Tootfinder

OVHcloud Global Environmental Director Grégory Lebourg reveals how a vertically integrated operating model, advanced water-cooling technology, and circular economy practices deliver a cloud that doesn’t cost the earth.
https://www.computing.co.uk/interview/2025

Frugal by design: Why cloud customers shouldn’t have to choose between cost and sustainability
OVHcloud Global Environmental Director Grégory Lebourg reveals how a vertically integrated operating model, advanced water-cooling technology, and circular economy practices deliver a cloud that doesn’t ...

Amazon plans to invest ~$13B in Australia from 2025 to 2029 to develop its data center infrastructure, as demand for cloud computing and AI grows in the country (Ainslie Chandler/Bloomberg)
https://www.bloomberg.com/news/articles/20

Microsoft is using engineers in China
to help maintain the Defense Department’s computer systems
— with minimal supervision by U.S. personnel
— leaving some of the nation’s most sensitive data vulnerable to hacking from its leading cyber adversary,
a ProPublica investigation has found.
The arrangement, which was critical to Microsoft winning the federal government’s cloud computing business a decade ago,
relies on U.S. citizens with security clearances to …

A Little-Known Microsoft Program Could Expose the Defense Department to Chinese Hackers
The Pentagon bans foreign citizens from accessing highly sensitive data, but Microsoft bypasses this by using engineers in China and elsewhere to remotely instruct American “escorts” who may lack expertise to identify malicious code.

Enabling Next-Generation Cloud-Connected Bionic Limbs Through 5G Connectivity
Ozan Karaali, Hossam Farag, Strahinja Dosen, Cedomir Stefanovic
https://arxiv.org/abs/2506.11744

Enabling Next-Generation Cloud-Connected Bionic Limbs Through 5G Connectivity
Despite the recent advancements in human-machine interfacing, contemporary assistive bionic limbs face critical challenges, including limited computational capabilities, high latency, and unintuitive control mechanisms, leading to suboptimal user experience and abandonment rates. Addressing these challenges requires a shift toward intelligent, interconnected solutions powered by advances in Internet of Things systems, particularly wireless connectivity and edge/cloud computing. This article pre…

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 …

A Review of Cloud Computing in Seismology
Yiyu Ni, Marine A. Denolle, Jannes Munchmeyer, Yinzhi Wang, Kuan-Fu Feng, Carlos Garcia Jurado Suarez, Amanda M. Thomas, Chad Trabant, Alex Hamilton, David Mencin
https://arxiv.org/abs/2506.11307

A Review of Cloud Computing in Seismology
Seismology has entered the petabyte era, driven by decades of continuous recordings of broadband networks, the increase in nodal seismic experiments, and the recent emergence of Distributed Acoustic Sensing (DAS). This review explains how commercial clouds - AWS, Google Cloud, and Azure - by providing object storage, elastic compute, and managed databases, enable researchers to "bring the code to the data," thereby overcoming traditional HPC solutions' bandwidth and capacity limitations. After …

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,…

Secure Data Access in Cloud Environments Using Quantum Cryptography
S. Vasavi Venkata Lakshmi, Ziaul Haque Choudhury
https://arxiv.org/abs/2506.10028 https…

Secure Data Access in Cloud Environments Using Quantum Cryptography
Cloud computing has made storing and accessing data easier but keeping it secure is a big challenge nowadays. Traditional methods of ensuring data may not be strong enough in the future when powerful quantum computers become available. To solve this problem, this study uses quantum cryptography to protect data in the cloud environment. Quantum Key Distribution (QKD) creates secure keys by sending information using quantum particles like photons. Specifically, we use the BB84 protocol, a simple …

A Systematic Literature Review on Continuous Integration and Deployment (CI/CD) for Secure Cloud Computing
Sabbir M. Saleh, Nazim Madhavji, John Steinbacher
https://arxiv.org/abs/2506.08055

A Systematic Literature Review on Continuous Integration and Deployment (CI/CD) for Secure Cloud Computing
As cloud environments become widespread, cybersecurity has emerged as a top priority across areas such as networks, communication, data privacy, response times, and availability. Various sectors, including industries, healthcare, and government, have recently faced cyberattacks targeting their computing systems. Ensuring secure app deployment in cloud environments requires substantial effort. With the growing interest in cloud security, conducting a systematic literature review (SLR) is critica…

Topology-Aware Virtualization over Inter-Core Connected Neural Processing Units
Dahu Feng, Erhu Feng, Dong Du, Pinjie Xu, Yubin Xia, Haibo Chen, Rong Zhao
https://arxiv.org/abs/2506.11446

Topology-Aware Virtualization over Inter-Core Connected Neural Processing Units
With the rapid development of artificial intelligence (AI) applications, an emerging class of AI accelerators, termed Inter-core Connected Neural Processing Units (NPU), has been adopted in both cloud and edge computing environments, like Graphcore IPU, Tenstorrent, etc. Despite their innovative design, these NPUs often demand substantial hardware resources, leading to suboptimal resource utilization due to the imbalance of hardware requirements across various tasks. To address this issue, prio…

A Language-Agnostic Logical Relation for Message-Passing Protocols
Tesla Zhang, Sonya Simkin, Rui Li, Yue Yao, Stephanie Balzer
https://arxiv.org/abs/2506.10026

A Language-Agnostic Logical Relation for Message-Passing Protocols
Today's computing landscape has been gradually shifting to applications targeting distributed and *heterogeneous* systems, such as cloud computing and Internet of Things (IoT) applications. These applications are predominantly *concurrent*, employ *message-passing*, and interface with *foreign objects*, ranging from externally implemented code to actual physical devices such as sensors. Verifying that the resulting systems adhere to the intended protocol of interaction is challenging -- the usu…

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…

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?

Sources: CoreWeave will provide computing capacity to Google as part of Google's cloud deal with OpenAI; Google will also supply its own compute to OpenAI (Reuters)
https://www.reuters.com/business/coreweave

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…

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

"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…

Cloud computing startup Crusoe plans to buy ~$400M worth of AI chips from AMD to rent to customers for its US data center that will come online by fall (Max A. Cherney/Reuters)
https://www.reuters.com/business/neocloud-c…

Supporting Intel(r) SGX on Multi-Package Platforms
Simon Johnson, Raghunandan Makaram, Amy Santoni, Vinnie Scarlata
https://arxiv.org/abs/2507.08190 https:…

Supporting Intel(r) SGX on Multi-Package Platforms
Intel(r) Software Guard Extensions (SGX) was originally released on client platforms and later extended to single socket server platforms. As developers have become familiar with the capabilities of the technology, the applicability of this capability in the cloud has been tested. Various Cloud Service Providers (CSPs) are demonstrating the value of using SGX based Trusted Execution Environments (TEE) to create a new paradigm of Confidential Cloud Computing. This paper describes the additional …

Replaced article(s) found for cs.PF. https://arxiv.org/list/cs.PF/new
[1/1]:
- Cloud Computing Energy Consumption Prediction Based on Kernel Extreme Learning Machine Algorithm ...
Yuqing Wang, Xiao Yang

UK Technology Secretary Peter Kyle addressed his critics head-on at Google Cloud London today.
https://www.computing.co.uk/news/2025/peter-kyle-addresses-big-tech-criticisms

Peter Kyle addresses Big Tech criticisms at Google Cloud London
UK Technology Secretary Peter Kyle addressed his critics head-on at Google Cloud London today.

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 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…

Towards Sustainable Computing: Exploring Energy Consumption Efficiency of Alternative Configurations and Workloads in an Open Source Messaging System
Maria Voreakou, George Kousiouris, Mara Nikolaidou
https://arxiv.org/abs/2506.10693

Towards Sustainable Computing: Exploring Energy Consumption Efficiency of Alternative Configurations and Workloads in an Open Source Messaging System
Energy consumption in current large scale computing infrastructures is becoming a critical issue, especially with the growing demand for centralized systems such as cloud environments. With the advancement of microservice architectures and the Internet of Things, messaging systems have become an integral and mainstream part of modern computing infrastructures, carrying out significant workload in a majority of applications. In this paper, we describe an experimental process to explore energy-ba…

Cloud-Based Interoperability in Residential Energy Systems
Darren Leniston, David Ryan, Ammar Malik, Jack Jackman, Terence O'Donnell
https://arxiv.org/abs/2506.05076

Cloud-Based Interoperability in Residential Energy Systems
As distributed energy resources (DERs) such as solar PV, batteries and electric vehicles become increasingly prevalent at the edge, maintaining grid stability requires advanced monitoring and control mechanisms. This paper presents a scalable smart grid gateway architecture that enables interoperability between Modbus-based inverters and IEEE 2030.5 cloud-based control systems. The proposed solution leverages Azure cloud services and edge-computing gateway devices to support dynamic configurati…

Exploring the Convergence of HCI and Evolving Technologies in Information Systems
Rajan Das Gupta, Ashikur Rahman, Md Imrul Hasan Showmick, Md. Yeasin Rahat, Md. Jakir Hossen
https://arxiv.org/abs/2506.08549

Exploring the Convergence of HCI and Evolving Technologies in Information Systems
Modern technology driven information systems are part of our daily lives. However, this deep integration poses new challenges to the human computer interaction (HCI) professionals. With the rapid growth of mobile and cloud computing and the Internet of Things (IoT), the demand for HCI specialists to design user-friendly and adaptable interfaces has never been more pressing. Especially for diverse user groups such as children, the elderly and people with disabilities who need interfaces tailored…

Bericht: AWS baut Hunderte von Stellen ab
Trotz Umsatzplus und positivem Betriebsergebnis im ersten Quartal plant Amazons Cloud-Computing-Einheit AWS laut einem Medienbericht einen großen Personalabbau.
https://www.

This https://arxiv.org/abs/2502.04039 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

A Cloud-native Agile approach to cyber platform prototyping and integration for astronomy: the ENGAGE SKA case
The Square Kilometre Array (SKA) Observatory is gearing up the formal construction of its two radio interferometers in Australia and South Africa after the end of design and pre-construction phases. Agile methodologies, the Cloud native Computing technologies and the DevOps software ideas are influencing the design of compute infrastructures that will be key to reduce the operational costs of SKA while improving the control and monitoring of the SKA antennas and ancillary systems, Correlators, …

Automating Multi-Tenancy Performance Evaluation on Edge Compute Nodes
Joanna Georgiou, Moysis Symeonides, George Pallis, Marios D. Dikaiakos
https://arxiv.org/abs/2506.10461

Automating Multi-Tenancy Performance Evaluation on Edge Compute Nodes
Edge Computing emerges as a promising alternative of Cloud Computing, with scalable compute resources and services deployed in the path between IoT devices and Cloud. Since virtualization techniques can be applied on Edge compute nodes, administrators can share their Edge infrastructures among multiple users, providing the so-called multi-tenancy. Even though multi-tenancy is unavoidable, it raises concerns about security and performance degradation due to resource contention in Edge Computing.…

FIDESlib: A Fully-Fledged Open-Source FHE Library for Efficient CKKS on GPUs
Carlos Agull\'o-Domingo (Universidad de Murcia), \'Oscar Vera-L\'opez (Universidad de Murcia), Seyda Guzelhan (Boston University), Lohit Daksha (Boston University), Aymane El Jerari (Northeastern University), Kaustubh Shivdikar (Advanced Micro Devices), Rashmi Agrawal (Boston University), David Kaeli (Northeastern University), Ajay Joshi (Boston University), Jos\'e L. Abell\'an (Universidad…

FIDESlib: A Fully-Fledged Open-Source FHE Library for Efficient CKKS on GPUs
Word-wise Fully Homomorphic Encryption (FHE) schemes, such as CKKS, are gaining significant traction due to their ability to provide post-quantum-resistant, privacy-preserving approximate computing; an especially desirable feature in Machine-Learning-as-a-Service (MLaaS) cloud-computing paradigms. OpenFHE is a leading CPU-based FHE library with robust CKKS operations, but its server-side performance is not yet sufficient for practical cloud deployment. As GPU computing becomes more common in da…

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…

Sources: OpenAI plans to add Google Cloud to meet its growing needs for computing capacity, a surprising collaboration; source: the deal was finalized in May (Reuters)
https://www.reuters.com/business/retail-co

Online Job Assignment
Farbod Ekbatani, Yiding Feng, Ian Kash, Rad Niazadeh
https://arxiv.org/abs/2506.06893 https://arxiv.org/pdf/250…

Online Job Assignment
Motivated primarily by applications in cloud computing, we study a simple, yet powerful, online allocation problem in which jobs of varying durations arrive over continuous time and must be assigned immediately and irrevocably to one of the available offline servers. Each server has a fixed initial capacity, with assigned jobs occupying one unit for their duration and releasing it upon completion. The algorithm earns a reward for each assignment upon completion. We consider a general heterogene…

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-…

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 ...

Nvidia plans to build its first industrial AI cloud platform in Germany for European manufacturers, combining AI with robotics to assist carmakers like BMW (Reuters)
https://www.reuters.com/business/nvidia-ceo-says-quantum-comp…

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…

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 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.

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…

A Hybrid Intrusion Detection System with a New Approach to Protect the Cybersecurity of Cloud Computing
Maryam Mahdi Al-Husseini
https://arxiv.org/abs/2506.19934

A Hybrid Intrusion Detection System with a New Approach to Protect the Cybersecurity of Cloud Computing
Cybersecurity is one of the foremost challenges facing the world of cloud computing. Recently, the widespread adoption of smart devices in cloud computing environments that provide Internet-based services has become prevalent. Therefore, it is essential to consider the security threats in these environments. The use of intrusion detection systems can mitigate the vulnerabilities of these systems. Furthermore, hybrid intrusion detection systems can provide better protection compared to conventio…

Despite the country's financial straits, this is a wonderful time to be a technologist in government, according to Paul Mukherjee, CTO of the UK Department for Environment, Food and Rural Affairs (Defra).
https://www.computing.co.uk/news/2025/ai/g

Government CTO: 'I had a lightbulb moment with agentic AI'
Defra’s CTO believes AI may help government thinking move from processes to outcomes, while agents could assist in complex technical tasks like cloud migrations.

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…

This https://arxiv.org/abs/2506.02709 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

Usability Evaluation of Cloud for HPC Applications
The rise of AI and the economic dominance of cloud computing have created a new nexus of innovation for high performance computing (HPC), which has a long history of driving scientific discovery. In addition to performance needs, scientific workflows increasingly demand capabilities of cloud environments: portability, reproducibility, dynamism, and automation. As converged cloud environments emerge, there is growing need to study their fit for HPC use cases. Here we present a cross-platform usa…

This type of reasoning is always baffling to me. When climate change is discussed these people always say that there is some magical technological solution that will pop up to save us (usually handed to us by the AI gods).
Why then, in the the several decades that it takes to scale up nuclear, can we not account for the possibility that AI could become more energy efficient?
That sounds like something you could solve for the cost of a few power plants...

UK needs more nuclear to power AI, says Amazon Web Services boss
The world's largest cloud computing company plans to spend £8bn on new data centres in the UK over the next four years.

Device-Cloud Collaborative Correction for On-Device Recommendation
Tianyu Zhan, Shengyu Zhang, Zheqi Lv, Jieming Zhu, Jiwei Li, Fan Wu, Fei Wu
https://arxiv.org/abs/2506.12687

Device-Cloud Collaborative Correction for On-Device Recommendation
With the rapid development of recommendation models and device computing power, device-based recommendation has become an important research area due to its better real-time performance and privacy protection. Previously, Transformer-based sequential recommendation models have been widely applied in this field because they outperform Recurrent Neural Network (RNN)-based recommendation models in terms of performance. However, as the length of interaction sequences increases, Transformer-based mo…

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…

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 …

This https://arxiv.org/abs/2503.23776 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDB_…

VIDEX: A Disaggregated and Extensible Virtual Index for the Cloud and AI Era
Virtual index, also known as hypothetical indexes, play a crucial role in database query optimization. However, with the rapid advancement of cloud computing and AI-driven models for database optimization, traditional virtual index approaches face significant challenges. Cloud-native environments often prohibit direct conducting query optimization process on production databases due to stability requirements and data privacy concerns. Moreover, while AI models show promising progress, their int…

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…

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…

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…

OpenAI says it will use the Google Cloud Platform for ChatGPT and its API, adding it to a list of suppliers that also includes Microsoft, CoreWeave, and Oracle (Jordan Novet/CNBC)
https://www.cnbc.com/2025/07/16/openai-googles-cloud-chatgpt.html

OpenAI says it will use Google's cloud for ChatGPT
Having faced supply shortages, OpenAI will now be drawing on computing resources from CoreWeave, Google, Microsoft and Oracle.

Sustainability Flags for the Identification of Sustainability Posts in Q&A Platforms
Sahar Ahmadisakha, Lech Bialek, Mohamed Soliman, Vasilios Andrikopoulos
https://arxiv.org/abs/2507.02695

Sustainability Flags for the Identification of Sustainability Posts in Q&A Platforms
In recent years, sustainability in software systems has gained significant attention, especially with the rise of cloud computing and the shift towards cloud-based architectures. This shift has intensified the need to identify sustainability in architectural discussions to take informed architectural decisions. One source to see these decisions is in online Q&A forums among practitioners' discussions. However, recognizing sustainability concepts within software practitioners' discussions remain…

European cloud providers and enterprise IT customers are sounding alarms over changes to VMware's licensing structure that have driven prices up by as much as 1,500% since Broadcom's acquisition of the virtualisation giant.
https://www.computing.co.uk/news/20…

European customers rail against Broadcom's 1,500% VMware price hike
A new report concludes that Broadcom’s price rises are leaving European customers without viable VMware alternatives.

A Global-scale Database of Seismic Phases from Cloud-based Picking at Petabyte Scale
Yiyu Ni, Marine A. Denolle, Amanda M. Thomas, Alex Hamilton, Jannes M\"unchmeyer, Yinzhi Wang, Lo\"ic Bachelot, Chad Trabant, David Mencin
https://arxiv.org/abs/2505.18874

A Global-scale Database of Seismic Phases from Cloud-based Picking at Petabyte Scale
We present the first global-scale database of 4.3 billion P- and S-wave picks extracted from 1.3 PB continuous seismic data via a cloud-native workflow. Using cloud computing services on Amazon Web Services, we launched ~145,000 containerized jobs on continuous records from 47,354 stations spanning 2002-2025, completing in under three days. Phase arrivals were identified with a deep learning model, PhaseNet, through an open-source Python ecosystem for deep learning, SeisBench. To visualize and …

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 …

Usability Evaluation of Cloud for HPC Applications
Vanessa Sochat, Daniel Milroy, Abhik Sarkar, Aniruddha Marathe
https://arxiv.org/abs/2506.02709 https://…

Usability Evaluation of Cloud for HPC Applications
The rise of AI and the economic dominance of cloud computing have created a new nexus of innovation for high performance computing (HPC), which has a long history of driving scientific discovery. In addition to performance needs, scientific workflows increasingly demand capabilities of cloud environments: portability, reproducibility, dynamism, and automation. As converged cloud environments emerge, there is growing need to study their fit for HPC use cases. Here we present a cross-platform usa…

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 …

“Microsoft has no magical 'get out of US surveillance law' card to play, and its sovereign private cloud is writing checks it cannot cash."
https://www.computing.co.uk/news/2025/microsoft-announces-new-sov…

Microsoft has announced new 'sovereign cloud' services - but just how sovereign can they be?
Microsoft has announced new ‘sovereign cloud’ services for Europe in response to regulations and concerns about control, security and privacy.

RL-based Adaptive Task Offloading in Mobile-Edge Computing for Future IoT Networks
Ziad Qais Al Abbasi, Khaled M. Rabie, Senior Member, Xingwang Li, Senior Member, Wali Ullah Khan, Asma Abu Samah
https://arxiv.org/abs/2506.22474

RL-based Adaptive Task Offloading in Mobile-Edge Computing for Future IoT Networks
The Internet of Things (IoT) has been increasingly used in our everyday lives as well as in numerous industrial applications. However, due to limitations in computing and power capabilities, IoT devices need to send their respective tasks to cloud service stations that are usually located at far distances. Having to transmit data far distances introduces challenges for services that require low latency such as industrial control in factories and plants as well as artificial intelligence assiste…

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.

This https://arxiv.org/abs/2504.11540 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDB_…

Pruning in Snowflake: Working Smarter, Not Harder
Modern cloud-based data analytics systems must efficiently process petabytes of data residing on cloud storage. A key optimization technique in state-of-the-art systems like Snowflake is partition pruning - skipping chunks of data that do not contain relevant information for computing query results. While partition pruning based on query predicates is a well-established technique, we present new pruning techniques that extend the scope of partition pruning to LIMIT, top-k, and JOIN operations…

Simultaneously Fair Allocation of Indivisible Items Across Multiple Dimensions
Yasushi Kawase, Bodhayan Roy, Mohammad Azharuddin Sanpui
https://arxiv.org/abs/2506.21727

Simultaneously Fair Allocation of Indivisible Items Across Multiple Dimensions
This paper explores the fair allocation of indivisible items in a multidimensional setting, motivated by the need to address fairness in complex environments where agents assess bundles according to multiple criteria. Such multidimensional settings are not merely of theoretical interest but are central to many real-world applications. For example, cloud computing resources are evaluated based on multiple criteria such as CPU cores, memory, and network bandwidth. In such cases, traditional one d…

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…

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 …

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…

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…

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…

Domain-Adversarial Transfer Learning for Fault Root Cause Identification in Cloud Computing Systems
Bruce Fang, Danyi Gao
https://arxiv.org/abs/2507.02233 …

Domain-Adversarial Transfer Learning for Fault Root Cause Identification in Cloud Computing Systems
This paper addresses the challenge of fault root cause identification in cloud computing environments. The difficulty arises from complex system structures, dense service coupling, and limited fault information. To solve this problem, an intelligent identification algorithm based on transfer learning is proposed. The method introduces a shared feature extraction module and a domain adversarial mechanism to enable effective knowledge transfer from the source domain to the target domain. This imp…

Real-Time Agile Software Management for Edge and Fog Computing Based Smart City Infrastructure
Debasish Jana, Pinakpani Pal, Pawan Kumar
https://arxiv.org/abs/2506.12616

Real-Time Agile Software Management for Edge and Fog Computing Based Smart City Infrastructure
The evolution of smart cities demands scalable, secure, and energy-efficient architectures for real-time data processing. With the number of IoT devices expected to exceed 40 billion by 2030, traditional cloud-based systems are increasingly constrained by bandwidth, latency, and energy limitations. This paper leverages the ROOF (Real-time Onsite Operations Facilitation) framework with decentralized computing at intermediary fog and peripheral edge network layers to reduce latency by processing …

Design high-confidence computers using trusted instructional set architecture and emulators
Shuangbao Paul Wang
https://arxiv.org/abs/2506.18780 https://…

Design high-confidence computers using trusted instructional set architecture and emulators
High-confidence computing relies on trusted instructional set architecture, sealed kernels, and secure operating systems. Cloud computing depends on trusted systems for virtualization tasks. Branch predictions and pipelines are essential in improving performance of a CPU/GPU. But Spectre and Meltdown make modern processors vulnerable to be exploited. Disabling the prediction and pipeline is definitely not a good solution. On the other hand, current software patches can only address non-essentia…

Distributed Intelligence in the Computing Continuum with Active Inference
Victor Casamayor Pujol, Boris Sedlak, Tommaso Salvatori, Karl Friston, Schahram Dustdar
https://arxiv.org/abs/2505.24618

Distributed Intelligence in the Computing Continuum with Active Inference
The Computing Continuum (CC) is an emerging Internet-based computing paradigm that spans from local Internet of Things sensors and constrained edge devices to large-scale cloud data centers. Its goal is to orchestrate a vast array of diverse and distributed computing resources to support the next generation of Internet-based applications. However, the distributed, heterogeneous, and dynamic nature of CC platforms demands distributed intelligence for adaptive and resilient service management. Th…

Balancing Fixed Number of Nodes Among Multiple Fixed Clusters
Paritosh Ranjan, Surajit Majumder, Prodip Roy, Bhuban Padhan
https://arxiv.org/abs/2506.08715

Balancing Fixed Number of Nodes Among Multiple Fixed Clusters
Cloud infrastructure users often allocate a fixed number of nodes to individual container clusters (e.g., Kubernetes, OpenShift), resulting in underutilization of computing resources due to asynchronous and variable workload peaks across clusters. This research proposes a novel system and method for dynamic rebalancing of a fixed total number of nodes among multiple fixed clusters based on real-time resource utilization thresholds. By introducing a Node Balancing Cluster Group (NBCG), clusters …

Service Placement in Small Cell Networks Using Distributed Best Arm Identification in Linear Bandits
Mariam Yahya, Aydin Sezgin, Setareh Maghsudi
https://arxiv.org/abs/2506.22480 …

Service Placement in Small Cell Networks Using Distributed Best Arm Identification in Linear Bandits
As users in small cell networks increasingly rely on computation-intensive services, cloud-based access often results in high latency. Multi-access edge computing (MEC) mitigates this by bringing computational resources closer to end users, with small base stations (SBSs) serving as edge servers to enable low-latency service delivery. However, limited edge capacity makes it challenging to decide which services to deploy locally versus in the cloud, especially under unknown service demand and dy…

Autonomic Microservice Management via Agentic AI and MAPE-K Integration
Matteo Esposito, Alexander Bakhtin, Noman Ahmad, Mikel Robredo, Ruoyu Su, Valentina Lenarduzzi, Davide Taibi
https://arxiv.org/abs/2506.22185

Autonomic Microservice Management via Agentic AI and MAPE-K Integration
While microservices are revolutionizing cloud computing by offering unparalleled scalability and independent deployment, their decentralized nature poses significant security and management challenges that can threaten system stability. We propose a framework based on MAPE-K, which leverages agentic AI, for autonomous anomaly detection and remediation to address the daunting task of highly distributed system management. Our framework offers practical, industry-ready solutions for maintaining ro…

Collaborative Multi-Agent Reinforcement Learning Approach for Elastic Cloud Resource Scaling
Bruce Fang, Danyi Gao
https://arxiv.org/abs/2507.00550 https:/…

Collaborative Multi-Agent Reinforcement Learning Approach for Elastic Cloud Resource Scaling
This paper addresses the challenges of rapid resource variation and highly uncertain task loads in cloud computing environments. It proposes an optimization method for elastic cloud resource scaling based on a multi-agent system. The method deploys multiple autonomous agents to perceive resource states in parallel and make local decisions. While maintaining the distributed nature of the system, it introduces a collaborative value function to achieve global coordination. This improves the respon…

Performance Measurements in the AI-Centric Computing Continuum Systems
Praveen Kumar Donta, Qiyang Zhang, Schahram Dustdar
https://arxiv.org/abs/2506.22884

Performance Measurements in the AI-Centric Computing Continuum Systems
Over the Eight decades, computing paradigms have shifted from large, centralized systems to compact, distributed architectures, leading to the rise of the Distributed Computing Continuum (DCC). In this model, multiple layers such as cloud, edge, Internet of Things (IoT), and mobile platforms work together to support a wide range of applications. Recently, the emergence of Generative AI and large language models has further intensified the demand for computational resources across this continuum…

Performance Assessment of Load Balancing Methods in Cloud Computing: Analysis of Round Robin, Equally Spread, and Throttled Strategies Using Cloud Analyst
Saeid Aghasoleymani Najafabadi
https://arxiv.org/abs/2507.11899

Performance Assessment of Load Balancing Methods in Cloud Computing: Analysis of Round Robin, Equally Spread, and Throttled Strategies Using Cloud Analyst
Load balancing plays a pivotal role in cloud computing, ensuring that resources are optimally allocated to maintain high service quality and operational efficiency. As workloads in cloud environments become increasingly dynamic and unpredictable, load balancing strategies are evolving from traditional static methods to more adaptive and intelligent approaches. In this study, the Cloud Analyst simulation tool was used to evaluate the performance of different load balancing algorithms under vario…

This https://arxiv.org/abs/2505.21199 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

Multi-Event Triggers for Serverless Computing
Function-as-a-Service (FaaS) is an event-driven serverless cloud computing model in which small, stateless functions are invoked in response to events, such as HTTP requests, new database entries, or messages. Current FaaS platform assume that each function invocation corresponds to a single event. However, from an application perspective, it is desirable to invoke functions in response to a collection of events of different types or only with every n\textsuperscript{th} event. To implement thi…

CRAFT: Latency and Cost-Aware Genetic-Based Framework for Node Placement in Edge-Fog Environments
Soheil Mahdizadeh, Amir Mahdi Rasouli, Mohammad Pourashory, Sadra Galavani, Mohsen Ansari
https://arxiv.org/abs/2507.12445

CRAFT: Latency and Cost-Aware Genetic-Based Framework for Node Placement in Edge-Fog Environments
Reducing latency in the Internet of Things (IoT) is a critical concern. While cloud computing facilitates communication, it falls short of meeting real-time requirements reliably. Edge and fog computing have emerged as viable solutions by positioning computing nodes closer to end users, offering lower latency and increased processing power. An edge-fog framework comprises various components, including edge and fog nodes, whose strategic placement is crucial as it directly impacts latency and sy…

CFTel: A Practical Architecture for Robust and Scalable Telerobotics with Cloud-Fog Automation
Thien Tran, Jonathan Kua, Minh Tran, Honghao Lyu, Thuong Hoang, Jiong Jin
https://arxiv.org/abs/2506.17991

CFTel: A Practical Architecture for Robust and Scalable Telerobotics with Cloud-Fog Automation
Telerobotics is a key foundation in autonomous Industrial Cyber-Physical Systems (ICPS), enabling remote operations across various domains. However, conventional cloud-based telerobotics suffers from latency, reliability, scalability, and resilience issues, hindering real-time performance in critical applications. Cloud-Fog Telerobotics (CFTel) builds on the Cloud-Fog Automation (CFA) paradigm to address these limitations by leveraging a distributed Cloud-Edge-Robotics computing architecture, e…

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…

The Cloud Next Door: Investigating the Environmental and Socioeconomic Strain of Datacenters on Local Communities
Wacuka Ngata, Noman Bashir, Michelle Westerlaken, Laurent Liote, Yasra Chandio, Elsa Olivetti
https://arxiv.org/abs/2506.03367

The Cloud Next Door: Investigating the Environmental and Socioeconomic Strain of Datacenters on Local Communities
Datacenters have become the backbone of modern digital infrastructure, powering the rapid rise of artificial intelligence and promising economic growth and technological progress. However, this expansion has brought growing tensions in the local communities where datacenters are already situated or being proposed. While the mainstream discourse often focuses on energy usage and carbon footprint of the computing sector at a global scale, the local socio-environmental consequences -- such as heal…

Emerging Trends in Software Architecture from the Practitioners Perspective: A Five Year Review
Ruoyu Su, Noman ahmad, Matteo Esposito, Andrea Janes, Davide Taibi, Valentina Lenarduzzi
https://arxiv.org/abs/2507.14554

Emerging Trends in Software Architecture from the Practitioners Perspective: A Five Year Review
Software architecture plays a central role in the design, development, and maintenance of software systems. With the rise of cloud computing, microservices, and containers, architectural practices have diversified. Understanding these shifts is vital. This study analyzes software architecture trends across eight leading industry conferences over five years. We investigate the evolution of software architecture by analyzing talks from top practitioner conferences, focusing on the motivations and…

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…

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…

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…

Accelerating Cloud-Based Transcriptomics: Performance Analysis and Optimization of the STAR Aligner Workflow
Piotr Kica, Sabina Licho{\l}ai, Micha{\l} Orzechowski, Maciej Malawski
https://arxiv.org/abs/2506.12611

Accelerating Cloud-Based Transcriptomics: Performance Analysis and Optimization of the STAR Aligner Workflow
In this work, we explore the Transcriptomics Atlas pipeline adapted for cost-efficient and high-throughput computing in the cloud. We propose a scalable, cloud-native architecture designed for running a resource-intensive aligner -- STAR -- and processing tens or hundreds of terabytes of RNA-sequencing data. We implement multiple optimization techniques that give significant execution time and cost reduction. The impact of particular optimizations is measured in medium-scale experiments followe…

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…

STRATUS: A Multi-agent System for Autonomous Reliability Engineering of Modern Clouds
Yinfang Chen, Jiaqi Pan, Jackson Clark, Yiming Su, Noah Zheutlin, Bhavya Bhavya, Rohan Arora, Yu Deng, Saurabh Jha, Tianyin Xu
https://arxiv.org/abs/2506.02009

STRATUS: A Multi-agent System for Autonomous Reliability Engineering of Modern Clouds
In cloud-scale systems, failures are the norm. A distributed computing cluster exhibits hundreds of machine failures and thousands of disk failures; software bugs and misconfigurations are reported to be more frequent. The demand for autonomous, AI-driven reliability engineering continues to grow, as existing humanin-the-loop practices can hardly keep up with the scale of modern clouds. This paper presents STRATUS, an LLM-based multi-agent system for realizing autonomous Site Reliability Engine…

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…

A Model Aware AIGC Task Offloading Algorithm in IIoT Edge Computing
Xin Wang, Xiao Huan Li, Xun Wang
https://arxiv.org/abs/2507.11560 https://

A Model Aware AIGC Task Offloading Algorithm in IIoT Edge Computing
The integration of the Industrial Internet of Things (IIoT) with Artificial Intelligence-Generated Content (AIGC) offers new opportunities for smart manufacturing, but it also introduces challenges related to computation-intensive tasks and low-latency demands. Traditional generative models based on cloud computing are difficult to meet the real-time requirements of AIGC tasks in IIoT environments, and edge computing can effectively reduce latency through task offloading. However, the dynamic n…

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

The AI Shadow War: SaaS vs. Edge Computing Architectures
Rhea Pritham Marpu, Kevin J McNamara, Preeti Gupta
https://arxiv.org/abs/2507.11545 https://

The AI Shadow War: SaaS vs. Edge Computing Architectures
The very DNA of AI architecture presents conflicting paths: centralized cloud-based models (Software-as-a-Service) versus decentralized edge AI (local processing on consumer devices). This paper analyzes the competitive battleground across computational capability, energy efficiency, and data privacy. Recent breakthroughs show edge AI challenging cloud systems on performance, leveraging innovations like test-time training and mixture-of-experts architectures. Crucially, edge AI boasts a 10,000x…

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…

This https://arxiv.org/abs/2505.16508 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

Edge-First Language Model Inference: Models, Metrics, and Tradeoffs
The widespread adoption of Language Models (LMs) across industries is driving interest in deploying these services across the computing continuum, from the cloud to the network edge. This shift aims to reduce costs, lower latency, and improve reliability and privacy. Small Language Models (SLMs), enabled by advances in model compression, are central to this shift, offering a path to on-device inference on resource-constrained edge platforms. This work examines the interplay between edge and clo…

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…

Optimizing Edge Gaming Slices through an Enhanced User Plane Function and Analytics in Beyond-5G Networks
Bruno Marques da Silva, Larissa Ferreira Rodrigues Moreira, Fl\'avio de Oliveira Silva, Rodrigo Moreira
https://arxiv.org/abs/2507.17843

Optimizing Edge Gaming Slices through an Enhanced User Plane Function and Analytics in Beyond-5G Networks
The latest generation of games and pervasive communication technologies poses challenges in service management and Service-Level Agreement compliance for mobile users. State-of-the-art edge-gaming techniques enhance throughput, reduce latency, and leverage cloud computing. However, further development of core functions such as the User Plane Function (UPF) is needed for non-intrusive user latency measurement. This paper proposes a closed-loop architecture integrating the Network Data Analytics …