Valentin Cristea

Adaptive Traffic Lights Using Car-to-Car Communication

Traffic coordination in intersections is a very studied and challenging topic. This paper presents an adaptive traffic light system based on wireless communication between vehicles and fixed controller nodes deployed in intersections. We present the integrated simulation environment we have developed in order to study the system. We argue that our system can significantly improve traffic fluency in intersections, and has clear advantages over other architectures regarding both cost and performance.

Resource-aware hybrid scheduling algorithm in heterogeneous distributed computing

Today, almost everyone is connected to the Internet and uses different Cloud solutions to store, deliver and process data. Cloud computing assembles large networks of virtualized services such as hardware and software resources. The new era in which ICT penetrated almost all domains (healthcare, aged-care, social assistance, surveillance, education, etc.) creates the need of new multimedia content-driven applications. These applications generate huge amount of data, require gathering, processing and then aggregation in a fault-tolerant, reliable and secure heterogeneous distributed system created by a mixture of Cloud systems (public/private), mobile devices networks, desktop-based clusters, etc. In this context dynamic resource provisioning for Big Data application scheduling became a challenge in modern systems. We proposed a resource-aware hybrid scheduling algorithm for different types of application: batch jobs and workflows. The proposed algorithm considers hierarchical clustering of the available resources into groups in the allocation phase. Task execution is performed in two phases: in the first, tasks are assigned to groups of resources and in the second phase, a classical scheduling algorithm is used for each group of resources. The proposed algorithm is suitable for Heterogeneous Distributed Computing, especially for modern High-Performance Computing (HPC) systems in which applications are modeled with various requirements (both IO and computational intensive), with accent on data from multimedia applications. We evaluate their performance in a realistic setting of CloudSim tool with respect to load-balancing, cost savings, dependency assurance for workflows and computational efficiency, and investigate the computing methods of these performance metrics at runtime. We proposed a hybrid approach for tasks scheduling in Heterogeneous Distributed Computing.The proposed algorithm considers hierarchical clustering of the available resources into groups.We considered different scheduling strategies for independent tasks and scheduling for DAG scheduling.We analyze the performance of our proposed algorithm through simulation by using and extending CloudSim.

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.

Satellite Image Processing Applications in MedioGRID

This paper presents a high level architectural specification of MedioGRID, a research project aiming at implementing a real-time satellite image processing system for extracting relevant environmental and meteorological parameters on a grid system. The presentation focuses on the key architectural decisions of the GRID-aware satellite image processing system, highlighting the technologies for each of the major components. An essential part of managing a global data grid is a monitoring system that is able to monitor and track all the site facilities, networks, and tasks in progress, all in real time. Considering this issue the paper analyzes the possible grid monitoring approaches, proposes a solution and presents a set of monitoring results for the MedioGRID data management subsystem

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.

Meta-scheduling issues in interoperable HPCs, grids and clouds

Over the last years, interoperability among resources has been emerged as one of the most challenging research topics. However, the commonality of the complexity of the architectures (e.g., heterogeneity) and the targets that each computational paradigm including HPC, grids and clouds aims to achieve (e.g., flexibility) remain the same. This is to efficiently orchestrate resources in a distributed computing fashion by bridging the gap among local and remote participants. Initially, this is closely 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 such as in grids and clouds. Within this context, meta-scheduling offers additional functionalities in the area of interoperable resource management, this is because of its great agility to handle sudden variations and dynamic situations in user demands. Accordingly, the case of inter-infrastructures, including InterCloud, entitle that the decentralised meta-scheduling scheme overcome issues like consolidated administration management, bottleneck and local information exposition. In this work, we detail the fundamental issues for developing an effective interoperable meta-scheduler for e-infrastructures in general and InterCloud in particular. Finally, we describe a simulation and experimental configuration based on real grid workload traces to demonstrate the interoperable setting as well as provide experimental results as part of a strategic plan for integrating future meta-schedulers.

A decentralized strategy for genetic scheduling in heterogeneous environments

The paper describes a solution to the key problem of ensuring high performance behavior of the Grid, namely the scheduling of activities It presents a distributed, fault-tolerant, scalable and efficient solution for optimizing task assignment The scheduler uses a combination of genetic algorithms and lookup services for obtaining a scalable and highly reliable optimization tool The experiments have been carried out on the MonALISA monitoring environment and its extensions The results demonstrate very good behavior in comparison with other scheduling approaches.

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.

Fault Tolerance and Recovery in Grid Workflow Management Systems

Complex scientific workflows are now commonly executed on global grids. With the increasing scale complexity, heterogeneity and dynamism of grid environments the challenges of managing and scheduling these workflows are augmented by dependability issues due to the inherent unreliable nature of large-scale grid infrastructure. In addition to the traditional fault tolerance techniques, specific checkpoint-recovery schemes are needed in current grid workflow management systems to address these reliability challenges. Our research aims to design and develop mechanisms for building an autonomic workflow management system that will exhibit the ability to detect, diagnose, notify, react and recover automatically from failures of workflow execution. In this paper we present the development of a Fault Tolerance and Recovery component that extends the ActiveBPEL workflow engine. The detection mechanism relies on inspecting the messages exchanged between the workflow and the orchestrated Web Services in search of faults. The recovery of a process from a faulted state has been achieved by modifying the default behavior of ActiveBPEL and it basically represents a non-intrusive checkpointing mechanism. We present the results of several scenarios that demonstrate the functionality of the Fault Tolerance and Recovery component, outlining an increase in performance of about 50% in comparison to the traditional method of resubmitting the workflow.

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.