The Effect of the Internationalization of the Markets in the European Union Through the Added Value

In this study, an analysis was made of added value from each EU27 economy as a result of the internationalization of the markets, starting from the activity fields in the economy that contribute to obtaining the added value. Thus, a ranking of the countries according to the size of the added value was obtained, an analysis of the changes in activity level in 2017 compared to 2010 and a comparative analysis of the value-added structure between the EU27 and Romania. Finally, a classification of the countries was obtained according to the added value generated by each person employed, at the level of 2017 and it was observed that this indicator shows us the efficiency with which each country manages to value the processes and activities, so that the difference between what produce and what it consumes be high as possible

A Simulation Model for Large Scale Distributed Systems

The use of discrete-event simulators in the design and development of large scale distributed systems is appealing due to their efficiency and scalability. Their core abstractions ofprocess and event map neatly to the components and interactions of modern-day distributed systems and allow designing realistic simulation scenarios. MONARC 2, a multithreaded, process oriented simulation framework designed for modelling large scale distributed systems, allows the realistic simulation of a wide-range of distributed system technologies, with respect to their specific components and characteristics. In this paper we present the design characteristics of the simulation model proposed in MONARC 2. We demonstrate that this model includes the necessary components to describe various actual distributed system technologies, andprovides the mechanisms to describe concurrent network traffic, evaluate different strategies in data replication, and analyze job scheduling procedures. 1

Reputation-guided Evolutionary Scheduling Algorithm for Independent Tasks in inter-Clouds Environments

Self-adaptation provides software with flexibility to different behaviours (configurations) it incorporates and the (semi-) autonomous ability to switch between these behaviours in response to changes. To empower clouds with the ability to capture and respond to quality feedback provided by users at runtime, we propose a reputation guided genetic scheduling algorithm for independent tasks. Current resource management services consider evolutionary strategies to improve the performance on resource allocation procedures or tasks scheduling algorithms, but they fail to consider the user as part of the scheduling process. Evolutionary computing offers different methods to find a near-optimal solution. In this paper we extended previous work with new optimisation heuristics for the problem of scheduling. We show how reputation is considered as an optimisation metric, and analyse how our metrics can be considered as upper bounds for others in the optimisation algorithm. By experimental comparison, we show our techniques can lead to optimised results.Peer Reviewe

Deadline Scheduling for Aperiodic Tasks in inter-Cloud Environments: a new approach to resource management

This is a copy of the author 's final draft version of an article published in the journal Journal of supercomputing. The final publication is available at Springer via http://dx.doi.org/10.1007/s11227-014-1285-8In the big data era, the speed of analytical processing is influenced by the storage and retrieval capabilities to handle large amounts of data. While the distributed crunching applications themselves can yield useful information, the analysts face difficult challenges: they need to predict how much data to process and where, such that to get an optimum data crunching cost, while also respect deadlines and service level agreements within a limited budget. In today's data centers, data processing on demand and data transfers requests coming from distributed applications are usually expressed as aperiodic tasks. In this paper, we challenge the problem of tasks scheduling with deadline constraints of aperiodic tasks within inter-Cloud environments. In massively multithreaded computing systems that deal with data-intensive applications, Hadoop and BaTs tasks arrive periodically, which challenges traditional scheduling approaches previously proposed for supercomputing. Here, we consider the deadline as the main constraint, and propose a method to estimate the number of resources needed to schedule a set of aperiodic tasks, considering both execution and data transfers costs. Starting from classical scheduling techniques, and considering asynchronous tasks handling, we analyze the possibility of decoupling task arriving from task creation, scheduling and execution, sets of actions that can be put into a peer-to-peer relation over a network or over a client-server architecture in the Cloud. Based on a mathematical model, and using different simulation scenarios, we prove the following statements: (1) multiple source of independent aperiodic tasks can be considered similar to a single one; (2) with respect to the global deadline, the tasks migration between different regional centers is the appropriate solution when the number of estimated resources exceed a data center capacity; and (3) in a heterogeneous data center, we need a higher number of resources for the same request in order to respect the deadline constraints. We believe such results will benefit researchers and practitioners alike, who are interested in optimizing the resource management in data centers according to novel challenges coming from next-generation big data applications.Peer ReviewedPostprint (author's final draft

Adaptive Distributed Data Storage for Context-Aware Applications, Journal of Telecommunications and Information Technology, 2013, nr 4

Context-aware computing is a paradigm that relies on the active use of information coming from a variety of sources, ranging from smartphones to sensors. The paradigm usually leads to storing large volumes of data that need to be processed to derive higher-level context information. The paper presents a cloud-based storage layer for managing sensitive context data. To handle the storage and aggregation of context data for context-aware applications, Clouds are perfect candidates. But a Cloud platform for context-aware computing needs to cope with several requirements: high concurrent access (all data needs to be available to potentially a large number of users), mobility support (such platform should actively use the caches on mobile devices whenever possible, but also cope with storage size limitations), real-time access guarantees – local caches should be located closer to the end-user whenever possible, and persistency (for traceability, a history of the context data should remain available). BlobSeer, a framework for Cloud data storage, is a perfect candidate for storing context data for large-scale applications. It offers capabilities such as persistency, concurrency and support for flexible storage schema requirement. On top of BlobSeer, Context Aware Framework is designed as an extension that offers context-aware data management to higher-level applications, and enables scalable high-throughput under high-concurrency. On a logical level, the most important capabilities offered by Context Aware Framework are transparency, support for mobility, real-time guarantees and support for access based on meta-information. On the physical layer, the most important capability is persistent Cloud storage