This study explores the performance of CP2K on a heterogeneous HPC system integrating CPU and GPDSP, aiming to optimize computational efficiency for large-scale molecular simulations. CP2K is an open-source software package designed for simulating condensed matter systems, particularly excelling in handling complex quantum chemistry and molecular dynamics workloads. We present the integration of CPU

Autoscaling technology enables cloud-native systems to adapt to dynamic workload changes by scaling outward or inward without manual intervention. However, when facing sudden and unpredictable workloads, it becomes particularly difficult to determine which services need to be scaled and to assess the amount of resources required, especially for complex time-varying service dependencies that are difficult

In an increasingly connected world, technologies such as smartphones, 5G, drones, the Internet of Things, and Smart Cities bring new challenges and opportunities. The increase in data collected by these devices and their ease of access allows the use of machine learning techniques to provide intelligent and quality services. Considering these services’ distributed access to data, using Federated Learning

Deep neural network (DNN) models have demonstrated significant success in large-scale image datasets, facilitating information exchange over networks for various purposes, including user identification, remote patient health monitoring, early disease detection, and personalized medical treatments. Given the increasing reliance on DNN models for critical applications, ensuring their copyright protection

Quantum circuit mapping is a critical process in quantum computing that involves adapting logical quantum circuits to adhere to hardware constraints, thereby generating physically executable quantum circuits. Current quantum circuit mapping techniques, such as solver-based methods, often encounter challenges related to slow solving speeds due to factors like redundant search iterations. Regarding this

Federated Learning (FL) is a distributed learning mechanism that enables multiple clients to collaboratively train a global model (e.g. a neural network) while maintaining the privacy of their data. However, FL is susceptible to adversarial attacks, especially those involving poisoned samples. Despite significant research efforts, adversarial attacks and defenses in FL remain an unresolved issue. In

As data centers and cloud environments expand, enhancing fairness in I/O queue resource scheduling has become increasingly urgent in the field of Non-Volatile Memory Express (NVMe) storage virtualization. Existing methods usually focus on metrics such as Input/Output Operations Per Second (IOPS) enhancement or latency reduction, overlooking fairness issues among virtual machines (VMs) which may lead

Class imbalance and high-dimensional data pose significant challenges in intrusion detection systems (IDSs), impacting model performance and interpretability. This paper introduces a novel approach, CTGAN-ENN, combining explainable Conditional Tabular generative adversarial networks (CTGAN) and Edited Nearest Neighbor (ENN) with feature selection (FS) for improving IDS interpretability. The framework

Realizing the Ocean Internet of Things (OIoT) requires diverse and comprehensive real-time marine data. This creates significant demands on spectrum resources for effective underwater communication. Coexisting users in the OIoT can generate massive amounts of data, which results in competition for underwater acoustic spectrum. To address the problem of poor utilization of this spectrum in complex and

Personal data, as an important category of data elements of a trusted data circulation, needs to be shared to others in “one-to-one” mode or “one-to-many” mode with simultaneously, parallelly or sequentially to meet all kinds of complex business scenarios. Actually, today’s application asks more personalized content and more often than ever before, it increasingly highlights the need for data owners

Conventional error-tolerant schemes for Neural Networks (NNs) usually require either redundancy, or changes in normal operation, leading to considerable overheads. They are not feasible for large-scale Machine Learning (ML) systems that typically employ several complex networks. This paper proposes a Perturbation-Based Error Detection and Correction (PBEDC) scheme designed to perform error detection

Denoising Diffusion Models (DDMs) have attracted significant attention due to their capacity to generate diverse, high-quality samples in computer vision tasks, offering flexible architectures and straightforward training processes. While several studies have extended diffusion models to quantum domains, these approaches often rely on hybrid U-net architectures or mixed-state manipulations with trace-out

Kubernetes, as a powerful tool for managing containerized applications, is considered a promising tool for supporting cloud computing platforms. The default scheduling scoring strategy only considers seeking an optimal node for the current pod and ignores the availability of subsequent nodes. Additionally, the node with the highest overall score may not necessarily be the most suitable node for the

Heterogeneous GPU clusters are crucial for high-performance computing and deep learning tasks, offering a flexible and cost-effective platform. GPU sharing allows multiple containers to concurrently access the same physical GPU, improving overall GPU usage. However, underutilization of GPU resources remains a significant challenge, primarily due to inefficient resource allocation and fragmentation

Drone fleets with onboard cameras coupled with computer vision and DNN inferencing models can support diverse applications, from package deliveries to disaster monitoring. One such novel domain is for one or more “buddy” drones to assist Visually Impaired People (VIPs) lead an active lifestyle. Video inferencing tasks from such drones can help both navigate the drone and provide situation awareness

Federated Learning (FL) allows a Machine Learning (ML) model to be trained collaboratively among distributed devices while preserving the privacy of the data being used for the training. On the other hand, Hierarchical Federated Learning (HFL) is the extended architecture of FL, which consists of additional edge servers for partial aggregation. FL is very useful in privacy-preserving machine learning

Given the increasing number and severity of cyber attacks, there has been a surge in cybersecurity information across various mediums such as posts, news articles, reports, and other resources. Cyber Threat Intelligence (CTI) involves processing data from these cybersecurity sources, enabling professionals and organizations to gain valuable insights. However, with the rapid dissemination of cybersecurity

With the rapid evolution of programmable network devices and the urge for energy-efficient and sustainable computing, network infrastructures are mutating toward a computing pipeline, providing In-Network Computing (INC) capability. Despite the initial success in offloading single/small kernels to the network devices, deploying multi-kernel applications remains challenging due to limited memory, computing

Serverless computing has gained a strong traction in the cloud computing community in recent years. Among the many benefits of this novel computing model, the rapid auto-scaling capability of user applications takes prominence. However, the offer of adhoc scaling of user deployments at function level introduces many complications to serverless systems. The added delay and failures in function request

Contextual data in smart cities are present in large quantities and distributed sources. Many applications can benefit from these data to provide better services to their users. The scale and dynamic nature of urban environments pose significant challenges in making context sources available to applications. These challenges involve transparent access to context, resilience, decentralization, extensibility

Edge computing increasingly collaborates with cloud computing to support numerous applications that involve large data volumes and frequent data interactions. In cloud-edge collaboration environments, applications especially with high requirements for low data transmission delay often deploy frequently accessed client data replicas on edge servers to improve data access efficiency. Consequently, client

Vertical Federated Learning (VFL) enables multiple parties with distinct feature spaces to train a joint VFL model collaboratively without exposing their original private data. In realistic scenarios, the scarcity of aligned and labeled samples among collaborating participants limits the effectiveness of traditional VFL approaches for model training. Current VFL frameworks attempt to leverage abundant

Sorting is becoming increasingly important in modern computing, ranging from small-scale Internet of Things (IoT) devices to supercomputers. To improve sorting performance, various algorithms, including Intro sort, Merge sort, Heap sort, and Insertion sort, are adopted in different systems. However, the performance of sorting algorithms depends on various factors, and our analysis shows that the optimal

Mobile edge computing (MEC) can enhance the computation performance of end-devices by providing computation offloading service at the network edge. However, given that both end-devices and edge servers have finite computation resources, inefficient offloading policies may lead to overload, thereby increasing the computation delays of tasks. In this paper, we investigate a multi-task partial computation

Database-as-a-Service(DBaaS) provides services for multiple tenants through resource containers, which are allowed to scale over time to fulfill the service-level agreements. Designing container auto-scaling methods for DBaaS can help reduce their expenditure. Reinforcement Learning (RL) shows powerful performance in cloud resource scaling due to its robustness in dynamic environments. However, the

Drug discovery is a long and costly process aimed at finding a molecule that yields a therapeutic effect. Virtual screening is one of the initial in-silico steps that aims at estimating how promising a molecule is. This stage needs to solve two well-known domain problems: molecular docking and scoring. While the accuracy of scoring functions is extensively investigated in comparisons, the execution

Federated learning (FL) trains a global model by aggregating local training gradients, but private information can be leaked from these gradients. To enhance privacy, differential privacy (DP) is often used by adding artificial noise. However, this approach reduces accuracy compared to noise-free learning. Balancing privacy protection and model accuracy remains a key challenge for DP-based FL. Additionally

This special issue aims to present new developments and advances in techniques for assessment performance portability of high performance computing applications. It contains revised and extended versions of selected papers presented at the 10th Workshop on Language-Based Parallel Programming Models, WLPP 2024, which was a part of 15th International Conference on Parallel Processing and Applied Mathematics

Digital twins are widely used for building various types of cyber–physical systems. There are a huge number of publications devoted to the use of digital twins in production systems. Much less attention is paid to the issues of building runtime digital twins. The article describes an approach to building complex distributed cyber–physical systems with a high level of architectural dynamics built on

Many data-intensive and distributed applications rely on low-latency and scalable key–value storage systems across the Computing Continuum. Key–value storage systems typically use consistent hashing or hash slot-sharding mechanisms to distribute data across storage nodes, which ensures load balancing but often leads to sub-optimal response times and monetary costs, particularly in geo-distributed systems

In current blockchain verifiable query research, redundant storage of data to be indexed is often required to enable efficient and feature-rich query algorithms. However, most blockchains currently face the problem of rapid data growth, leading to significant storage resource consumption by nodes. To provide a high-efficiency and generic verifiable query capability while reducing the storage burden

Federated Learning (FL) aims to allow numerous clients to participate in collaborative training in an efficient communication manner without exchanging private data. Traditional FL assumes that all clients have sufficient local resources to train models with the same architecture, and does not consider the reality that clients may struggle to deploy the same model across devices with varying computational

Federated Learning (FL) is an emerging technology in collaborative machine learning, where multiple data owners train a unified model by exchanging model parameters instead of private data. Despite providing data privacy and a wide range of applications, FL faces several challenges, such as slow convergence, high computation and communication costs, and security in parameter sharing. In this paper

The integration of physical and digital systems is fundamental to enabling intelligent, adaptive, and scalable solutions in modern IoT environments. This paper explores Fluid Digital Twins (FDTs), a novel framework combining Fluid Computing (FC) principles with Digital Twin (DT) technology, to address challenges related to interoperability, dynamic functionality, and adaptability in IoT ecosystems

In recent years, the rapid development of 5G/6G networks has connected billions of IoT devices, generating massive amounts of data. Efficiently collecting, processing, and analyzing this data is crucial for gaining valuable insights and improving decision-making. However, the underlying communication networks face significant challenges in securely and scalably managing these devices, impacting infrastructure

In recent years, the application domains of unmanned swarms have been continuously expanding. Existing swarm navigation methods predominantly rely on communication networks for frequently information exchange to achieve stable navigation behavior. However, this reliance presents challenges in achieving coordinated cooperative behavior in communication-restricted and obstacle-rich environments. To ensure

Modern vehicles are equipped with high-performance compute systems. These compute resources mostly stay idle as most of the time vehicles get parked in the parking lots. In this work, we propose to utilize the unused compute resources of the vehicles efficiently to enhance the computing power of regular cloud systems, which is termed as vehicular cloud. Unlike in traditional cloud computing resources

Federated learning (FL) has emerged as a powerful framework for privacy-preserving machine learning, especially relevant in fields like healthcare, finance, and mobile devices. Despite its success, traditional FL systems have a significant limitation: they rely on a static set of clients, forming a federation at the beginning of the training process, which remains fixed throughout the training cycle

With the rapid development of Decentralized Finance (DeFi) and Real-World Assets (RWA), the importance of blockchain oracles in real-time data acquisition has become increasingly prominent. Using cryptographic techniques, threshold signature oracles can achieve consensus on data from multiple nodes and provide corresponding proofs to ensure the credibility and security of the information. However,

Federated learning has attracted widespread attention due to its privacy-preserving characteristic. However, in real-world scenarios, the heterogeneity of decentralized data and the limited communication resources of clients pose great challenges to the deployment of federated training. Although existing works have made great strides in dealing with heterogeneous data or compressing communication,

The Internet of Things (IoT) has revolutionized various sectors by enabling devices to communicate and interact seamlessly. However, developing IoT applications has data management, security, and interoperability challenges. Large Language Models (LLMs) have shown promise in addressing these challenges due to their advanced language processing capabilities. This Systematic Literature Review assesses

The majority of studies regard virtual machine placement (VMP) as a multi-dimensional bin packing problem. The most common solution is to place as many virtual machines (VMs) on physical machines (PMs) as possible in order to improve the overall resource utilization of the cloud data centers (CDCs). However, it brings some obstacles for the working performance of VMs and the quality of service (QoS)

Conventional deep learning (DL) model compression methods affect all input samples equally. However, as samples vary in difficulty, a dynamic model that adapts computation based on sample complexity offers a novel perspective for compression and scaling. Despite this potential, existing dynamic techniques are typically monolithic and have model-specific implementations, limiting their generalizability

Serverless computing, also known as Functions as a Service (FaaS), is an emerging cloud deployment paradigm that offers advantages such as pay-as-you-go pricing and automatic scaling. Functions often suffer from cold starts delays due to the overhead of initializing code and data dependencies before execution. Retaining containers in memory for a period after execution can reduce cold start latency

The high-performance computing (HPC) landscape is undergoing rapid transformation, with an increasing emphasis on energy-efficient and heterogeneous computing environments. This comprehensive study extends our previous research on SYCL’s performance portability by evaluating its effectiveness across a broader spectrum of computing architectures, including CPUs, GPUs, and hybrid CPU–GPU configurations

The emergence of heterogeneity in high-performance computing, which harnesses under one integrated system several platforms of different architectures, also led to the development of innovative cross-platform programming models. Along with the expectation that these models will yield computationally intensive performance, there is demand for them to provide a reasonable degree of performance portability

Julia is a high-level language that supports the execution of parallel code through various packages. CUDA.jl is widely used for developing GPU-accelerated code in Julia and is integrated into many libraries and programs. In this paper, we present Juliana, a novel tool that automatically translates Julia code utilizing the CUDA.jl package into an abstract, multi-backend representation powered by the

This Special Issue addresses the evolving landscape of big data generated by sensors, devices, and services. The shift from centralized cloud infrastructures to distributed systems that involve cloud, edge, and Internet of Things (IoT) devices requires innovative approaches to managing and analyzing big data. The key challenges include privacy, security, energy efficiency, data quality, and trust.

Edge computing is a promising technology for deploying time-sensitive and privacy-sensitive applications closer to the premises of users. However, it is crucial to identify the sources of performance variability caused by application co-location to meet user requirements effectively. Monitoring systems typically expose hundreds of metrics, making comprehensive analysis challenging. As a result, researchers

This article proposes an object layout regenerator called Regen which regenerates and removes the object layout dynamically to improve the read performance of applications. Regen first detects frequent access patterns from the I/O requests of the applications. Second, Regen reorganizes the objects and regenerates or preallocates new object layouts according to the identified access patterns. Finally

Forecasting the resource consumption of workloads is a frequent approach in the cloud provisioning field. Ideally, such predictions allow obtaining a more accurate scheduling and management of resources in a computing cluster. However, the current approaches fail to properly forecast the future consumption in areas where sudden increases of consumption are present, i.e., spikes. Even, commonly employed

Proactive fault tolerance increasingly serves as an added protection for data in Reed–Solomon (RS) systems. Compared with declustered placement, grouped placement reduces the failure units and also decreases the repair parallelism, which have the opposite effect on systems reliability. For a RS (k, m) system, the values of (k, m) impact storage overhead, fault tolerance and repair traffic. When designing

In federated learning with data heterogeneity, the global model often exhibits a severe imbalance in fitting data from different categories, and clients may not be able to obtain useful information from the impaired global model. To address this challenge, Federated Learning Based on Model Repair (FedMR) is proposed to repair the global model by a set of prototypes with minimal divergence. The repair

Unmanned Aerial Vehicle (UAV) swarms, as efficient and flexible monitoring tools, can collect real-time traffic information over extensive areas. However, UAV swarms engaged in traffic monitoring are vulnerable to network attacks and privacy breaches, leading to data distortion and compromised system performance. To address these security challenges and incentivize UAV participation, we propose CI-AGFL

The aim of this paper is to study the performance portability of OpenMP, OpenACC and SYCL implementations of sparse matrix–vector product (SpMV) and its extended version in which the dot product of the input vector and the result is also calculated, for CSR and BSR storage formats, on Intel and AMD CPUs and NVIDIA GPU platforms. We compare it with the performance portability of much more sophisticated

High-performance computing (HPC) systems often suffer from interference caused by multiple applications accessing a shared parallel file system, which can negatively impact compute performance. One solution to this problem is to add new tiers to the HPC storage hierarchy that can absorb I/O bursts and support moving data between tiers based on its hotness. Ad-hoc file systems serve as an intermediate

Network slicing allows multiplexing virtualized networks, called slices, over a single physical network infrastructure. Research has extensively focused on the placement of virtual functions and the links that compose each network slice. On the other hand, performance greatly depends on how many resources are allocated to virtual nodes and links, after they are placed. This aspect has been mostly neglected

In smart cities, the Cloud of Health Things (CoHT) enhances service delivery and optimizes task scheduling and allocation. As CoHT systems proliferate and offer a range of services with varying Quality of Service (QoS) demands, servers face the challenge of efficiently distributing limited virtual machines across internet-based applications. This can strain performance, particularly for latency-sensitive