, employs a tournament-based design to systematically compare application executions with different tunable parameter configurations, enabling it to identify the relative performance of different t…

Metronome: Efficient Scheduling for Periodic Traffic Jobs with Network and Priority Awareness
Hao Jiang, Meng Qin, Ruijie Kuai, Dandan Liang
https://arxiv.org/abs/2510.12274 htt…

Metronome: Efficient Scheduling for Periodic Traffic Jobs with Network and Priority Awareness
With the rapid growth in computing power demand, cloud native networks have emerged as a promising solution to address the challenges of efficient resource coordination, particularly in coping with the dynamic fluctuations of network bandwidth in clusters. We propose Metronome, a network-aware and priority-aware scheduling mechanism for cloud native networks. This mechanism is designed to support jobs that exhibit periodic traffic patterns and dynamic bandwidth demands, particularly in the cont…

A distributed routing protocol for sending data from things to the cloud leveraging fog technology in the large-scale IoT ecosystem
Mohammad Reza Akbari, Hamid Barati, Ali Barati
https://arxiv.org/abs/2510.03524

A distributed routing protocol for sending data from things to the cloud leveraging fog technology in the large-scale IoT ecosystem
Fog computing integrates cloud and edge resources. According to an intelligent and decentralized method, this technology processes data generated by IoT sensors to seamlessly integrate physical and cyber environments. Internet of Things uses wireless and smart objects. They communicate with each other, monitor the environment, collect information, and respond to user requests. These objects have limited energy resources since they use batteries to supply energy. Also, they cannot replace their …

Encrypted-State Quantum Compilation Scheme Based on Quantum Circuit Obfuscation
Chenyi Zhang, Tao Shang, Xueyi Guo
https://arxiv.org/abs/2507.17589 https://

Encrypted-State Quantum Compilation Scheme Based on Quantum Circuit Obfuscation
With the rapid advancement of quantum computing, quantum compilation has become a crucial layer connecting high-level algorithms with physical hardware. In quantum cloud computing, compilation is performed on the cloud side, which exposes user circuits to potential risks such as structural leakage and output predictability. To address these issues, we propose the encrypted-state quantum compilation scheme based on quantum circuit obfuscation (ECQCO), the first secure compilation framework tailo…

Source: Meta has signed a deal with Google, worth more than $10B over six years, to use Google Cloud's servers, storage, networking, and other services (The Information)
https://www.theinformation.com/articles/meta-signs-10-billion-plus-cloud-deal-goo…

Meta Signs $10 Billion-Plus Cloud Deal With Google
Google has struck a major cloud computing deal with Meta Platforms, according to two people familiar with the matter. As part of the agreement, the social media giant will use Google Cloud’s servers, storage, networking and other services, one of the people said. The deal—worth more than $10 ...

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…

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 …

A Management Framework for Vehicular Cloudtoward Economic and Environmental Efficiency
Rosario Patan\`e, Andrea Araldo, Nadjib Achir, Lila Boukhatem
https://arxiv.org/abs/2509.24946

A Management Framework for Vehicular Cloudtoward Economic and Environmental Efficiency
Vehicular Cloud Computing (VCC) leverages the idle computing capacity of vehicles to execute end-users' offloaded tasks without requiring new computation infrastructure. Despite its conceptual appeal, VCC adoption is hindered by the lack of quantitative evidence demonstrating its profitability and environmental advantages in real-world scenarios. This paper tackles the fundamental question: Can VCC be both profitable and sustainable? We address this problem by proposing a management scheme for …

REACH: Reinforcement Learning for Adaptive Microservice Rescheduling in the Cloud-Edge Continuum
Xu Bai, Muhammed Tawfiqul Islam, Rajkumar Buyya, Adel N. Toosi
https://arxiv.org/abs/2510.06675

REACH: Reinforcement Learning for Adaptive Microservice Rescheduling in the Cloud-Edge Continuum
Cloud computing, despite its advantages in scalability, may not always fully satisfy the low-latency demands of emerging latency-sensitive pervasive applications. The cloud-edge continuum addresses this by integrating the responsiveness of edge resources with cloud scalability. Microservice Architecture (MSA) characterized by modular, loosely coupled services, aligns effectively with this continuum. However, the heterogeneous and dynamic computing resource poses significant challenges to the op…

The Flood Complex: Large-Scale Persistent Homology on Millions of Points
Florian Graf, Paolo Pellizzoni, Martin Uray, Stefan Huber, Roland Kwitt
https://arxiv.org/abs/2509.22432

The Flood Complex: Large-Scale Persistent Homology on Millions of Points
We consider the problem of computing persistent homology (PH) for large-scale Euclidean point cloud data, aimed at downstream machine learning tasks, where the exponential growth of the most widely-used Vietoris-Rips complex imposes serious computational limitations. Although more scalable alternatives such as the Alpha complex or sparse Rips approximations exist, they often still result in a prohibitively large number of simplices. This poses challenges in the complex construction and in the s…

Vehicular Cloud Computing: A cost-effective alternative to Edge Computing in 5G networks
Rosario Patan\`e, Nadjib Achir, Andrea Araldo, Lila Boukhatem
https://arxiv.org/abs/2507.15670

Vehicular Cloud Computing: A cost-effective alternative to Edge Computing in 5G networks
Edge Computing (EC) is a computational paradigm that involves deploying resources such as CPUs and GPUs near end-users, enabling low-latency applications like augmented reality and real-time gaming. However, deploying and maintaining a vast network of EC nodes is costly, which can explain its limited deployment today. A new paradigm called Vehicular Cloud Computing (VCC) has emerged and inspired interest among researchers and industry. VCC opportunistically utilizes existing and idle vehicular …

Oxide Computer, which provides on-premise cloud computers to businesses, raised a $100M Series B led by Thomas Tull's US Innovative Technology Fund (Max A. Cherney/Reuters)
https://www.reuters.com/technology/cloud-server-startup-oxid…

Efficient Cloud-Edge-Device Query Execution Based on Collaborative Scan Operator
Chunyu Zhao, Hongzhi Wang, Kaixin Zhang, Hongliang Li, Yihan Zhang, Jiawei Zhang, Kunkai Gu, Yuan Tian, Xiangdong Huang, Jingyi Xu
https://arxiv.org/abs/2508.15285

Efficient Cloud-Edge-Device Query Execution Based on Collaborative Scan Operator
In cloud-edge-device (CED) collaborative query (CQ) processing, by leveraging CED collaboration, the advantages of both cloud computing and edge resources can be fully integrated. However, it is difficult to implement collaborative operators that can flexibly switch between the cloud and the edge during query execution. Thus, in this paper, we aim to improve the query performance when the edge resources reach a bottleneck. To achieve seamless switching of query execution between the cloud and e…

A Novel IaaS Tax Model as Leverage Towards Green Cloud Computing
Benedikt Pittl, Werner Mach, Erich Schikuta
https://arxiv.org/abs/2509.02767 https://arxiv…

A Novel IaaS Tax Model as Leverage Towards Green Cloud Computing
The cloud computing technology uses datacenters, which require energy. Recent trends show that the required energy for these datacenters will rise over time, or at least remain constant. Hence, the scientific community developed different algorithms, architectures, and approaches for improving the energy efficiency of cloud datacenters, which are summarized under the umbrella term Green Cloud computing. In this paper, we use an economic approach - taxes - for reducing the energy consumption of …

Alibaba reports Q1 revenue up 2% YoY to ~$34.6B, below ~$35.4B est., and net income up 76% to ~$5.9B, above ~$4B est., as AI demand drives cloud sales up 26% (Arjun Kharpal/CNBC)
https://www.cnbc.com/2025/08/29/alibaba-baba-june-quarter-2025-earnings-repor…

Alibaba's key cloud unit sales shine even as quarterly revenues miss
Alibaba posted a a revenue miss in the June quarter even as it showed an acceleration at its cloud computing unit.

Future-Proofing Cloud Security Against Quantum Attacks: Risk, Transition, and Mitigation Strategies
Yaser Baseri, Abdelhakim Hafid, Arash Habibi Lashkari
https://arxiv.org/abs/2509.15653

Future-Proofing Cloud Security Against Quantum Attacks: Risk, Transition, and Mitigation Strategies
Quantum Computing (QC) introduces a transformative threat to digital security, with the potential to compromise widely deployed classical cryptographic systems. This survey offers a comprehensive and systematic examination of quantumsafe security for Cloud Computing (CC), focusing on the vulnerabilities, transition strategies, and mitigation mechanisms required to secure cloud infrastructures in the quantum era. We evaluated the landscape of quantum threats across the entire CC stack, demonstra…

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…

Quantum Software Security Challenges within Shared Quantum Computing Environments
Samuel Ovaskainen, Majid Haghparast, Tommi Mikkonen
https://arxiv.org/abs/2507.17712 https://…

Quantum Software Security Challenges within Shared Quantum Computing Environments
The number of qubits in quantum computers keeps growing, but most quantum programs remain relatively small because of the noisy nature of the underlying quantum hardware. This might lead quantum cloud providers to explore increased hardware utilization, and thus profitability through means such as multi-programming, which would allow the execution of multiple programs in parallel. The adoption of such technology would bring entirely new challenges to the field of quantum software security. This…

Edge-assisted Parallel Uncertain Skyline Processing for Low-latency IoE Analysis
Chuan-Chi Lai, Yan-Lin Chen, Bo-Xin Liu, Chuan-Ming Liu
https://arxiv.org/abs/2508.04596 https:/…

Edge-assisted Parallel Uncertain Skyline Processing for Low-latency IoE Analysis
Due to the Internet of Everything (IoE), data generated in our life become larger. As a result, we need more effort to analyze the data and extract valuable information. In the cloud computing environment, all data analysis is done in the cloud, and the client only needs less computing power to handle some simple tasks. However, with the rapid increase in data volume, sending all data to the cloud via the Internet has become more expensive. The required cloud computing resources have also becom…

Data Scheduling Algorithm for Scalable and Efficient IoT Sensing in Cloud Computing
Noor Islam S. Mohammad
https://arxiv.org/abs/2508.04334 https://arxiv.o…

Data Scheduling Algorithm for Scalable and Efficient IoT Sensing in Cloud Computing
The rapid growth of Internet of Things (IoT) devices produces massive, heterogeneous data streams, demanding scalable and efficient scheduling in cloud environments to meet latency, energy, and Quality-of-Service (QoS) requirements. Existing scheduling methods often lack adaptability to dynamic workloads and network variability inherent in IoT-cloud systems. This paper presents a novel hybrid scheduling algorithm combining deep Reinforcement Learning (RL) and Ant Colony Optimization (ACO) to ad…

Quality-of-Service Aware LLM Routing for Edge Computing with Multiple Experts
Jin Yang, Qiong Wu, Zhiying Feng, Zhi Zhou, Deke Guo, Xu Chen
https://arxiv.org/abs/2508.00234 http…

Quality-of-Service Aware LLM Routing for Edge Computing with Multiple Experts
Large Language Models (LLMs) have demonstrated remarkable capabilities, leading to a significant increase in user demand for LLM services. However, cloud-based LLM services often suffer from high latency, unstable responsiveness, and privacy concerns. Therefore, multiple LLMs are usually deployed at the network edge to boost real-time responsiveness and protect data privacy, particularly for many emerging smart mobile and IoT applications. Given the varying response quality and latency of LLM s…

Crosslisted article(s) found for cs.DC. https://arxiv.org/list/cs.DC/new
[1/1]:
- A Semantic Model for Audit of Cloud Engines based on ISO/IEC TR 3445:2022
Morteza Sargolzaei Javan

A Non-leveled and Reliable Approximate FHE Framework through Binarized Polynomial Rings
Baigang Chen, Dongfang Zhao
https://arxiv.org/abs/2508.02943 https://

A Non-leveled and Reliable Approximate FHE Framework through Binarized Polynomial Rings
Homomorphic encryption (HE) enables secure computation on encrypted data, safeguarding user privacy in domains such as cloud computing, healthcare, and finance. Among fully homomorphic encryption (FHE) schemes, CKKS is notable for supporting approximate arithmetic over complex numbers, a key requirement for machine-learning and numerical workloads. However, CKKS incurs rapid noise growth, complex parameter tuning, and relies on costly modulus switching. We propose a binary variant of CKKS that …

Automated Energy-Aware Time-Series Model Deployment on Embedded FPGAs for Resilient Combined Sewer Overflow Management
Tianheng Ling, Vipin Singh, Chao Qian, Felix Biessmann, Gregor Schiele
https://arxiv.org/abs/2508.13905

Automated Energy-Aware Time-Series Model Deployment on Embedded FPGAs for Resilient Combined Sewer Overflow Management
Extreme weather events, intensified by climate change, increasingly challenge aging combined sewer systems, raising the risk of untreated wastewater overflow. Accurate forecasting of sewer overflow basin filling levels can provide actionable insights for early intervention, helping mitigating uncontrolled discharge. In recent years, AI-based forecasting methods have offered scalable alternatives to traditional physics-based models, but their reliance on cloud computing limits their reliability …

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…

Energy Efficiency in Cloud-Based Big Data Processing for Earth Observation: Gap Analysis and Future Directions
Adhitya Bhawiyuga, Serkan Girgin, Rolf A. de By, Raul Zurita-Milla
https://arxiv.org/abs/2510.02882

Energy Efficiency in Cloud-Based Big Data Processing for Earth Observation: Gap Analysis and Future Directions
Earth observation (EO) data volumes are rapidly increasing. While cloud computing are now used for processing large EO datasets, the energy efficiency aspects of such a processing have received much less attention. This issue is notable given the increasing awareness of energy costs and carbon footprint in big data processing, particularly with increased attention on compute-intensive foundation models. In this paper we identify gaps in energy efficiency practices within cloud-based EO big data…

A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networks
Jiaqi Wu, Jing Liu, Yang Liu, Lixu Wang, Zehua Wang, Wei Chen, Zijian Tian, Richard Yu, Victor C. M. Leung
https://arxiv.org/abs/2508.18803

A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networks
The proliferation of Internet of things (IoT) devices in smart cities, transportation, healthcare, and industrial applications, coupled with the explosive growth of AI-driven services, has increased demands for efficient distributed computing architectures and networks, driving cloud-edge-terminal collaborative intelligence (CETCI) as a fundamental paradigm within the artificial intelligence of things (AIoT) community. With advancements in deep learning, large language models (LLMs), and edge c…

BOLT: Bandwidth-Optimized Lightning-Fast Oblivious Map powered by Secure HBM Accelerators
Yitong Guo, Hongbo Chen, Haobin Hiroki Chen, Yukui Luo, XiaoFeng Wang, Chenghong Wang
https://arxiv.org/abs/2509.01742

BOLT: Bandwidth-Optimized Lightning-Fast Oblivious Map powered by Secure HBM Accelerators
While Trusted Execution Environments provide a strong foundation for secure cloud computing, they remain vulnerable to access pattern leakages. Oblivious Maps (OMAPs) mitigate this by fully hiding access patterns but suffer from high overhead due to randomized remapping and worst-case padding. We argue these costs are not fundamental. Modern accelerators featuring High-Bandwidth Memory (HBM) offer a new opportunity: Vaswani et al. [OSDI'18] point out that eavesdropping on HBM is difficult -- ev…

Validating Terrain Models in Digital Twins for Trustworthy sUAS Operations
Arturo Miguel Russell Bernal, Maureen Petterson, Pedro Antonio Alarcon Granadeno, Michael Murphy, James Mason, Jane Cleland-Huang
https://arxiv.org/abs/2508.16104

Validating Terrain Models in Digital Twins for Trustworthy sUAS Operations
With the increasing deployment of small Unmanned Aircraft Systems (sUAS) in unfamiliar and complex environments, Environmental Digital Twins (EDT) that comprise weather, airspace, and terrain data are critical for safe flight planning and for maintaining appropriate altitudes during search and surveillance operations. With the expansion of sUAS capabilities through edge and cloud computing, accurate EDT are also vital for advanced sUAS capabilities, like geolocation. However, real-world sUAS de…

Intelligent Load Balancing in Cloud Computer Systems
Leszek Sliwko
https://arxiv.org/abs/2509.22704 https://arxiv.org/pdf/2509.22704

Intelligent Load Balancing in Cloud Computer Systems
Cloud computing is an established technology allowing users to share resources on a large scale, never before seen in IT history. A cloud system connects multiple individual servers in order to process related tasks in several environments at the same time. Clouds are typically more cost-effective than single computers of comparable computing performance. The sheer physical size of the system itself means that thousands of machines may be involved. The focus of this research was to design a str…

Oracle will pay for and oversee the construction of three of the new Stargate data centers, and OpenAI will then purchase computing power from Oracle (Cade Metz/New York Times)
https://www.nytimes.com/2025/09/23/technology/openai-data-centers-united-state…

OpenAI to Join Tech Giants in Building 5 New Data Centers in U.S.
Working with the Japanese conglomerate SoftBank and the cloud company Oracle, the A.I. start-up will spend $400 billion over the next five years.

Replaced article(s) found for cs.DC. https://arxiv.org/list/cs.DC/new
[1/1]:
- SageServe: Optimizing LLM Serving on Cloud Data Centers with Forecast Aware Auto-Scaling
Shashwat Jaiswal, et al.

Hybrid AI-Driven Intrusion Detection: Framework Leveraging Novel Feature Selection for Enhanced Network Security
Maryam Mahdi Alhusseini, Mohammad Reza Feizi Derakhshi
https://arxiv.org/abs/2509.00896 …

Hybrid AI-Driven Intrusion Detection: Framework Leveraging Novel Feature Selection for Enhanced Network Security
In today's rapidly evolving digital landscape, safeguarding network infrastructures against cyberattacks has become a critical priority. This research presents an innovative AI-driven real-time intrusion detection framework designed to enhance network security, particularly in Wireless Sensor Networks (WSNs) and Cloud Computing (CC) environments. The system employs classical machine learning models, Logistic Regression, Decision Tree, and K-Nearest Neighbors, optimized through the novel Energy …

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…

A High-performance Real-time Container File Monitoring Approach Based on Virtual Machine Introspection
Kai Tan, Dongyang Zhan, Lin Ye, Hongli Zhang, Binxing Fang, Zhihong Tian
https://arxiv.org/abs/2509.16030

A High-performance Real-time Container File Monitoring Approach Based on Virtual Machine Introspection
As cloud computing continues to advance and become an integral part of modern IT infrastructure, container security has emerged as a critical factor in ensuring the smooth operation of cloud-native applications. An attacker can attack the service in the container or even perform the container escape attack by tampering with the files. Monitoring container files is important for APT detection and cyberspace security. Existing file monitoring methods are usually based on host operating system or …

Adaptive AI Model Partitioning over 5G Networks
Tam Thanh Nguyen, Tuan Van Ngo, Long Thanh Le, Yong Hao Pua, Mao Van Ngo, Binbin Chen, Tony Q. S. Quek
https://arxiv.org/abs/2509.01906

Adaptive AI Model Partitioning over 5G Networks
Mobile devices increasingly rely on deep neural networks (DNNs) for complex inference tasks, but running entire models locally drains the device battery quickly. Offloading computation entirely to cloud or edge servers reduces processing load at devices but poses privacy risks and can incur high network bandwidth consumption and long delays. Split computing (SC) mitigates these challenges by partitioning DNNs between user equipment (UE) and edge servers. However, 5G wireless channels are time-v…

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…

Crosslisted article(s) found for cs.DC. https://arxiv.org/list/cs.DC/new
[1/1]:
- Efficient Fault Localization in a Cloud Stack Using End-to-End Application Service Topology
Dhanya R Mathews, Mudit Verma, Pooja Aggarwal, J. Lakshmi

Efficient Routing of Inference Requests across LLM Instances in Cloud-Edge Computing
Shibo Yu, Mohammad Goudarzi, Adel Nadjaran Toosi
https://arxiv.org/abs/2507.15553

Efficient Routing of Inference Requests across LLM Instances in Cloud-Edge Computing
The rising demand for Large Language Model (LLM) inference services has intensified pressure on computational resources, resulting in latency and cost challenges. This paper introduces a novel routing algorithm based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to distribute inference requests across heterogeneous LLM instances in a cloud-edge computing environment. Formulated as a multi-objective optimization problem, the algorithm balances response quality, response time, and i…

The High Cost of Keeping Warm: Characterizing Overhead in Serverless Autoscaling Policies
Leonid Kondrashov, Boxi Zhou, Hancheng Wang, Dmitrii Ustiugov
https://arxiv.org/abs/2509.03104

The High Cost of Keeping Warm: Characterizing Overhead in Serverless Autoscaling Policies
Serverless computing is transforming cloud application development, but the performance-cost trade-offs of control plane designs remain poorly understood due to a lack of open, cross-platform benchmarks and detailed system analyses. In this work, we address these gaps by designing a serverless system that approximates the scaling behaviors of commercial providers, including AWS Lambda and Google Cloud Run. We systematically compare the performance and cost-efficiency of both synchronous and asy…

Deadline-Aware Joint Task Scheduling and Offloading in Mobile Edge Computing Systems
Ngoc Hung Nguyen, Van-Dinh Nguyen, Anh Tuan Nguyen, Nguyen Van Thieu, Hoang Nam Nguyen, Symeon Chatzinotas
https://arxiv.org/abs/2507.18864

Deadline-Aware Joint Task Scheduling and Offloading in Mobile Edge Computing Systems
The demand for stringent interactive quality-of-service has intensified in both mobile edge computing (MEC) and cloud systems, driven by the imperative to improve user experiences. As a result, the processing of computation-intensive tasks in these systems necessitates adherence to specific deadlines or achieving extremely low latency. To optimize task scheduling performance, existing research has mainly focused on reducing the number of late jobs whose deadlines are not met. However, the prima…

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…

Percepta: High Performance Stream Processing at the Edge
Clarisse Sousa, Tiago Fonseca, Luis Lino Ferreira, Ricardo Ven\^ancio, Ricardo Severino
https://arxiv.org/abs/2510.05149

Percepta: High Performance Stream Processing at the Edge
The rise of real-time data and the proliferation of Internet of Things (IoT) devices have highlighted the limitations of cloud-centric solutions, particularly regarding latency, bandwidth, and privacy. These challenges have driven the growth of Edge Computing. Associated with IoT appears a set of other problems, like: data rate harmonization between multiple sources, protocol conversion, handling the loss of data and the integration with Artificial Intelligence (AI) models. This paper presents …

Replaced article(s) found for cs.DC. https://arxiv.org/list/cs.DC/new
[1/1]:
- Resource Management Schemes for Cloud-Native Platforms with Computing Containers of Docker and Ku...
Ying Mao, Yuqi Fu, Suwen Gu, Wenrui Mu, Long Cheng, Qingzhi Liu

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…

FOGNITE: Federated Learning-Enhanced Fog-Cloud Architecture
Somayeh Sobati-M
https://arxiv.org/abs/2507.16668 https://arxiv.org/pdf/2…

FOGNITE: Federated Learning-Enhanced Fog-Cloud Architecture
Modern smart grids demand fast, intelligent, and energy-aware computing at the edge to manage real time fluctuations and ensure reliable operation. This paper introduces FOGNITE Fog-based Grid In intelligence with Neural Integration and Twin based Execution a next-generation fog cloud framework designed to enhance autonomy, resilience, and efficiency in distributed energy systems. FOGNITE combines three core components: federated learning, reinforcement learning, and digital twin validation. Ea…

DDoS Attacks in Cloud Computing: Detection and Prevention
Zain Ahmad, Musab Ahmad, Bilal Ahmad
https://arxiv.org/abs/2508.13522 https://arxiv.org/pdf/2508.…

DDoS Attacks in Cloud Computing: Detection and Prevention
DDoS attacks are one of the most prevalent and harmful cybersecurity threats faced by organizations and individuals today. In recent years, the complexity and frequency of DDoS attacks have increased significantly, making it challenging to detect and mitigate them effectively. The study analyzes various types of DDoS attacks, including volumetric, protocol, and application layer attacks, and discusses the characteristics, impact, and potential targets of each type. It also examines the existing…

An ML-Driven Participant Selection Technique for Federated Recommendation System in Edge-Cloud Computing
Jintao Liu, Mohammad Goudarzi, Adel Nadjaran Toosi
https://arxiv.org/abs/2507.15233

An ML-Driven Participant Selection Technique for Federated Recommendation System in Edge-Cloud Computing
Recommendation systems (RS) personalize content by analyzing user preferences, but typically require centralized collection of user data, raising privacy and scalability concerns. Federated Recommendation Systems (FRS) address these issues by enabling distributed, privacy-preserving model training across edge devices, keeping raw data on-device. Although existing FRS frameworks benefit from on-device feature extraction and privacy preservation, they suffer from heterogeneous device capabilities…

The Cost Advantage of Virtual Machine Migrations: Empirical Insights into Amazon's EC2 Marketspace
Benedikt Pittl, Werner Mach, Erich Schikuta
https://arxiv.org/abs/2508.14883

The Cost Advantage of Virtual Machine Migrations: Empirical Insights into Amazon's EC2 Marketspace
In recent years, cloud providers have introduced novel approaches for trading virtual machines. For example, Virtustream introduced so-called muVMs to charge cloud computing resources while other providers such as Google, Microsoft, or Amazon re-invented their marketspaces. Today, the market leader Amazon runs six marketspaces for trading virtual machines. Consumers can purchase bundles of virtual machines, which are called cloud-portfolios, from multiple marketspaces and providers. An industry…

Optimal Parallel Scheduling under Concave Speedup Functions
Chengzhang Li, Peizhong Ju, Atilla Eryilmaz, Ness Shroff
https://arxiv.org/abs/2509.01811 https://

Optimal Parallel Scheduling under Concave Speedup Functions
Efficient scheduling of parallel computation resources across multiple jobs is a fundamental problem in modern cloud/edge computing systems for many AI-based applications. Allocating more resources to a job accelerates its completion, but with diminishing returns. Prior work (heSRPT) solved this problem only for some specific speedup functions with an exponential form, providing a closed-form solution. However, the general case with arbitrary concave speedup functions -- which more accurately c…

Replaced article(s) found for cs.DC. https://arxiv.org/list/cs.DC/new
[1/1]:
- Minos: Exploiting Cloud Performance Variation with Function-as-a-Service Instance Selection
Trever Schirmer, Valentin Carl, Nils H\"oller, Tobias Pfandzelter, David Bermbach

Quantifying the Performance Gap for Simple Versus Optimal Dynamic Server Allocation Policies
Niklas Carlsson, Derek Eager
https://arxiv.org/abs/2507.19667 https://

Quantifying the Performance Gap for Simple Versus Optimal Dynamic Server Allocation Policies
Cloud computing enables the dynamic provisioning of server resources. To exploit this opportunity, a policy is needed for dynamically allocating (and deallocating) servers in response to the current load conditions. In this paper we describe several simple policies for dynamic server allocation and develop analytic models for their analysis. We also design semi-Markov decision models that enable determination of the performance achieved with optimal policies, allowing us to quantify the perform…

Edge Intelligence with Spiking Neural Networks
Shuiguang Deng, Di Yu, Changze Lv, Xin Du, Linshan Jiang, Xiaofan Zhao, Wentao Tong, Xiaoqing Zheng, Weijia Fang, Peng Zhao, Gang Pan, Schahram Dustdar, Albert Y. Zomaya
https://arxiv.org/abs/2507.14069

Edge Intelligence with Spiking Neural Networks
The convergence of artificial intelligence and edge computing has spurred growing interest in enabling intelligent services directly on resource-constrained devices. While traditional deep learning models require significant computational resources and centralized data management, the resulting latency, bandwidth consumption, and privacy concerns have exposed critical limitations in cloud-centric paradigms. Brain-inspired computing, particularly Spiking Neural Networks (SNNs), offers a promisin…

Replaced article(s) found for cs.DC. https://arxiv.org/list/cs.DC/new
[1/1]:
- KubeDSM: A Kubernetes-based Dynamic Scheduling and Migration Framework for Cloud-Assisted Edge Cl...
Amirhossein Pashaeehir, Sina Shariati, Shayan Shafaghi, Manni Moghimi, Mahmoud Momtazpour

Experience Deploying Containerized GenAI Services at an HPC Center
Angel M. Beltre, Jeff Ogden, Kevin Pedretti
https://arxiv.org/abs/2509.20603 https://arx…

Experience Deploying Containerized GenAI Services at an HPC Center
Generative Artificial Intelligence (GenAI) applications are built from specialized components -- inference servers, object storage, vector and graph databases, and user interfaces -- interconnected via web-based APIs. While these components are often containerized and deployed in cloud environments, such capabilities are still emerging at High-Performance Computing (HPC) centers. In this paper, we share our experience deploying GenAI workloads within an established HPC center, discussing the in…

XaaS Containers: Performance-Portable Representation With Source and IR Containers
Marcin Copik, Eiman Alnuaimi, Alok Kamatar, Valerie Hayot-Sasson, Alberto Madonna, Todd Gamblin, Kyle Chard, Ian Foster, Torsten Hoefler
https://arxiv.org/abs/2509.17914

XaaS Containers: Performance-Portable Representation With Source and IR Containers
High-performance computing (HPC) systems and cloud data centers are converging, and containers are becoming the default method of portable software deployment. Yet, while containers simplify software management, they face significant performance challenges in HPC environments as they must sacrifice hardware-specific optimizations to achieve portability. Although HPC containers can use runtime hooks to access optimized MPI libraries and GPU devices, they are limited by application binary interfa…