Load balancing is critical for maintaining computing systems’ stability and achieving optimal performance. Its significance is widely recognized across different computing fields, particularly in the context of heterogeneous systems. These systems comprise computing devices with varying computational capabilities and architectures, each optimized for specific workloads. This heterogeneity introduces dynamic resource constraints, architectural mismatches, and unpredictable task-device affinity, which aggravates the challenges of load balancing. This paper presents an AI-driven load balancing solution for real-time distributed heterogeneous systems. Our approach continuously monitors the system state, capturing key factors that influence performance, such as task and device characteristics. Leveraging AI-based models, it computes a dynamic load index for each device based on the collected data. Using these load estimations, the method predicts potential imbalances through a novel imbalance metric and proactively schedules incoming applications to the most suitable devices, ensuring system-wide balance. To validate our approach, we first evaluated the prediction models by comparing a variety of machine learning algorithms with device-specific deep learning models, with the latter achieving superior accuracy. We then compared our method against widely used scheduling techniques across diverse workloads. The results show that our approach achieves more balanced workload distribution, faster execution, higher throughput, improved resource utilization, and reduced energy consumption across all scenarios, showcasing its adaptability to dynamic conditions and its applicability in real-world settings.

Summary Data aggregation, in its basic form, has been widely used, and several solutions have been proposed for IoT environments. However, to calculate statistical metrics, detect anomalies, and predict future trends, we need to perform various data analysis functions on the aggregated data. Recently, multifunctional data aggregation (MFDA) has been proposed to calculate various statistical functions such as sum, mean, variance, covariance, and analyze of variance (ANOVA). The purpose of MFDA is to enable the improvement of decision making, resource allocation and system performance by providing diverse and varied statistical data. However, the existing solutions involving MFDA generate significant communication and calculation costs. Furthermore, they cannot prevent malicious aggregators from sending fake data. Recently, the Fog computing paradigm has been adopted in IoT environments to address various challenges and enhance the efficiency of data processing and storage. The blockchain technology has been integrated in various IoT applications to enhance the security, increase transparency, and facilitate decentralized data exchange and transactions. In this article, we propose BMDA, a blockchain‐based secure multifunctional data aggregation method for IoT‐Fog environments. BMDA employs an encoding function to structure the data before their transmission. Furthermore, to ensure privacy preservation, authentication, data integrity and to resist malicious aggregators, we employ Paillier homomorphic encryption, BLS signature, and blockchain technology. The security analysis demonstrates the robustness of our proposal, and the performance analysis in terms of computations and communications shows the effectiveness of BMDA compared to existing solutions.

Summary Heterogeneous systems deliver high computing performance when effectively utilized. It is crucial to execute each application on the most suitable device while maintaining system balance. However, achieving equal distribution of the computing load is challenging due to variations in computing power and device architectures within the system. Moreover, scheduling applications at real‐time further complicates this task, as prior information about the submitted applications is absent. In this context, we introduce “Equalizer,” a real‐time load balancer for heterogeneous systems. “Equalizer” leverages machine learning to continuously monitor the system's state, predicting optimal devices for application execution at runtime. It assigns applications to devices that minimize system imbalance. To quantify system imbalance, we propose a novel metric that reflects the disparity in computing loads across the system's devices. This metric is calculated using predicted execution times of applications. To validate the performance of “Equalizer,” we conducted a comparative study against widely adopted approaches, namely Round Robin and Device Suitability. The experiments were performed on a heterogeneous cluster comprising a master host and three slave servers, equipped with a total of 4 central processing units (CPUs) and 4 graphics processing units (GPUs). All approaches were deployed on the cluster and evaluated using three distinct workloads categorized by their computing intensity: medium intensity, heavy intensity, and a combination of heavy and medium intensity, simulating real‐world scenarios. Each workload consisted of a set of 80 OpenCL applications with varying input data sizes. The experimental results demonstrate that “Equalizer” effectively minimized the system's imbalance, reduced the idle time of devices, and eliminated overloads. Moreover, “Equalizer” exhibited significant improvements in workload execution time, resource utilization, throughput, and energy consumption. Across all tested scenarios, “Equalizer” consistently outperformed alternative approaches, showcasing its robustness, adaptability to dynamic environments, and applicability in real‐world practice.

The estimation of energy consumption has become vital in developing eco-friendly and sustainable video streaming solutions to monitor CO2 emissions. In this paper, we seek to evaluate and compare the energy consumption and CO2 emissions of the decoding process related to three popular video coding standards, namely AVC, HEVC, VVC, along with two video formats VP9, and AV1 through their real-time software decoders, including h264, hevc, VVdeC/OpenVVC, vp9, and libdav1d. The evaluation is conducted on two types of consumer hardware, desktop PC and laptop. To ensure a fair evaluation, we also assess the coding efficiency of software encoder implementations using three objective quality metrics. The experimental results revealed that the h264 decoder consumes the lowest energy and is associated with the lowest CO2 emissions compared to other decoders on both hardware platforms. On the other hand, the VVenC encoder enhances coding efficiency at the cost of increased decoding energy consumption and CO2 emissions, particularly noticeable in the case of the OpenVVC decoder. Meanwhile, x265/hevc achieves a compelling balance between coding efficiency and decoding energy consumption. The full results of this work are available at https://decodingenergy.github.io/decoding_energy_co2.html.

Summary Heterogeneous systems composed of multiple CPUs and GPUs are progressively attractive as platforms for high performance computing because of their higher performance. Especially with the use of containers which are rapidly replacing virtual machines as the compute instance of choice in cloud‐based deployments such in Kubernetes clusters. The task scheduling in a heterogeneous environment became one of the most important issues considered by the platform providers. The ability to choose the appropriate device, CPU or GPU, has a direct impact on the performance of a particular system. It reduces total processing time and increases customer satisfaction. In heterogeneous systems, optimizing resource consumption is a critical aspect for cloud service providers. Adequate scheduling of an application implies optimization of its execution time, which results in resource consumption for the service provider. The development of algorithms for scheduling applications in heterogeneous computing systems has received a significant amount of attention in recent years. A variety of efforts are dedicated to the design of such scheduling algorithms. This article is one of those efforts. We present in this work, KubeSC‐RTP, a scheduler for Kubernetes environment using machine learning based on runtime prediction of the applications in order to better select the appropriate device, CPU or GPU.

Summary The last few years have been marked by the presence of very large sets of images and videos in our everyday lives. These multimedia objects have a very fast frequency of creation and sharing since images and videos can come from different devices such as smartphones, satellites, cameras, or drones. They are generally used to illustrate objects in different situations (public areas, train stations, hospitals, political and sport events and competitions, etc). As consequence, image and video processing algorithms have got increasing importance for several computer vision applications that should be adapted for managing large‐scale volumes and exploiting high performance computing resources (local or cloud). In this work, we propose a cloud‐based toolbox (platform) for computer vision applications. This platform integrates a toolbox of image and video processing algorithms that can (i) exploit high performance computing cloud resources, (ii) execute applications in real time, and (iii) manage large‐scale database using Big Data technologies. The related libraries and hardware drivers are automatically integrated and configured in order to offer to users an access to the different applications without the need to download, install, and configure software or hardware. Experiments were conducted using three kinds of applications: (i) image and video processing applications, (ii) deep learning techniques for images classification and multiobject localization, and (iii) images indexation and retrieval. These experiments demonstrated the interest of our platform for sharing, in an efficient way, our scientific contributions and annotated databases in order to improve the quality and performance of computer vision applications.

Nowadays, many NoSQL systems are developed to deal with data elasticity in distributed environments. This is very useful for Data mining such as association rules technique which generates a huge number of rules. To avoid any manual post-processing for selecting the interesting rules, many researchers suggest integrating expert users’ knowledge by using ontology and rule patterns. Nevertheless, with NoSQL Big Data that contain very large data, the number of generated rules is so huge that any post-processing becomes complicated especially in industrial areas. Also, any solution and results have to be tested and checked with a real Big Data context. In order to deal with this issue, we use an adjusted approach with ontology and rule patterns to reduce database NoSQL context before generating any rule. After that, we conduct a real experiment on distributed industrial MongoDB database to calculate execution time and generated rules. This work proves the gain in performance for using association rules with ontology in the NoSQL systems.

Summary Nowadays, image and video processing applications have become widely used in many domains related to computer vision. Indeed, they can come from cameras, smartphones, social networks, or from medical devices. Generally, these images and videos are used for illustrating people or objects (cars, trains, planes, etc) in many situations such as airports, train stations, public areas, sport events, and hospitals. Thus, image and video processing algorithms have got increasing importance, they are required from various computer vision applications such as motion tracking, real time event detection, database (images and videos) indexation, and medical computer‐aided diagnosis methods. The main inconvenient of image and video processing applications is the high intensity of computation and the complex configuration and installation of the related materials and libraries. In this paper, we propose a new framework that allows users to select in a smart and efficient way the computing units (CPU or/and GPU) in a cloud‐based platform, in case of processing one image (or one video in real time) or many images (or videos). This framework enables to affect the local or remote computing units for calculation after analyzing the type of media and the algorithm complexity. The framework disposes of a set of selected CPU and GPU‐based computer vision methods, such as image denoising, histogram computation, features descriptors (SIFT, SURF), points of interest extraction, edges detection, silhouette extraction, and sparse and dense optical flow estimation. These primitive functions are exploited in various applications such as medical image segmentation, videos indexation, real time motion analysis, and left ventricle segmentation and tracking from 2D echocardiography. Experimental results showed a global speedup ranging from 5× to 273×(compared to CPU versions) as result of the application of our framework for the above‐mentioned methods. In addition to these performances, the parallel and heterogeneous implementations offered lower power consumption as result of the fast treatment.

Summary Cloud Computing is a type of distributed system that is usually based on the services offered to the user based on SLA contract. In this case, the implementation of a fault‐tolerant system that ensures the reliability and the services continuity becomes a major requirement. In this paper, we propose a fault tolerance strategy based on checkpointing and replication. Our approach uses a smart checkpoint infrastructure for cloud computing tasks. The checkpoints are stored in alternative already paid VMs. This allows resuming a task execution faster and cheaper after a node crash. Since checkpoints are distributed and replicated, our approach increases also the system reliability. The experimental results show the effectiveness of the proposed strategy in term of energy consumption, SLA (System Level Aggregation) violation, and reliability.

Nowadays, data grids are can be seen as a frameworks responding to the needs large scale applications by affording varied geographically distributed resources set. The main aim here is to ensure a robust and efficient access and quality data, to improve the availability, we must improve application costs and to tolerate the faults. In such systems, these advantages are not yielded by means others than replication mechanisms. The effective use the replication technique involves several problems, in relation with the problem of the coherence maintenance of replicas. Our contribution consists new approach for the consistency management in the data grid. The proposed approach combines between pessimistic and optimistic approaches, taking into account benefits of both approaches, to find a compromise between performance and quality. In addition, our approach has been extended by a mechanism placement of replicas based on models of market economies.