Autonomous driving technologies have achieved significant advances in recent years, yet their real-world deployment remains constrained by data scarcity, safety requirements, and the need for generalization across diverse environments. In response, synthetic data and virtual environments have emerged as powerful enablers, offering scalable, controllable, and richly annotated scenarios for training and evaluation. This survey presents a comprehensive review of recent developments at the intersection of autonomous driving, simulation technologies, and synthetic datasets. We organize the landscape across three core dimensions: 1) the use of synthetic data for perception and planning, 2) digital twin-based simulation for system validation, and 3) domain adaptation strategies bridging synthetic and real-world data. We also highlight the role of vision-language models and simulation realism in enhancing scene understanding and generalization. A detailed taxonomy of datasets, tools, and simulation platforms is provided, alongside an analysis of trends in benchmark design. Finally, we discuss critical challenges and open research directions, including Sim2Real transfer, scalable safety validation, cooperative autonomy, and simulation-driven policy learning, that must be addressed to accelerate the path toward safe, generalizable, and globally deployable autonomous driving systems.

Optimized routing algorithms are of most importance for Mobile Ad-hoc NETworks. The routing protocol offers a route to the destination and adapts the path when the network changes. The popular routing algorithm, Optimized Link State Routing (OLSRv2) is evaluated in realistic real time emulations using the open source EMANE platform and the open Anglova scenario. The effect of fading is analyzed to account for the mobile wireless environment. OLSR estimates the quality of each link by processing received HELLO packets. OLSR has limited performance in scenario with fading and can lead to a poor completion ratio: 67% for the 24-node Anglova company 1 scenario. The proposed solution called OLSR+PHY uses the physical layer estimate of the Signal to Interference and Noise Ratio (SINR) to drop the HELLO packets received with poor SINR. OLSR+PHY mitigates routes flapping and data packet loss. The completion ratio of acked-messages is improved to 79% while the Round Trip Time is only increased from 0.6 to 1s.

Affect is often expressed via non-verbal body language such as actions/gestures, which are vital indicators for human behaviors. Recent studies on recognition of fine-grained actions/gestures in monocular images have mainly focused on modeling spatial configuration of body parts representing body pose, human-objects interactions and variations in local appearance. The results show that this is a brittle approach since it relies on accurate body parts/objects detection. In this work, we argue that there exist local discriminative semantic regions, whose “informativeness” can be evaluated by the attention mechanism for inferring fine-grained gestures/actions. To this end, we propose a novel end-to-endregional attention network (RAN), which is a fully convolutional neural network (CNN) to combine multiple contextual regions through attention mechanism, focusing on parts of the images that are most relevant to a given task. Our regions consist of one or more consecutive cells and are adapted from the strategies used in computing HOG (Histogram of Oriented Gradient) descriptor. The model is extensively evaluated on ten datasets belonging to 3 different scenarios: 1) head pose recognition, 2) drivers state recognition, and 3) human action and facial expression recognition. The proposed approach outperforms the state-of-the-art by a considerable margin in different metrics.

Over the past few years, a significant progress has been made in deep convolutional neural networks (CNNs)-based image recognition. This is mainly due to the strong ability of such networks in mining discriminative object pose and parts information from texture and shape. This is often inappropriate for fine-grained visual classification (FGVC) since it exhibits high intra-class and low inter-class variances due to occlusions, deformation, illuminations, etc. Thus, an expressive feature representation describing global structural information is a key to characterize an object/ scene. To this end, we propose a method that effectively captures subtle changes by aggregating context-aware features from most relevant image-regions and their importance in discriminating fine-grained categories avoiding the bounding-box and/or distinguishable part annotations. Our approach is inspired by the recent advancement in self-attention and graph neural networks (GNNs) approaches to include a simple yet effective relation-aware feature transformation and its refinement using a context-aware attention mechanism to boost the discriminability of the transformed feature in an end-to-end learning process. Our model is evaluated on eight benchmark datasets consisting of fine-grained objects and human-object interactions. It outperforms the state-of-the-art approaches by a significant margin in recognition accuracy.

There is significant progress in recognizing traditional human activities from videos focusing on highly distinctive actions involving discriminative body movements, body-object and/or human-human interactions. Driver's activities are different since they are executed by the same subject with similar body parts movements, resulting in subtle changes. To address this, we propose a novel framework by exploiting the spatiotemporal attention to model the subtle changes. Our model is named Coarse Temporal Attention Network (CTA-Net), in which coarse temporal branches are introduced in a trainable glimpse network. The goal is to allow the glimpse to capture high-level temporal relationships, such as `during', `before' and `after' by focusing on a specific part of a video. These branches also respect the topology of the temporal dynamics in the video, ensuring that different branches learn meaningful spatial and temporal changes. The model then uses an innovative attention mechanism to generate high-level action specific contextual information for activity recognition by exploring the hidden states of an LSTM. The attention mechanism helps in learning to decide the importance of each hidden state for the recognition task by weighing them when constructing the representation of the video. Our approach is evaluated on four publicly accessible datasets and significantly outperforms the state-of-the-art by a considerable margin with only RGB video as input.

When IoT meets Industry 4.0, we talk about Industrial Internet of Things (IIoT) or the use of typical Internet of Things (IoT) solutions in the industrial sector, integrating different technologies such as machine learning, big data and analytics, sensor data, Machine to Machine communication (M2M) and automation. Its application within the fourth industrial revolution has initiated a profound change within companies making them increasingly connected and exploiting data to optimize their production processes. IIoT is revolutionizing sectors such as manufacturing, automotive and healthcare. Collecting and analyzing all the data coming from the production sensors and drawing the information for your business through business analytics tools is the new key to competitiveness.

This article presents a novel keypoints-based attention mechanism for visual recognition in still images. Deep Convolutional Neural Networks (CNNs) for recognizing images with distinctive classes have shown great success, but their performance in discriminating fine-grained changes is not at the same level. We address this by proposing an end-to-end CNN model, which learns meaningful features linking fine-grained changes using our novel attention mechanism. It captures the spatial structures in images by identifying semantic regions (SRs) and their spatial distributions, and is proved to be the key to modeling subtle changes in images. We automatically identify these SRs by grouping the detected keypoints in a given image. The "usefulness" of these SRs for image recognition is measured using our innovative attentional mechanism focusing on parts of the image that are most relevant to a given task. This framework applies to traditional and fine-grained image recognition tasks and does not require manually annotated regions (e.g. bounding-box of body parts, objects, etc.) for learning and prediction. Moreover, the proposed keypoints-driven attention mechanism can be easily integrated into the existing CNN models. The framework is evaluated on six diverse benchmark datasets. The model outperforms the state-of-the-art approaches by a considerable margin using Distracted Driver V1 (Acc: 3.39%), Distracted Driver V2 (Acc: 6.58%), Stanford-40 Actions (mAP: 2.15%), People Playing Musical Instruments (mAP: 16.05%), Food-101 (Acc: 6.30%) and Caltech-256 (Acc: 2.59%) datasets.

Introduction to the Special Section on Data Science for Cyber-Physical Systems

Summary Advances in computer networking, storage technologies, and high‐performance computing are helping global communities of researchers to address increasingly ambitious problems in Science collaboratively. E‐Science is the “science of this age”; it is realized through collaborative scientific enquiry which requires the utilization of non‐trivial amounts of computing resources and massive data sets. Core to this is the integrated set of technologies collectively known as e‐Infrastructures. In this paper, we explore the e‐Science and the e‐Infrastructure knowledge base through co‐citation analysis of existing literature. The dataset for this analysis is downloaded from the ISI Web of Science and includes over 12,000 articles. We identify prominent articles, authors, and articles with citation bursts. The detection of research clusters and the underlying seminal papers provide further insights. Our analysis is an important source of reference for academics, researchers, and students starting research in this field.

Today, Internet users expect Web applications to be fast, performant, and always available. With the emergence of Internet of Things (IoT), data collection and the analysis of streams have become more and more challenging. Behind the scenes, application owners and cloud service providers work to meet these expectations, yet, the problem of how to most effectively and efficiently auto-scale a Web application to optimize for performance while reducing costs and energy usage is still a challenge. In particular, this problem has new relevance due to the continued rise of IoT and microservice-based architectures. A key concern, that is often not addressed by current auto-scaling systems, is the decision on which microservice to scale in order to increase performance. Our aim is to design a prototype auto-scaling system for microservice-based Web applications that can learn from the past service experience. The contributions of the work can be divided into two parts: 1) developing a pipeline for microservice auto-scaling and 2) evaluating a hybrid sequence and supervised learning model for recommending scaling actions. The pipeline has proven to be an effective platform for exploring auto-scaling solutions, as we will demonstrate through the evaluation of our proposed hybrid model. The results of the hybrid model show the merit of using a supervised model to identify which microservices should be scaled up more.

The influence of the Internet of Things (IoT) and related produced data is destined to revolutionize economic and social society, even more incisively than the advent of digital. The creation of the IoT world would have been much more complicated if a big data structure had not been followed since the latter allows to analyze vast amounts of data. The IoT is the most significant flow of information collected on the Internet and, therefore, the largest supplier for big data systems, artificial intelligence (AI), and data science. Advanced statistical analysis techniques, neural networks, and AI algorithms, but also the ability to create mathematical models that best represent a physical phenomenon or social behavior, these are the new strategic assets in the digital transformation process that is facing the world of industry and services. In fact, collecting data is not enough: they must be managed, integrated, and compared with a mathematical model that formalizes the intrinsic knowledge in the experience and competence of people.

A forum or social media post can express multiple emotions, such as love, joy or anger. Emotion classification has been proven useful for measuring aspects such as user satisfaction. Despite its usefulness, research in emotion classification is limited, because the task is multi-label and publicly available data sets and lexica are very limited. A number of emotion classifiers for general-domain text have been proposed recently, but only a few for text in the domain of Open Source Software (OSS), such as EmoTxt. In this paper, we explore different lexica and two multi-label algorithms for classifying emotions in text related to OSS. We trained various multi-label classifiers using HOMER and RAkEL on a data set of Stack Overflow posts and a data set of JIRA Issue Tracker comments. The classifiers have been enriched with features derived from different state-of-the-art lexica. We achieved multi-label Micro F-scores up to 0.811 and Subset 0/1 Loss of 0.290. These results represent a statistically significant improvement over the state-of-the-art.

Everyone, knowingly or not, is in a position to generate data at all times in our lives. Mainly in the professional life, where the production of data is addressed and aimed at achieving a series of objectives, and in social and personal life where the production of data, direct and indirect, is equally important although not always clearly identifiable. Always and in any case, the representation of our actions in digital contents or the analysis of our behaviour as consumers, just to give two examples, are a common denominator of our relationship with the digital framework which has a direct impact at the level of relationship with companies or with public administrations. In the business, the most appropriate example today is that of Industry 4.0 which with the virtualization of the processes and products themselves is allowing companies to explore novel paradigms of innovation that were once unthinkable.

In urban environments there are daily issues of traffic congestion which city authorities need to address. Realtime analysis of traffic flow information is crucial for efficiently managing urban traffic. This paper aims to conduct traffic analysis using UAV-based videos and deep learning techniques. The road traffic video is collected by using a position-fixed UAV. The most recent deep learning methods are applied to identify the moving objects in videos. The relevant mobility metrics are calculated to conduct traffic analysis and measure the consequences of traffic congestion. The proposed approach is validated with the manual analysis results and the visualization results. The traffic analysis process is real-time in terms of the pre-trained model used.

Today, cloud computing applications are rapidly constructed by services belonging to different cloud providers and service owners. This work presents the inter-cloud elasticity framework, which focuses on cloud load balancing based on dynamic virtual machine reconfiguration when variations on load or on user requests volume are observed. We design a dynamic reconfiguration system, called inter-cloud load balancer (ICLB), that allows scaling up or down the virtual resources (thus providing automatized elasticity), by eliminating service downtimes and communication failures. It includes an inter-cloud load balancer for distributing incoming user HTTP traffic across multiple instances of inter-cloud applications and services and we perform dynamic reconfiguration of resources according to the real time requirements. The experimental analysis includes different topologies by showing how real-time traffic variation (using real world workloads) affects resource utilization and by achieving better resource usage in inter-cloud.

Author name disambiguation is a very important and complex research topic. During the retrieval and research of literature the quality of the investigation results has been reduced because of the high probability of different authors sharing the same name, which lengthens the whole cycle of the scientific research. Therefore, it is necessary to find a reasonable and efficient method to distinguish the different authors who share the same name. In this paper, an author name disambiguation algorithm based on the fusion of multiple features (NDFMF) is proposed. First we proposed a single feature similarity detection algorithm (SFSD). SFSD is used to compute the degree of similarity between two features of a paper and to assess the threshold value. Then, SFSD is used to realize the preliminary SFSD-based disambiguation algorithm (SFSDD). Furthermore, different features are evaluated according to the disambiguation results of author names and the evaluation metrics, including precision, recall and F-measure with SFSDD. The evaluation parameter of weight (W) is introduced to express each feature's influence in disambiguation. NDFMF can disambiguate author names more efficiently based on the fusion of multiple features. Experiments were implemented to test the performance of NDFMF. Experimental results show that NDFMF was effective in the disambiguation precision, recall and F-measure.

In recent years, different kinds of natural hazards or man-made disasters happened that were diversified and difficult to control with heavy casualties. In this paper, we focus on the rapid and systematic evacuation of large-scale densities of people after disasters to reduce loss in an effective manner. The optimal evacuation planning is a key challenge and becomes a hotspot of research and development. We design our system based on an Internet of Things (IoT) scenario that utilizes a mobile cloud computing platform in order to develop the crowd lives oriented track and help optimization system (CLOTHO). CLOTHO is an evacuation planning system for large-scale densities of people in disasters. It includes the mobile terminal (IoT side) for data collection and the cloud backend system for storage and analytics. We build our solution upon a typical IoT/fog disaster management scenario and we propose an IoT application based on an evacuation planning algorithm that uses the artificial potential field (APF), which is the core of CLOTHO. APF is conceptualized as an IoT service, and can determine the direction of evacuation automatically according to the gradient direction of the potential field, suitable for rapid evacuation of large population. Based on APF, we propose an evacuation planning algorithm names as APF with relationship attraction (APF-RA). APF-RA guides the evacuees with relationship to move to the same shelter as much as possible, to calm evacuees and realize a more humanitarian evacuation. The experimental results show that CLOTHO (using APF and APF-RA) can effectively improve convergence rate, shorten the evacuation route length and evacuation time, and make the remaining capacity of the surrounding shelters well balanced.

Combinatorial optimization problems are typically NP-hard, due to their intrinsic complexity. In this paper, we propose a novel chaotic particle swarm optimization algorithm (CS-PSO), which combines the chaos search method with the particle swarm optimization algorithm (PSO) for solving combinatorial optimization problems. In particular, in the initialization phase, the priori knowledge of the combination optimization problem is used to optimize the initial particles. According to the properties of the combination optimization problem, suitable classification algorithms are implemented to group similar items into categories, thus reducing the number of combinations. This enables a more efficient enumeration of all combination schemes and optimize the overall approach. On the other hand, in the chaos perturbing phase, a brand-new set of rules is presented to perturb the velocities and positions of particles to satisfy the ideal global search capability and adaptability, effectively avoiding the premature convergence problem found frequently in traditional PSO algorithm. In the above two stages, we control the number of selected items in each category to ensure the diversity of the final combination scheme. The fitness function of CS-PSO introduces the concept of the personalized constraints and general constrains to get a personalized interface, which is used to solve a personalized combination optimization problem. As part of our evaluation, we define a personalized dietary recommendation system, called Friend, where CS-PSO is applied to address a healthy diet combination optimization problem. Based on Friend, we implemented a series of experiments to test the performance of CS-PSO. The experimental results show that, compared with the typical HLR-PSO, CS-PSO can recommend dietary schemes more efficiently, while obtaining the global optimum with fewer iterations, and have the better global ergodicity.

Inter-cloud is an approach that facilitates scalable resource provisioning across multiple cloud infrastructures. In this paper, we focus on the performance optimization of Infrastructure as a Service (IaaS) using the meta-scheduling paradigm to achieve an improved job scheduling across multiple clouds. We propose a novel inter-cloud job scheduling framework and implement policies to optimize performance of participating clouds. The framework, named as Inter-Cloud Meta-Scheduling (ICMS), is based on a novel message exchange mechanism to allow optimization of job scheduling metrics. The resulting system offers improved flexibility, robustness and decentralization. We implemented a toolkit named “Simulating the Inter-Cloud” (SimIC) to perform the design and implementation of different inter-cloud entities and policies in the ICMS framework. An experimental analysis is produced for job executions in inter-cloud and a performance is presented for a number of parameters such as job execution, makespan, and turnaround times. The results highlight that the overall performance of individual clouds for selected parameters and configuration is improved when these are brought together under the proposed ICMS framework.

Cloud computing and the future Internet concept highlight new requirements for the software engineering phases including testing and validation of modular web services. A major reason is because cloud applications are developed by services belonging to different providers, thus making software testing a really challenging issue. In this work, we propose a testing methodology that includes two fold testing actions; a unit testing of cloud service APIs following white and black box techniques and an integration testing strategy by identifying services that could interface with each other. In addition, we present the Elvior TestCast T3 (TTCN-3) testing tool for automation of use case testing. We demonstrate the results of the methodology when applied to different cloud services and we present a discussion of our conclusions for a real world use case, in which we applied this methodology.

Human action recognition from video sequences is a challenging topic in computer vision research. In recent years, many studies have explored the use of deep learning representations to consistently improve the analysis accuracy. Meanwhile, designing a fast and reliable framework is becoming increasingly important given the exponential growth of video data collected for many purposes (e.g. public security, entertainment, and early medical diagnosis etc.). In order to design a more efficient automatic human action annotation method, the sequential probability ratio test, one of the classical statistical sampling scheme, is adapted to solve a multi-classes hypothesis test problem in our work. With the proposed algorithm, the computational cost is reduced significantly without sacrificing the performance of the underlying system. The experimental results based on the UCF101 data set demonstrated the efficiency of the framework compared to the fixed sampling scheme.

In latest years, there is a corresponding growth for cloud computing services, especially for those that can be offered as modular components in order to construct Future Internet (FI) applications. In addition, the emergence of inter-clouds as the mean to achieve utilization of various services available from different providers highlights new research directions. Following the business benefits of the cloud services into more advanced and modular systems the requirements for effective accounting and billing with regards to the service usage and the accurate billing of consumers of services becomes challenging. This work focuses on cloud platforms, their platform segregations and services, and presents an accounting and billing service that can be used to rate, charge and bill consumers of within a cloud or an inter-cloud platforms. The solution is developed based on FIWARE platform, which promotes the usage of Cloud Computing in Europe by designing general purpose cloud services namely as Generic Enablers (GEs). Here we design and implement an Accounting and Billing GE that is built upon OpenStack platforms and hosted as an easily to deploy and use cloud service.

Summary In this paper, we introduce a novel method to define semi‐automatically fuzzy partition rules to provide a powerful and accurate insight into cardiac arrhythmia. In particular, we define a text mining approach applied to a large dataset consisting of the freely available scientific papers provided by PubMed. The information extracted is then integrated with expert knowledge, as well as experimental data, to provide a robust, scalable and accurate system, which can successfully address the challenges posed by the management and assessment of big data in the medical sector. The evaluation we carried out shows an accuracy rate of 93% and interpretability of 0.646, which clearly shows that our method provides an excellent balance between accuracy and system transparency. Furthermore, this contributes substantially to the knowledge discovery and offers a powerful tool to facilitate the decision‐making process. Copyright © 2015 John Wiley & Sons, Ltd.

The internet of things: Shaping the new internet space

Networks are widely used to model a variety of complex, often multi-disciplinary, systems in which the relationships between their sub-parts play a significant role. In particular, there is extensive research on the topological properties associated with their structure as this allows the analysis of the overall behaviour of such networks. However, extracting networks from structured and unstructured data sets raises several challenges, including addressing any inconsistency present in the data, as well as the difficulty in investigating their properties especially when the topological structure is not fully determined or not explicitly defined. In this paper, we propose a novel method to address the automated identification, assessment and ranking of the most likely structure associated with networks extracted from a variety of data sets. More specifically, our approach allows to mine data to assess whether their associated networks exhibit properties comparable to well-known structures, namely scale-free, small world and random networks. The main motivation is to provide a toolbox to classify and analyse real-world networks otherwise difficult to fully assess due to their potential lack of structure. This can be used to investigate their dynamical and statistical behaviour which would potentially lead to a better understanding and prediction of the properties of the system (s) they model. Our initial validation shows the potential of our method providing relevant and accurate results.

Ant Colony Optimization (ACO) and other similar nature inspired mechanisms like artificial neural networks, swarm intelligence and evolutionary algorithms are based on naturally existing Complex Adaptive Systems (CAS). Human immune system, sand dune ripples, and ant foraging are some examples of the naturally existing CAS. Participating agents in these systems interact according to simple local rules which result in complex behavior and self-organization at system level. Adhoc grids are dynamic in nature and participating nodes show intermittent and volatile participation. Resource availability fluctuates over time inadhoc grids and results in a new adhoc grid state. These changes require adoption of the adhoc grid to anew state by applying some self organizing mechanism. In this paper, we present nature-inspired (ACO), micro-economic based mechanisms for infrastructure level self-organization in adhoc grids. These mechanisms help in achieving a scalable, dynamic and a self-organizing adhoc grid infrastructure. These mechanisms are evaluated with varying workloads in different network conditions. Study of these mechanisms helped in understanding the effect of ACO based self-organization mechanism on the infrastructural spectrum, ranging from completely centralized to fully decentralized.

The mobile interaction in Cloud systems became a fancy behavior. Data processing on demand or data transfers requests are usually sporadic tasks. In a public environment like a Cloud, events are processed according to specific conditions. Each event has one ore more tasks that will be scheduled and executed in Cloud. This paper addresses the problem of remote scheduling of a periodic and sporadic tasks with deadline constrains in Cloud Environments. Starting from classical addressed scheduling techniques and considering asynchronous mechanism to handle tasks, we analyze the possibility of decoupling event listening from task creation and scheduling, actions that can be put into a peer-peer relation over a network or to client-server in Cloud. We consider multiple independent tasks sources that follow with a specific distribution. We will prove in this paper that for a scheduler in a Cloud these independent sources could be considered as a single one. More, we will prove that the resource allocation process respects the same distribution. We created a simulation experiment in MONARC that highlights the capability of tasks migration in order to respect the deadlines.

'Simulating the Inter-Cloud' (SimIC) is a discrete event simulation toolkit based on the process oriented simulation package of SimJava. The SimIC aims of replicating an inter-cloud facility wherein multiple clouds collaborate with each other for distributing service requests with regards to the desired simulation setup. The package encompasses the fundamental entities of the inter-cloud meta-scheduling algorithm such as users, meta-brokers, local-brokers, datacenters, hosts, hyper visors and virtual machines (VMs). Additionally, resource discovery and scheduling policies together with VMs allocation, re-scheduling and VM migration strategies are included as well. Using the SimIC a modeler can design a fully dynamic inter-cloud setting wherein collaboration is founded on meta-scheduling inspired characteristics of distributed resource managers that exchange user requirements as driven events in real-time simulations. The SimIC aims of achieving interoperability, flexibility and service elasticity while at the same time introducing the notion of heterogeneity of multiple clouds' configurations. In addition it accepts an optimization of a variety of selected performance criteria for a diversity of entities. The crucial factor of dynamics consideration has implemented by allowing reactive orchestration based on current workload of already executed heterogeneous user specifications. These are in the form of text files that the modeler can load in the toolkit and occurs in real-time at different simulation intervals. Finally, a unique request is scheduled for execution to an internal cloud datacenter host VM that is capable of performing the service contract. This is formally designed in Service Level Agreements (SLAs) based upon user profiling.

This work covers the inter-cloud meta-scheduling system that encompasses the essential components of the interoperable cloud setting for wide service dissemination. The study herein illustrates a set of distributed and decentralized operations by highlighting meta-computing characteristics. This is achieved by using meta-brokers that determine a middle-standing component for orchestrating the decision making process in order to select the most appropriate datacenter resource among collaborated clouds. The selection is based on heuristic performance criteria (e.g. the service execution time, latency, energy efficiency etc.). Our solution is more advanced when compared to conventional centralized schemes, as it offers robust real-time scalable, elastic and flexible service scheduling in a fully decentralized and dynamic manner. Similarly, issues related with bottleneck on multiple service requests, heterogeneity, information exposition and consideration of variation of workloads are of prime focus. In view of that, the whole process is based upon random service requests from users that are clients of a sub-cloud of an inter-cloud datacenter and access is done via a meta-broker. The inter-cloud facility distributes the request for service by enclosing each personalized service into a host virtual machine. The study presents a detailed discussion of the algorithmic model for demonstrating the whole service dissemination, allocation, execution and monitoring process along with the preliminary implementation and configuration on a proposed SimIC simulation framework.

Highly dynamic environments like clouds by nature cause a high degree of unpredictability of resource utilization and performance. Failures, latencies and heterogeneity should always be the main concern for affecting the scheduling decisions in distributed infrastructures. As a result, the scheduling efficiency of jobs before their submission is very difficult to be achieved or either forecasted. Even in the cases of the most complex schedulers a comprehensive dynamic view cannot always be predicted. Thus, the rescheduling concept takes advantage of the current scheduling status and performs a dynamic scheduling decision. In this paper we present a discussion of the virtual machine migration strategies that are currently available in distributed systems based on the need of migrating virtualized resources in order to achieve better resource utilization and performance such as improve load balancing, makespan and higher throughput of jobs. We conclude our study with a critical discussion of vital requirements for virtual machine migration.

Over the last decades, the cooperation amongst different resources that belong to various environments has been arisen as one of the most important research topic. This is mainly because of the different requirements, in terms of jobs' preferences that have been posed by different resource providers as the most efficient way to coordinate large scale settings like grids and clouds. However, the commonality of the complexity of the architectures (e.g. in heterogeneity issues) and the targets that each paradigm aims to achieve (e.g. flexibility) remains the same. This is to efficiently orchestrate resources and user demands in a distributed computing fashion by bridging the gap among local and remote participants. At a first glance, this is directly related with the scheduling concept, which is one of the most important issues for designing a cooperative resource management system, especially in large scale settings. In addition, the term meta-computing, hence meta-scheduling, offers additional functionalities in the area of interoperable resource management because of its great proficiency to handle sudden variations and dynamic situations in user demands by bridging the gap among local and remote participants. This work presents a review on scheduling in high performance, grid and cloud computing infrastructures. We conclude by analysing most important characteristics towards inter-cooperated infrastructures.

The ability to seamlessly bridge clouds across organisational and administrative boundaries will play a vital role in establishing the utility of cloud computing for large-scale collaborative processes. Managing human and environmental contexts across geographical, network and administrative boundaries is a process that can benefit from a federation of cloud platforms. In the absence of a mature standard that defines access, control, management and coordination mechanisms between clouds in a federation, we explore these issues through a use-case of managing the dissemination and consumption of contextual information. The use-case is driven by the deployment of a broker-based context provisioning system, for homogenous cloud deployments that reside in different administrative domains. The discussion is driven by the aim to highlight key issues and challenges for enabling cloud federation for large-scale context provisioning, which forms the main contribution of this article.

Monitoring process offer a quantitative and qualitative measurement of performance by collecting information relevant to environment and applications. Monitoring allows the obtaining of valuable parameters about performance, resource usage and availability, the efficiency of scheduling and used algorithms and represents a mechanism for analyzing and adapting an application's behavior, particularly useful for optimization of complex applications. Self-* properties of different applications are the answer to the complexity and large scale of distributed systems. The purpose of this paper is to analyze the requirements and to build such a tool destined for computational grids using the Grid Gain middleware platform (an Enterprise middleware for Grids, dedicated both to researcher environments and to industry). The optimization process is very important for QoS assurance, so multi-criteria approach could be adopted. The self-* behavior consider bio-inspired techniques for optimization (genetic algorithms, immune algorithms, swarm intelligence).

Cloud computing provides an efficient and flexible means for various services to meet the diverse and escalating needs of IT end-users. It offers novel functionalities including the utilization of remote services in addition to the virtualization technology. The latter feature offers an efficient method to harness the cloud power by fragmenting a cloud physical host in small manageable virtual portions. As a norm, the virtualized parts are generated by the cloud provider administrator through the hyper visor software based on a generic need for various services. However, several obstacles arise from this generalized and static approach. In this paper, we study and propose a model for instantiating dynamically virtual machines in relation to the current job characteristics. Following, we simulate a virtualized cloud environment in order to evaluate the model's dynamic-ness by measuring the correlation of virtual machines to hosts for certain job variations. This will allow us to compute the expected average execution time of various virtual machines instantiations per job length.

The MSV project aims to survey current best practice in multiscale visualisation and to construct a software toolkit which will make multiscale techniques readily accessible to biomedical researchers and clinicians. In this paper, current methods for multiscale data visualisation in several domains are reviewed, and a novel classification of multiscale techniques for biomedical applications by function is proposed. The classification will form the basis of a design menu and toolkit for multiscale visualisation.

Much work is underway within the broad next generation technologies community on issues associated with the development of services to foster collaboration via the integration of distributed and heterogeneous data systems and technologies. In previous works, we have discussed how these could help coin and prompt future direction of their usage (integration) in various real-world scenarios such as in disaster management. This paper builds upon on our previous works and addresses the use of learning agents called learning birds in modelling the process of data collection using wireless sensor networks, Specifically, learning birds are some sort of nature-inspired learning agents collaborating to create collective behaviours. As an artificial bird flock, the swarm members collaborate in positioning while moving within a particular environment. In order to improve the diversity of the flock, each individual needs learning the how to position relatively to its neighbours. Q learning is a very famous reinforcement learning algorithm, which offers a very efficient and straightforward learning approach based-on gained experiences. Therefore, a swarm of birds collaborating and learning while exchanging information to position offers a very useful modelling approach to develop ad hoc based mobile data collection tools. To achieve this, we use a disaster management scenario.

We re-examine the concept of peer autonomy from an agent-oriented perspective and identify rational behaviours that can be embedded within complex P2P networks. Emergent behaviour is considered in relation to the environmental conditions presented by P2P networks, and we propose a classification of social characteristics that facilitate the mapping and subsequent exchange of peer experiences between nodes.

Resource provision within critical friend environment take place on a demand fashion and it is based on the aptitude of members to look across multiple locations for resource discovery and allocation. A major concern of such large scale and uncertain topology setting is the capability of members (nodes) to efficiently search and locate neighbouring participants. Here we adopt a Peer to Peer (P2P) approach in which every node in the network acts alike and discovers resources in a distributed coordination. More specifically, we propose the use of Chord algorithms, as a distributed peer to peer lookup protocol, put forward a new solution by assigning keys to different nodes. By specifying the aspect in which keys (data) are assigned to nodes, and how a node can determine the value for a given key, the algorithm locates the node responsible for that key. In the work herein we discuss a notable case, namely how Chord algorithms as a distributed P2P lookup protocol may determine provable solutions to the problem of efficient large scale Grid and Cloud resource localization. More specifically, the aforementioned proposal deals with the load balancing, scalability and availability of Grid and Cloud resources in a decentralized manner.

With the proliferation of international standards for grid-enabled databases, the need for data loading and mapping is identified. It is required in a large integrated environment encompassing heterogeneous databases. This research study proposes an intermediate staging facility in order to upload and integrate data from various small to large size data repositories. The proposed facility has been employed to temporarily store and process the data before it is validated. This research expands the integration notion of a database management system (DBMS) to include the data loading, data matching and transformation processes in traditional and grid distributed environments. The data mapping is employed in the form of value correspondences by using a staging DBMS. A service-based generic strategy grid staging catalogue service (GSCATS) is introduced. It consolidates distinct catalogue schemas of federated databases to access information seamlessly. It involves data mapping and matching of structure objects of federated databases.

Much work has been done to exploit the effectiveness and efficiency of job scheduling upon distributed computational--resources. With regard to existing resource topology and administrative constraints, scheduling approaches are designed for different hierarchic layers, for example, scheduling for job queues of local resource management systems (local scheduling), and scheduling for job queues of high level schedulers (also known as meta-schedulers or grid schedulers). Such scheduling approaches mainly focus on optimizing job queues of the hosting nodes, which are interconnected with computational resources directly or indirectly. In the real world (or in a community-based grid), a grid is comprised of nodes with different computing power and scheduling preferences, which in turn, raise a notable opportunity that is to exploit and optimize the process of job sharing between reachable grid nodes via improving the job allocation and efficiency ratio. In our work, we introduce a novel scheduling protocol which dedicates to disseminate scheduling events happened on each involving node to as many candidate nodes as possible. By means of the proposed protocol, scheduling process of each received job consists of several phases with awareness of grid volatility, and dynamic scheduling and rescheduling is allowed as long as the job execution has not started yet. To this end, a set of concerning algorithms and processing steps are described. A prototype of our scheduling approach is being implemented within the SmartGRID project.

In this paper we are focusing on the minimum requirements to be addressed in order to demonstrate a inter-node communication within a Virtual Organisation (VO) using the method of Self-led Critical Friends (SCF). The method is able to decide paths that a node can choose in order to locate neighbouring nodes by aiming at realizing the overhead of each communication. The weight of each path will be measured by the analysis of prerequisites in order to achieve the interaction between nodes. We define requirements as the least fundamentals that a node needs to achieve in order to determine its accessibility factor. The information gathered from an interaction is then stored in a snapshot, a profile that is made available during the discovery stage.