Load forecasting is considered to be the core of an efficient predictive energy management for buildings. In this context, the deployment of smart meters and sensors enabled continuous energy usage monitoring in modern buildings. This streaming data led to the development of a new Online Home Energy Management System (OHEMS). The aim of this study is to develop an advanced smart metering infrastructure for online power forecasting, using embedded hardware with low computing power and real time constraints. As a benchmark, we applied both online and offline load forecasting modes to assess three prediction approaches in terms of accuracy and computational time. A single Machine learning algorithm using Long Short-Term Memory (LSTM) and hybrid Machine learning algorithms (CNN-LSTM), and ensembles machine learning approaches including eXtreme Gradient Boosting Machine (XGBoost) and Random Forest (RF). Furthermore, a novel practical stacking method for Short-Term Load Forecasting (STLF) using a stacked generalization ensemble method, which combines XGBoost and RF methods has been proposed. In online mode, the proposed stacking model achieved the best forecasting performance with a sMAPE of 1.15%, followed by RF (1.22%), XGBoost (1.23%), CNN-LSTM (1.97%) and LSTM (2.20%). In offline mode, the CNN-LSTM model outperformed all other methods with a sMAPE of 1.01%, demonstrating the advantage of deep feature extraction and complete data availability in batch forecasting. Performance-based retraining was shown to be more effective than periodic retraining, which might still be useful in fog computing scenarios. In general, offline CNN-LSTM is preferable for scenarios demanding maximum accuracy, while the stacking model is more suitable for scalable, real-time online forecasting in constrained environments.

This special issue presents recent contributions and results in the fields of cloud computing, IoT, and big data technologies.It includes a set of selected articles that have been presented at the 2017 International Conference on Cloud Computing and Applications (CloudTech'17). 1 CloudTech'17 was held in Rabat on October 24-26, 2017, and it is one of the most successful events in the fields of cloud computing and big data technologies.CloudTech addresses topics related to cloud computing and big data technologies, architectures, and applications, including data privacy and security.The set of papers accepted for publication in this special issue are briefly described in the rest of this document and are structured in four main research areas: (i) platforms and architectures, (ii) cloud computing and big data analysis, (iii) virtualization techniques, and (iv) data security and privacy.

Summary Context‐awareness in energy‐efficient buildings has been considered as a crucial fact for developing context‐driven control approaches in which sensing and actuation tasks are performed according to the contextual changes. This could be done by including the presence of occupants, number, actions, and behaviors in up‐to‐date context, taking into account the complex interlinked elements, situations, processes, and their dynamics. However, many studies have shown that occupancy information is a major leading source of uncertainty when developing control approaches. Comprehensive and real‐time fine‐grained occupancy information has to be, therefore, integrated in order to improve the performance of occupancy‐driven control approaches. The work presented in this paper is toward the development of a holistic platform that combines recent IoT and Big Data technologies for real‐time occupancy detection in smart building. The purpose of this work focuses mainly on the presence of occupants by comparing both static and dynamic machine learning techniques. An open‐access occupancy detection dataset was first used to assess the usefulness of the platform and the effectiveness of static machine learning strategies for data processing. This dataset is used for applications that follow the strategy aiming at storing data first and processing it later. However, many smart buildings' applications, such as HVAC and ventilation control, require online data streams processing. Therefore, a distributed real‐time machine learning framework was integrated into the platform and tested to show its effectiveness for this kind of applications. Experiments have been conducted for ventilation systems in energy‐efficient building laboratory (EEBLab) and preliminary results show the effectiveness of this platform in detecting on‐the‐fly presence of occupants, which is required to either make ON or OFF the system and then activate the corresponding embedded control technique (eg, ON/OFF, PID, state‐feedback).

In this paper, an approach for HVAC (Heating, Ventilation and Air-Conditioning systems) control has been designed and evaluated in real sitting scenarios against conventional logical methods (e.g. On/Off, PID, state feedback) in terms of energy consumption and occupants' thermal comfort. The proposed approach uses fuzzy logic as a soft control method in order to maintain the ambient temperature at required set points by controlling the speed of the HVAC evaporator fan. We have first used MATLAB fuzzy toolbox where the control system is designed using Fuzzy logic technique. The system's control is based on two inputs, the set point temperatures and temperature difference, while the control output is the speed's variation of the evaporator fan. Energy consumption was analyzed at various set point temperatures for the considered control methods. Experimental results show that the potential of controlling the evaporator fan speed with a Fuzzy Logic controller allows sustaining an ambient temperature with significant reduction in energy consumption.

Internet of Things (IoT) and Big data technologies combined with context-driven control could play an important role in developing approaches for reducing energy consumption while maintaining occupants' comfort in smart buildings. For instance, Heating, Ventilation and Air Conditioning systems (HVAC) are the most used systems for maintaining the thermal comfort inside buildings, however, they are considered among the main electricity consuming systems. Therefore, adopting new approaches for minimizing energy consumption seems to be the relevant solution. Renewable energy sources, such as geothermal energy is a promising technique that could be integrated together with the HVAC systems in order to save energy. In this paper, we propose a new approach towards an integrated system using Earth to Air Heat exchanger together with active HVAC systems operated and controlled by IoT and Big Data technologies. An experimental setup is discussed to highlight its components and usefulness towards energy saving in smart buildings.

The intermittent nature and overwhelming variability of the produced electricity from renewable energy sources (RES) constitutes the main challenge for their seamless integration into buildings. Recent studies stated that storage devises, despite their actual high cost, could be used to tackle this issue by balancing the intermittent nature of RES production and the unpredictable building's occupancy. In fact, these storage devises could handle this issue, which is very complex without taking into consideration actual occupancy and environmental context. This paper introduces a demand/response control approach by mainly focusing on micro-grid systems, which are composed of PVs, batteries, and buildings equipment (e.g., ventilation system, lighting, BVAC). A prototype was developed and deployed in a real-setting scenario at our EEBLAB. Experimentations have been performed and results show the usefulness of the proposed control approach for micro-grid systems.

Control approaches of HVAC systems in buildings have been proposed in the past years for minimizing energy consumption and maintaining occupants' comfort. However, recent studies have shown that context-driven control approaches using IoT and Big Data technologies could further improve energy consumption of HVAC systems. In this paper, an intelligent control approach using a state-feedback technique is introduced to regulate the HVAC system according to the actual context. The performance of the proposed control was evaluated in a real test site by deploying a control card that links the controller with the HVAC system. A smart application for real feedback control was also developed and deployed to dynamically adapt the controller to context's changes. Experiments results are reported to demonstrate that this approach is effective in term of energy saving while maintaining a comfortable room temperature.

Localization phase for sensor nodes is considered as a critical issue for Static and Mobile Wireless Sensor Networks (SWSNs & MWSNs) for Three-Dimensional (3D) in last decides. Several localization techniques have been introduced to provide position of sensor nodes, but a limited number of these algorithms have been proposed to accurately estimate the location of sensor nodes for mobile wireless sensor networks for three-dimensional. In this paper, we propose localization algorithms for mobile nodes in 3D. The algorithms based on DV-Hop and EDV-Hop techniques. The selected model is Random Way Point Mobility model. Our presented algorithms guarantees that the nodes estimate their own position with a butter localization accuracy in comparison to static scenarios. Moreover, the proposed simulation confirms that the introduced algorithms can significantly improve the localization error and localization coverage of algorithms in Mobile Wireless Sensor Networks for nodes in a Three- Dimensional.

Summary The easy deployment of wireless sensors allows the development of context‐aware applications that could react to the environment changes and users preferences. For example, data extracted from mobile phones, IoT devices, and embedded computers in buses and taxis could be processed in real time to understand city dynamics. However, gathering and real‐time processing of relevant data are still a challenging task. For instance, the high volume of streaming data requires real‐time processing in order to generate immediate mitigation actions. Complex‐event processing (CEP) techniques and predictive analytics have been recently proposed for analyzing streaming data in order to generate fast insights and then take suitable actions according to the situations' changes. These techniques could be also used to predict future situations and react to them properly before happening. The work presented in this paper focuses mainly on the performance evaluation of three CEP engines, CQELS, C‐SPARQL, and ETALIS, that are widely used by researchers for linked stream data processing. Experiments have been conducted using two existing benchmarks, CityBench and SP2Bench. Several performance metrics have been evaluated to assess on their ability to process high streaming data using complex queries. Results are reported to show the efficiency and scalability of these CEP engines for both social‐based data (SP2Bench) and physical‐based data (CityBench). Reported results show that ETALIS outperforms CQELS and C‐SPARQL in terms of throughput and memory utilization. ETALIS was integrated as a use case scenario for occupancy and air quality comfort in energy‐efficient buildings. Results show the usefulness of CEP for real‐time data processing and reasoning.

Cloud computing

Ventilation systems operate at buildings level for enhancing air quality by injecting fresher air from outside into inside buildings. In many situations, occupants have to open doors or windows in order to get fresh air. However, in cold or hot periods, or when there are no windows, ventilation systems automatically act on behalf of occupants by insuring good indoor air quality. More precisely, the ventilation controller performs this task by adjusting fresh air as much as needed based on actual indoor CO2concentration. Several approaches have been implemented and deployed in real-setting scenarios, but most of them are either time-triggered or are based on fixed threshold values. In this paper, we propose to use contextual data, mainly indoor/outdoor CO2concentration, for developing a Wireless Sensor Network (WSN) based platform for monitoring and ventilation control. A prototype was developed and deployed in a laboratory-based test scenario for ventilation control. Experiments have been conducted and obtained results show the efficiency of the proposed context-driven control approach.

Wireless nano sensors networks (WNSN) have emerged recently to allow nanonodes to communicate to each other using nano-communication mechanisms. Nanodevices are characterized by limited resources, and new communication protocols are, therefore, required. For instance, the flooding mechanism has been proposed for broadcasting in electromagnetic-based WNSNs. While the flooding scheme is very simple, the excessive number of rebroadcasts increases the utilization of the resources that affects the performance and increases the energy consumption. In this paper, we adopt the probabilistic-based broadcasting mechanism in the context of WNSNs. Simulations have been conducted using Nanosim and results show that probabilistic-based flooding mechanism reduces redundant retransmissions while maintaining good performance.

During the last decade, cloud computing has gained great attention from academia, industry, and government as a new infrastructure requiring slighter investments in hardware platform, staff training, or licensing new software tools.It is defined in the work of Borko and Armando 1 as a new computing paradigm in which resources are provided as services and considered as an infrastructure characterized by its availability, ease of use, and no installation or configuration required from end users.In other words, cloud computing can be seen as a collection of resources and applications that offer the following services.2,3 SaaS (Software-as-a-Service) that allows end users to run applications from their PCs, laptops, PDAs, smartphones, or tablets.IaaS (Infrastructure-as-a-Service) that allows users to use cloud's resources as a service.PaaS (Platform-as-a-Service) that gives the users a complete development platform including computing resources and operating systems to develop new services and applications.

Recent developments in research and technological studies have shown that High Performance Computing (HPC) will indeed lead to advances in several areas of Science, engineering, and technology, permitting the successful completion of more computationally intensive and data-intensive problems such as those in healthcare, biomedical and biosciences, climate and environmental changes, multimedia processing, design and manufacturing of advanced materials, geology, astronomy, chemistry, physics, and even financial systems. However, further research is required for developing computing infrastructures, models to support newly evolving architectures, programming paradigms, tools to simulate and evaluate new approaches and solutions, and programming languages that are appropriate for the new and emerging domains and applications. The development of the HPC infrastructure has been accelerated by the advances in silicon technology, which permitted the design of complex systems able to incorporate many hardware and software blocks and cores. More precisely, recent rapid advances in technology and design tools enabled engineers to design systems with hundreds of cores, called multi-processor system-on-chip. These systems are composed of several processing elements, that is, dedicated hardware and software components that are interconnected by an on-chip interconnect. According to Moore's law, the number of cores on-chip will double every 18 months; therefore, thousands of cores-on-chip will be integrated in the next 20 years to meet the power and performance requirements of applications. Moreover, current trends on the road to exascale are moving toward the integration of more and more cores into a single chip 1, 2. For example, accelerators and heterogeneous processing offer some opportunities to greatly increase computational performance and to match increasing application requirements 3. Engineering these computing systems is one of the most dynamic fields in modern Science and technology. That said, there will continue to be a growing demand for more powerful HPC in the upcoming years, not just to tackle basic mounting computing needs but also to lay out the foundations for the HPC market that is becoming potentially larger than the desktop/laptop computer market. Furthermore, HPC is turning out to be a major source of hope for future applications development that require greater amounts of computing resources in various modern Science domains such as bioengineering, nanotechnology, and energy where HPC capabilities are mandatory in order to run simulations and perform visualization tasks. At the time of writing this editorial, petaflop computing is well established 4. Several architectures are making major breakthroughs: commodity, accelerators on commodity, and special-purpose cores. All of top 500 systems are based on multicore technologies 5. HPC usage is growing considerably, especially in industry. And significant efforts toward establishing exascale are underway. At the same time, several challenges have been recently identified in order to create large-scale computing systems that meet current and projected application requirements. Most of them are related to system architectures, algorithms, big data processing, and programming models 6. However, energy cost, resilience, Central Processing Unit (CPU) access latency and memory transfers are key challenges to address in the era of exascale. To address these challenges, completely new approaches and technologies and a shift from the current approaches used for application development and execution to adaptive approaches are required. Consequently, further research is required for developing advanced exascale-based computing infrastructures, models, and paradigms to support newly emerging architectures, programming models, tools to simulate and evaluate more elaborate solutions and applications, and programming languages that are appropriate for these new and emerging domains and challenges. This special issue is intended to provide an overview of some key topics and state-of-the-art of recent advances in subjects relevant to High Performance Computing and simulation. The general objectives are to address, explore, and exchange information on the challenges and current state-of-the-art in high-performance and large-scale computing systems, their use in modeling and simulation, their design, performance and use, and their impact in various Science and engineering domains and applications. This special issue contains research papers addressing the state-of-the-art in high-performance and large-scale computing systems. A set of carefully selected works was invited based on the original presentations at the 2013 IEEE International Conference on High Performance Computing and Simulation (HPCS 2013), which was held in Helsinki, Finland, July 01–05, 2013 7. The extended works have been thoroughly reviewed by an international technical reviewing committee, and only thirteen papers covering a wide range of relevant challenges in HPC were selected for this special issue. The manuscripts tackle research on different topics including HPC, distributed, Peer-to-Peer (P2P) systems, data mining, Graphics Processor Unit (GPU), multicore systems as well as real-world simulations related to Computational Fluid Dynamics (CFD), neuroinformatics, bioinformatics and weather forecast performed on large computational infrastructures. The set of accepted papers can be organized under the following key subjects and subsections, and are briefly described in the remaining parts of this Section. Accelerating compute-intensive applications is another recent research in HPC domain. These accelerators are special-purpose processors, which are designed mainly to speed up compute-intensive sections of applications and achieve better performance than CPUs for certain workloads 8. There are two types of accelerators: field-programmable gate arrays (FPGAs) and GPUs. FPGAs are highly customized and designed to be configured, while GPUs provide massive parallel execution resources and high memory bandwidth. GPU is designed to rapidly manipulate and alter memory to accelerate image-processing applications. Generally, GPUs are easier to program and require less hardware resources, while FPGAs provide the best expectation of performance, flexibility, and low overhead. Hardware acceleration using GPUs or FPGAs could potentially improve run times or higher accuracy simulations. Werner et al. 9, in their article Accelerated Join Evaluation in Semantic Web Databases by Using FPGA, provide different FPGA implementations of the join operation in the context of Semantic Web Databases. Authors develop a flexible FPGA-based hardware accelerator to improve the performance of query evaluation in a Semantic Web database. They propose an architecture based on partial reconfiguration to integrate the FPGA in the software system logically and physically optimized Semantic Web Database engines 10 to accelerate the database operations. Thus, hardware architecture considers joining algorithms for query execution implemented on FPGA. Experimental results are compared with a C code software solution for general-purpose CPU and show the efficiency of the hardware-based solution. Another application that could benefit from using accelerators like FPGA and GPU is the Weather Research and Forecasting (WRF) model 11. It is a model designed to serve both atmospheric research and operational forecasting needs. It is a next-generation mesoscale numerical weather prediction system to allow researchers to generate atmospheric simulations based on real data. However, the WRF model requires significant execution time and storage space. The porting to HPC platforms enables the simulations to run faster. GPUs are designed for computationally intensive applications, via a large number of threads on a larger number of processing elements. In their article An Analysis of the Feasibility and Benefits of GPU/Multicore Acceleration of the Weather Research and Forecasting Model, Vanderbauwhede and Takemi 12 show that porting numerical weather prediction model on GPU outperform current multi-core CPUs implementations. First, a simple study is done to evaluate the possible gains of porting a kernel to the GPU. Then, one kernel is selected through profiling and ported to GPU using OpenCL. The performance is then studied both independently and once the kernel integrated into WRF. Porting the code for GPU greatly improves the parallelization, which translates into better scalability for the OpenMP version of the code. HPC infrastructures can also be used for the development, acceleration, and application of bioinformatics applications. Jaziri et al. 13, in their article High Performance Computing of Oligopeptides Complete Backtranslation applied to DNA Microarray Probe Design, tackle the issue of large-scale backtranslation of oligopeptides, a step of generating all possible nucleic acid sequences from a protein sequence, which is needed for the discovery of new organisms. Because back translation is a time-consuming task that can generate very large quantities of data, authors propose an efficient distributed algorithm to compute a complete back translation of several hundreds of oligopeptides for functional Deoxyribose Nucleic Acid microarrays. The proposed algorithm was implemented, and simulations have been conducted on both simulated and real biological datasets. The results reported show a significant computing speedup on different architectures (symmetric multiprocessors, cluster, and grid). Most applications are computationally intensive. Scientists have traditionally attempted to parallelize their algorithms across HPC infrastructures. However, this task requires significant work and effort for learning parallel programming. Developing parallel libraries and high-level and easy to use languages are required to hide parallelization complexity of programs. Another important issue that can influence HPC systems performance is vectorisation. Many scientific codes have vectorisation potential that cannot be exploited due to an algorithm-driven choice of data layouts. In their article Data Layout Inference for Code Vectorisation, Sinkarovs and Scholz 14 propose an interesting approach for automatically generating efficient code for vectorisation by mainly focusing on the evaluation of a family of data layout transformations. The authors demonstrate the effectiveness of their approach by applying it on an N-body simulation. Coullon et al. 15, in their paper Implicit parallelism on 2D meshes using SkelGIS, tackle the issue of overcoming restrictions in parallelization of scientific simulations because of the complexity of functional concepts and specific features. Parallelization of scientific simulations requires a lot of efforts and field-specific knowledge to produce efficient parallel programs. For this reason, the authors introduce SkelGIS as a solution for abstracted and implicit parallelism. They apply SkelGIS to solve heat equations and shallow-water equations and compare both the SkelGIS performance and the SkelGIS programming effort with Message Passing Interface (MPI) solutions counterparts. The DARPA High Productivity Computing Systems program (2002–2011) has defined and released benchmark suits for measuring performance, portability, programmability, robustness, and the productivity in the HPC domain 3, 16. This suite is composed of several performance tests that are required to examine the performance and classify the HPC architectures, languages, and libraries 6: (i) High-Performance Linpack for evaluating the floating point rate of execution for solving a linear system of equations; (ii) Double precision GEneral Matrix Multiply for measuring the floating point rate of execution of double precision real matrix–matrix multiplication; (iii) STREAM for sustainable memory bandwidth evaluation; (iv) PTRANS for testing the total communications capacity of the network; (v) Random Access, which is required for measuring the rate of integer random updates of memory; (vi) Fast Fourier Transform for evaluating the floating point rate of execution of double precision complex one-dimensional Discrete Fourier Transform; and (vii) b-eff for measuring latency and bandwidth of a number of simultaneous communication patterns. Heinecke et al. 17, in their article Data Mining on Vast Datasets as a Cluster System Benchmark, introduce recent situation about benchmark and procurement of supercomputers, and describe the trend from Linpack benchmark to miniapp benchmarks to determine the performance in real usage. Also, it describes the difficulty to optimize the benchmark for new architectures. They discuss a data mining application that is compared on different (accelerated) cluster architectures and the needed optimization to efficiently run on different platforms. The authors in their work demonstrate such an optimization for a data mining algorithm, which solves regression and classification problems on vast datasets. In other terms, the authors propose a data mining application for cluster system benchmark using overlapping of computation and communication to hide latency and overhead reduction. Experiments have been conducted with different datasets and on the SuperMUC machine at the Leibniz-Rechenzentrum, the local CoolMAX AMD GPU cluster in Munich, the Phi-accelerated Beacon at University of Tennessee, and the Todi Cray XK7 at the Swiss National Supercomputing Centre. The performance results show that for strong scaling settings, GPUs and coprocessors suffer from lack of parallelism and do not perform as well at large scale. For weak scaling settings however, they always outperform. Several challenges, as stated previously, have been identified in order to create large-scale computing systems that meet current application requirements. These computing systems may rely on distributed computing mechanisms, implemented often as clusters and clouds, to provide continuous access to a variety of resources, for example, processing cores, large data stores, and information repositories. For example, a computational grid is a distributed computing infrastructure that can provide globally available network resources. These environments have the potential and ability to integrate large-scale computing resources, on demand. User ability to compute will no longer be limited to the resources he has at hand currently or those localized statically on a set of hosts known a priori. However, the main challenge in large-scale computing is how to program and control these distributed systems (Clouds or Exascale) with a billion nodes. Evidently, algorithms and simulators have to be developed to explore these new infrastructures. Distributed peer-to-peer control mechanisms could be devised and used to simulate new algorithms and computing architectures. In their article Flexible Replica Placement for Optimized P2P Backup on Heterogeneous, Unreliable Machines, Skowron and Rzadca 18 tackle the issue of data replication over distributed P2P systems with unreliable machines. They introduce a P2P backup system architecture using an optimal replication strategy for storing data over distributed P2P systems. Furthermore, research works to date have concentrated on static approaches tailored to parallelize existing applications on different HPC systems. However, the rapid growth in the size and complexity of contemporary distributed parallel applications, usually assembled out of a set of interacting software components executed over distributed and heterogeneous platforms, makes such approaches unsuitable for these dynamic environments. Therefore, dynamic approaches are required to allow the system to autonomously adapt its structure and its behavior during the course of its operation. In other words, these approaches allow the system to automatically modify its configuration according to the settings of its computing environment and the properties of its workload. These approaches are mainly motivated by the following issues. The high number of nodes makes the system vulnerable to failures. Consequently, its ability to react autonomously to faults is challenging. For example, the system's nodes should react to the changing environment by taking over pending tasks from faulty nodes. Static and centralized configurations of these systems are difficult or even impossible to use for dynamic and large-scale applications. For instance, for a large system with thousands of nodes, several applications could compete for resources. However, managing the resources at runtime and in a decentralized manner is a challenging task. Mencagli 19 in his article, Adaptive Model Predictive Control of Autonomic Distributed Parallel Computations with Variable Horizons and Switching Costs, addresses the dynamic reconfiguration issue of parallel computations by proposing an automatic method for reconfiguring distributed parallel computing. An autonomic computing approach is applied. It monitors the behavior of parallel modules and adjusts the degree of parallelism in order to achieve a global optimization while balancing the number of reconfigurations in view of performance and efficiency. The approach is based on model predictive control. The paper investigates especially the influence of different horizon lengths and different models for switching costs. The proposed method was evaluated by using a video-streaming application with synthesized workload. A model predictive control-based policy is evaluated with fixed horizon and variable horizon, respectively, and results are reported to show solution's effectiveness in improving the target properties of the adaptation process. Such autonomic control models to automate reconfiguration management based on current system load and application status could be employed in cloud computing platforms in which decision-making strategies are required for the purpose of resource management. Several compute-intensive and emerging applications have also been the subject of extensive research. These applications range from life sciences (e.g., medical imaging and gene sequencing), financial trading, oil and gas exploration, to bioscience, combustion (e.g., complex fluid simulation), astrophysics (e.g., formation of stars, evolution of galaxies), and environment (e.g., modeling world climate). The special issue includes a few such application areas with interesting representative works. Future human brain neuroimaging requires the integration of HPC to achieve high-temporal and high-spatial resolutions. Salman et al. 20, in their article Concurrency in Electrical Neuroinformatics: Parallel Computation for Studying the Volume Conduction of Brain Electrical Fields in Human Head Tissues, highlight the necessity of integrating HPC tools and techniques in order to have a systematic methodology for analyzing the main factors and study the interdependent parameters that affect the accuracy of solutions especially for large, multi-dimensional images. Their paper discusses challenges in human brain neuroimaging, particularly how to achieve high-temporal and high-spatial resolution. They provide two accurate, efficient, and reliable finite difference method-based forward solvers that are parallelized using OpenMP in shared memory and CUDA on GPU in order to show that advances in neuroimaging science and engineering will depend significantly on HPC integration. In their article A Novel Technique for Detecting Suspicious Lesions in Breast Ultrasound Images, Karimi and Krzyzak 21 address an important practical problem of automatic classification of breast lesion images using ultrasound. The problem of automatic classification of suspicious masses in ultrasound images has fundamental importance in oncology. The main advantage of ultrasound is that it is a noninvasive diagnostic tool, its main disadvantage being the heavy presence of acoustic noise. Any progress in automatic breast cancer classification using ultrasound may have significant impact on early detection and treatment of breast cancer. The authors tackle this issue by introducing a novel automatic classification technique of suspicious breast lesions using ultrasound images. The system proposed in the paper is a pipeline, which consists of several functional components. The first component uses the fuzzy logic approach, texture and morphology for de-noising, and segmentation of suspicious lesions. The second component deals with feature extraction and selection. The authors considered geometrical, texture, and morphological features. After applying sequential forward and backward searches, they selected the best features and passed them to the third component, which implements support vector machine classifier categorizing suspicious lesions into benign and malignant classes. The system is validated by a computer experiment on 80 real images. According to the authors, its performance reached a 98% success rate. It was then compared with two other methods, which were significantly outperformed by the proposed system. Pattern recognition is another research field, which focuses on the recognition of patterns and regularities in data. Most approaches used in pattern recognition employ classification methods. Support vector machines (SVM) are considered the most widely used classification technique in the pattern recognition community. It is a supervised learning model with associated learning algorithms that are used for classification and regression analysis needed for recognizing patterns and data analysis. In other words, SVM is mainly a classifier method that performs classification tasks by constructing hyperplanes in a multidimensional space. et al. in their article Support for Pattern propose to improve the SVM classification techniques by using SVM They examine the performance on pattern The authors a on the source with the kernel and its effectiveness on datasets from Deoxyribose Nucleic Experimental results conducted in this paper show that the proposed SVM is in practical pattern recognition applications. et al. in their paper of the Support with the parallelization on multi-core of SVM supervised learning They propose a new SVM algorithm with a optimization algorithm for automatic The authors have two parallelization approaches on GPU using and The results reported demonstrate an important for the proposed approach compared with the CPU and are of compute-intensive applications for complex fluid that have from researchers in computational are for the large of algorithms used to simulate different types of (e.g., and that require and memory requirements. Therefore, them on recent platforms and for different application has been in the 10 In their article and Analysis of et al. the behavior of solvers on modern HPC systems. They first models for both performance and energy In the article, authors also the performance on Message Passing that their models are tools to optimal for large-scale simulations. They highlight the importance of performance and the choice of number of cores used chip to energy The in this special issue provide recent advances in some fields related to High Performance Computing and simulations. In the manuscripts research on different topics including HPC, systems, data mining, systems as well as real-world simulations related to neuroinformatics, and weather forecasting performed on large computational infrastructures. hope that the can benefit from the in this special issue and will to these and research The of this special issue to their to all of the authors their papers to this special issue. are also to the for the work and the to the of this special also to to the for the to this special his during the special issue and for the authors the to their work in the international of Concurrency and and to the for their and the following

The development of ubiquitous and pervasive computing systems requires new approaches and paradigms. Mobile agent based approaches have received a great attention for developing distributed applications. Agents are programs that can migrate from a machine to another in a network and perform tasks on distant machines. However, it is difficult to estimate a priori the appropriate number of agents allowed to be spawned in the network without any global information or controller. Indeed, increasing agent population size, with cloning operation, will increase resource demands in the network, which would indirectly affect the network performance. This paper focuses on the problem of dynamic regulation of mobile agent population size in a distributed system and proposes an approach that takes inspiration from the immune system concepts. Simulations have been conducted and results are reported to show the effectiveness of the proposed approach.

Broadcasting is a fundamental operation in a mobile ad hoc network (MANETs) protocols. The flooding is the simplest mechanism used for message broadcasting in MANETs in which a message is rebroadcasted, by each node, exactly once. Despite its simplicity, it can cause high redundancy retransmission that could generate messages contention and collision. However, the issue is finding a way to minimize the number of redundantly received messages in order to save transmission energy while maintaining good latency and high reachability. Several enhanced versions of the flooding mechanism have been proposed to alleviate this issue by inhibiting some nodes from rebroadcasting. The selection of relay nodes is a major design consideration in all enhanced versions. In this paper, a dynamic threshold-based probabilistic scheme (DTP) is introduced. In DTP, the threshold value at the nodes is dynamically adjusted using only information from neighboring nodes. Results are presented and show the efficiency of DTP in terms of reachability, saved rebroadcast, and average latency.

Ubiquitous and pervasive applications may present critical requirements from the point of view of functional correctness, reliability, availability, security, and safety. Unlike traditional safety-critical applications, the behavior of ubiquitous and pervasive applications is affected by the movements and location of users and resources. In this article, we first present emerging formal methods for the description of both entities and their behavior in pervasive computing environments; then, we introduce this special issue. Despite many previous works that have focused on modeling the entities, relatively few have concentrated on modeling or verifying behaviors; and almost none has dealt with combining techniques proposed in these two aspects. The articles accepted in this special issue cover some of the topics aforementioned and constitute a representative sample of the latest development of formal methods in pervasive computing environments.

High-performance reconfigurable computing involves acceleration of significant portions of an application using reconfigurable hardware. Mapping application task graphs onto reconfigurable hardware is, therefore, of rising attention. In this work, we approach the mapping problem by incorporating multiple architectural variants for each hardware task; the variants reflect tradeoffs between the logic resources consumed and the task execution throughput. We propose a mapping approach based on the genetic algorithm, and show its effectiveness for random task graphs as well as an N-body simulation application, demonstrating improvements of up to 78.6 percent in the execution time compared with choosing a fixed implementation variant for all tasks. We then validate our methodology through experiments on real hardware, an SRC-6 reconfigurable computer.

Rapid advances in technology and design tools enabled today engineers to design system-on-chip containing large number of cores. These systems have limited resources and should be implemented with very little silicon area overhead. Several studies have demonstrated that buffers inside switches of the on-chip interconnect take a significant portion of the system silicon area that can affects the performance and the energy consumption. Therefore, their size should be carefully customized to match communication patterns of a target application. In this paper, a compartmental Fluid-flow based modeling approach is presented to allocate required resource for each buffer based on the application traffic pattern. Simulations are conducted and preliminary results are reported to show the efficiency of the Fluid-flow based modeling method for a buffer space allocation.

Ubiquitous and Pervasive Computing (UPC) are recent paradigms with a goal of providing computing and communication services anytime and everywhere. In UPC, automatic service composition requires dealing with four major research issues: service matching and selection, coordination and management, scalability and fault tolerance, and adaptiveness to users’ contexts and network conditions. The articles in this special issue cover some of these topics and constitute a representative sample of the latest developments in adaptive service discovery and composition in UPC.

Inter-Vehicles Networks (IVNs) have emerged as a new environment for intelligent transportation applications. The implementation of these applications requires dealing with many issues. The design and the implementation of an efficient and scalable architecture for information dissemination constitute one major issue. In this paper, a decentralized architecture inspired by stigmergy and direct communication between Ants is presented. The Ants colony has a set of organizing principles, such as scalability and adaptability that are useful for developing a decentralized architecture in highly dynamic networks. Simulations are conducted using Starlogo simulator and some metrics such as information relevance and drivers' awareness are evaluated. Preliminary results are reported to first show the effectiveness of the proposed information dissemination strategy.

Network-on-Chip (NoC) has been proposed as an alternative to bus-based schemes to achieve high performance and scalability in System-on-Chip (SoC) design. Performance evaluation of On-Chip Interconnect (OCI) architectures is widely based on simulation which becomes computationally expensive, especially for large-scale NoCs. In this paper, a performance analysis model using Network Calculus is presented to characterize and evaluate the performance of NoC-based applications. The 2D Mesh on-chip interconnect is analyzed and main performance metrics such as end-to-end delay and buffer size requirements are computed and compared against the results produced by a discrete event simulator. The results shed more light on the potential of this analytical technique as a useful tool for NoC design and performance analysis.

Mobile agent is a program that can migrate from a machine to another in a network and perform tasks on machines that provide agent hosting capability. The agent can clone itself in order to increase system robustness and efficiency. The clone operation creates multiple instances of an agent to run on different machines. However, increasing agent population size, with cloning operation, will increase resource demands in the network, which would indirectly affect network performance. When, the mobile agents operate in a dynamic and distributed environment, it is difficult to estimate a priori the appropriate number of agents allowed to be spawned in the network. This paper focuses on the problem of dynamic regulation of mobile agent population size in a distributed system, and proposes an approach that takes inspiration from the immune system concept

Very large scale networks such as the Internet require a new operational model to use resources efficiently and reduce the need for the administration necessary in client-server networks. In this paper, we present an autonomous decentralised system based on mobile agent paradigm and inspired by the immune system as an alternative to the traditional client-server paradigm. The immune system has a useful set of organising principles that guide the design of scale, adapt and efficient enough networking model to bring answers to some large scale networking challenges. This research is the part of an effort to develop a mobile agent-based middleware that can monitor the resources distributed over a large scale networks.