Ciprian Dobre

MonALISA: An agent based, dynamic service system to monitor, control and optimize distributed systems ☆

The MonALISA (Monitoring Agents in a Large Integrated Services Architecture) framework provides a set of distributed services for monitoring, control, management and global optimization for large scale distributed systems. It is based on an ensemble of autonomous, multi-threaded, agent-based subsystems which are registered as dynamic services. They can be automatically discovered and used by other services or clients. The distributed agents can collaborate and cooperate in performing a wide range of management, control and global optimization tasks using real time monitoring information.

Ad hoc and sensor networks

The articles in this special section focus on ad hoc and sensor networks. Today we are witnessing an interesting paradigm shift from the traditional client-server computing model, and the Internet of Things (IoT) is driving it. The reality is that the cost of Internet connectivity for wearable and mobile technology is decreasing, and our daily routines depend more on the use of devices that come equipped with more sensors and much more interesting capabilities at much lower costs. As a result, sensors, connected machinery, and many other things are all becoming connected devices. New enterprises take opportunity from this by exploiting data being collected from such connected devices, for new services and products designed to react to data in the most personalized ways. There is a growing trend in companies dominating their respective industries without owning estates or tangible assets, all thanks to data.

Social aspects to support opportunistic networks in an academic environment

As wireless and 3G networks become more crowded, users with mobile devices have difficulties in accessing the network. Opportunistic networks, created between mobile phones using local peer-to-peer connections, have the potential to solve such problems by dispersing some of the traffic to neighboring smartphones. Recently various opportunistic routing or dissemination algorithms were proposed and evaluated in different scenarios emulating real-world phenomena as close as possible. In this paper we present an experiment performed at the Politehnica University of Bucharest in which we collected social and mobiltity data to evaluate opportunistic routing and dissemination algorithms. We present an analysis of our findings, highlighting key social and mobility behavior factors that can influence such opportunistic solutions. Most importantly, we show that by adding knowledge such as social links between participants in an opportunistic network routing and dissemination algorithms can be greatly improved.

Parallel Programming Paradigms and Frameworks in Big Data Era

With Cloud Computing emerging as a promising new approach for ad-hoc parallel data processing, major companies have started to integrate frameworks for parallel data processing in their product portfolio, making it easy for customers to access these services and to deploy their programs. We have entered the Era of Big Data. The explosion and profusion of available data in a wide range of application domains rise up new challenges and opportunities in a plethora of disciplines—ranging from science and engineering to biology and business. One major challenge is how to take advantage of the unprecedented scale of data—typically of heterogeneous nature—in order to acquire further insights and knowledge for improving the quality of the offered services. To exploit this new resource, we need to scale up and scale out both our infrastructures and standard techniques. Our society is already data-rich, but the question remains whether or not we have the conceptual tools to handle it. In this paper we discuss and analyze opportunities and challenges for efficient parallel data processing. Big Data is the next frontier for innovation, competition, and productivity, and many solutions continue to appear, partly supported by the considerable enthusiasm around the MapReduce paradigm for large-scale data analysis. We review various parallel and distributed programming paradigms, analyzing how they fit into the Big Data era, and present modern emerging paradigms and frameworks. To better support practitioners interesting in this domain, we end with an analysis of on-going research challenges towards the truly fourth generation data-intensive science.

Big Data and Internet of Things: A Roadmap for Smart Environments

This book presents current progress on challenges related to Big Data management by focusing on the particular challenges associated with context-aware data-intensive applications and services. The book is a state-of-the-art reference discussing progress made, as well as prompting future directions on the theories, practices, standards and strategies that are related to the emerging computational technologies and their association with supporting the Internet of Things advanced functioning for organizational settings including both business and e-science. Apart from inter-operable and inter-cooperative aspects, the book deals with a notable opportunity namely, the current trend in which a collectively shared and generated content is emerged from Internet end-users. Specifically, the book presents advances on managing and exploiting the vast size of data generated from within the smart environment (i.e. smart cities) towards an integrated, collective intelligence approach. The book also presents methods and practices to improve large storage infrastructures in response to increasing demands of the data intensive applications. The book contains 19 self-contained chapters that were very carefully selected based on peer review by at least two expert and independent reviewers and is organized into the three sections reflecting the general themes of interest to the IoT and Big Data communities:Section I: Foundations and Principles Section II: Advanced Models and Architectures Section III: Advanced Applications and Future Trends The book is intended for researchers interested in joining interdisciplinary and transdisciplinary works in the areas of Smart Environments, Internet of Things and various computational technologies for the purpose of an integrated collective computational intelligence approach into the Big Data era.

SPRINT: Social prediction-based opportunistic routing

Opportunistic networks are mobile networks that rely on the store-carry-and-forward paradigm, using contacts between nodes to opportunistically transfer data. For this reason, traditional routing mechanisms are no longer suitable. To increase the probability of successfull message delivery, we propose SPRINT, an opportunistic routing algorithm that introduces an additional routing criterion: online social information about nodes. Furthermore, previous results show that, for particular environments, contacts between devices in opportunistic networks are highly predictable. When users follow rare events-based mobility patterns, we show that human mobility can be approximated as a Poisson distribution. Based on this result, we add an additional prediction component into our routing algorithm. Our solution delivers better results compared to traditional social-based routing approaches, for different real-world and synthetic mobility scenarios.

A Realistic Mobility Model Based on Social Networks for the Simulation of VANETs

The validation of mobile ad hoc technologies relies almost exclusively on modeling and simulation. In this paper we present a novel mobility model based on social network theory. The mobility model is designed to accurately reflect the realistic mobility of the involved actors in various VANET simulation scenarios. This is much needed as, in order to have a high degree of confidence in the validation of various technologies using simulation, the mobility model (as well as the network model) must act very realistic. However, most of the mobility models currently used are very simplistic. The mobility model being presented is part of a VNSim, a generic VANET simulator designed to evaluate a wide range of VANET technologies. We present several results obtained using this mobility model. The results show that the presented mobility model offers a good approximation of real-world movement patterns.

Genetic algorithm for DAG scheduling in Grid environments

Complex applications are describing using work-flows. Execution of these workflows in Grid environments require optimized assignment of tasks on available resources according with different constrains. This paper presents a decentralized scheduling algorithm based on genetic algorithms for the problem of DAG scheduling. The genetic algorithm presents a powerful method for optimization and could consider multiple criteria in optimization process. Also, we describe in this paper the integration platform for the proposed algorithm in Grid systems. We make a comparative evaluation with other existing DAG scheduling solution: Cluster ready Children First, Earliest Time First, Highest Level First with Estimated Times, Improved Critical Path with Descendant Prediction) and Hybrid Remapper. We carry out our experiments using a simulation tool with various scheduling scenarios and with heterogeneous input tasks and computation resources. We present several experimental results that offer a support for near-optimal algorithm selection.

Resource usage prediction algorithms for optimal selection of multimedia content delivery methods

This paper proposes two algorithms adopted in a prototype network architecture, for optimal selection of multimedia content delivery methods, as well as balanced delivery load, by exploiting a novel resource prediction engine. The proposed architecture exploits both algorithms for the prediction of future multimedia services demands, by providing the ability to keep optimal the distribution of the streaming data, among Content Delivery Networks, cloud-based providers and Home Media Gateways. In addition, the prediction of the upcoming fluctuations of the network, provides the ability to the proposed network architecture, achieving optimized Quality of Service (QoS) and Quality of Experience (QoE) for the end users. Both algorithms were evaluated to establish their efficiency, towards effectively predicting future network traffic demands. The experimental results validated their performance and indicated fields for further research and experimentation.

Exploring Predictability in Mobile Interaction

Recent endeavors in mobile computing concentration analyzing the predictability of human behavior by means of mobility models synthesized from real mobile user traces. Currently, the main focus of such studies is physic allocation: discovering travel patterns, estimating real user movements and anticipating the whereabouts and dynamics of individuals. In this paper, we propose to widen the analyzed context as to take into account a more natural activity inhuman behavior, namely interaction. As such, we explore the predictability of user synergy based on tracing data collected from mobile phone users in academic and office environments. We take into account interactions over Bluetooth and over wireless networks and, by measuring the entropy of interacting both with peers and wireless access points, we discover a remarkable invariability in synergic patterns.