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Mugurel Ionut Andreica

Publications and source records attributed to Mugurel Ionut Andreica.

At least 37 records · Page 2Linked to original sources

New Algorithmic Approaches for Computing Optimal Network Paths with Several Types of QoS Constraints

The problem of efficiently delivering data within networks is very important nowadays, especially in the context of the large volumes of data which are being produced each year and of the increased data access needs of the users. Efficient data delivery strategies must satisfy several types of Quality of Service (QoS) constraints which are imposed by the data consumers. One possibility of achieving this goal (particularly in the case of in-order data transfers) is to choose a satisfactory network delivery path. In this paper we present novel algorithmic approaches for computing optimal network paths which satisfy several types of (QoS) constraints.

cs.DS↗

Practical Algorithmic Techniques for Several String Processing Problems

The domains of data mining and knowledge discovery make use of large amounts of textual data, which need to be handled efficiently. Specific problems, like finding the maximum weight ordered common subset of a set of ordered sets or searching for specific patterns within texts, occur frequently in this context. In this paper we present several novel and practical algorithmic techniques for processing textual data (strings) in order to efficiently solve multiple problems. Our techniques make use of efficient string algorithms and data structures, like KMP, suffix arrays, tries and deterministic finite automata.

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Critical Analysis of Middleware Architectures for Large Scale Distributed Systems

Distributed computing is increasingly being viewed as the next phase of Large Scale Distributed Systems (LSDSs). However, the vision of large scale resource sharing is not yet a reality in many areas - Grid computing is an evolving area of computing, where standards and technology are still being developed to enable this new paradigm. Hence, in this paper we analyze the current development of middleware tools for LSDS, from multiple perspectives: architecture, applications and market research. For each perspective we are interested in relevant technologies used in undergoing projects, existing products or services and useful design issues. In the end, based on this approach, we draw some conclusions regarding the future research directions in this area.

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Robust Failure Detection Architecture for Large Scale Distributed Systems

Failure detection is a fundamental building block for ensuring fault tolerance in large scale distributed systems. There are lots of approaches and implementations in failure detectors. Providing flexible failure detection in off-the-shelf distributed systems is difficult. In this paper we present an innovative solution to this problem. Our approach is based on adaptive, decentralized failure detectors, capable of working asynchronous and independent on the application flow. The proposed solution considers an architecture for the failure detectors, based on clustering, the use of a gossip-based algorithm for detection at local level and the use of a hierarchical structure among clusters of detectors along which traffic is channeled. The solution can scale to a large number of nodes, considers the QoS requirements of both applications and resources, and includes fault tolerance and system orchestration mechanisms, added in order to asses the reliability and availability of distributed systems.

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Towards a Grid Platform for Scientific Workflows Management

Workflow management systems allow the users to develop complex applications at a higher level, by orchestrating functional components without handling the implementation details. Although a wide range of workflow engines are developed in enterprise environments, the open source engines available for scientific applications lack some functionalities or are too difficult to use for non-specialists. Our purpose is to develop a workflow management platform for distributed systems, that will provide features like an intuitive way to describe workflows, efficient data handling mechanisms and flexible fault tolerance support. We introduce here an architectural model for the workflow platform, based on the ActiveBPEL workflow engine, which we propose to augment with an additional set of components.

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Approximating Mathematical Semantic Web Services Using Approximation Formulas and Numerical Methods

Mathematical semantic web services are very useful in practice, but only a small number of research results are reported in this area. In this paper we present a method of obtaining an approximation of a mathematical semantic web service, from its semantic description, using existing mathematical semantic web services, approximation formulas, and numerical methods techniques. We also give a method for automatic comparison of two complexity functions. In addition, we present a method for classifying the numerical methods mathematical semantic web services from a library.

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Optimal Geometric Partitions, Covers and K-Centers

In this paper we present some new, practical, geometric optimization techniques for computing polygon partitions, 1D and 2D point, interval, square and rectangle covers, as well as 1D and 2D interval and rectangle K-centers. All the techniques we present have immediate applications to several cost optimization and facility location problems which are quite common in practice. The main technique employed is dynamic programming, but we also make use of efficient data structures and fast greedy algorithms.

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Algorithmic Aspects of Several Data Transfer Service Optimization Problems

Optimized data transfer services are highly demanded nowadays, due to the large amounts of data which are frequently being produced and accessed. In this paper we consider several data transfer service optimization problems (optimal server location in path networks, optimal packet sequencing and minimum makespan packet scheduling), for which we provide novel algorithmic solutions.

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Algorithmic Decision Optimization Techniques for Multiple Types of Agents with Contrasting Interests

In this paper I present several algorithmic techniques for improving the decision process of multiple types of agents behaving in environments where their interests are in conflict. The interactions between the agents are modelled by using several types of two-player games, where the agents have identical roles and compete for the same resources, or where they have different roles, like in query-response games. The described situations have applications in modelling behavior in many types of environments, like distributed systems, learning environments, resource negotiation environments, and many others. The mentioned models are applicable in a wide range of domains, like computer science or the industrial (e.g. metallurgical), economic or financial sector.

cs.GT↗

Efficient Offline Algorithmic Techniques for Several Packet Routing Problems in Distributed Systems

In this paper we consider several problems concerning packet routing in distributed systems. Each problem is formulated using terms from Graph Theory and for each problem we present efficient, novel, algorithmic techniques for computing optimal solutions. We address topics like: bottleneck paths (trees), optimal paths with non-linear costs, optimal paths with multiple optimization objectives, maintaining aggregate connectivity information under a sequence of network link failures, and several others.

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Optimal Constrained Resource Allocation Strategies under Low Risk Circumstances

In this paper we consider multiple constrained resource allocation problems, where the constraints can be specified by formulating activity dependency restrictions or by using game-theoretic models. All the problems are focused on generic resources, with a few exceptions which consider financial resources in particular. The problems consider low-risk circumstances and the values of the uncertain variables which are used by the algorithms are the expected values of the variables. For each of the considered problems we propose novel algorithmic solutions for computing optimal resource allocation strategies. The presented solutions are optimal or near-optimal from the perspective of their time complexity. The considered problems have applications in a broad range of domains, like workflow scheduling in industry (e.g. in the mining and metallurgical industry) or the financial sector, motion planning, facility location and data transfer or job scheduling and resource management in Grids, clouds or other distributed systems.

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Efficient Algorithms for Several Constrained Activity Scheduling Problems in the Time and Space Domains

In this paper we consider several constrained activity scheduling problems in the time and space domains, like finding activity orderings which optimize the values of several objective functions (time scheduling) or finding optimal locations where certain types of activities will take place (space scheduling). We present novel, efficient algorithmic solutions for all the considered problems, based on the dynamic programming and greedy techniques. In each case we compute exact, optimal solutions.

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Efficient Algorithms for Several Constrained Resource Allocation, Management and Discovery Problems

In this paper we present efficient algorithmic solutions for several constrained resource allocation, management and discovery problems. We consider new types of resource allocation models and constraints, and we present new geometric techniques which are useful when the resources are mapped to points into a multidimensional feature space. We also consider a resource discovery problem for which we present a guessing game theoretical model.

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Towards a Centralized Scheduling Framework for Communication Flows in Distributed Systems

The overall performance of a distributed system is highly dependent on the communication efficiency of the system. Although network resources (links, bandwidth) are becoming increasingly more available, the communication performance of data transfers involving large volumes of data does not necessarily improve at the same rate. This is due to the inefficient usage of the available network resources. A solution to this problem consists of data transfer scheduling techniques, which manage and allocate the network resources in an efficient manner. In this paper we present several online and offline data transfer optimization techniques, in the context of a centrally controlled distributed system.

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Offline Algorithms for Several Network Design, Clustering and QoS Optimization Problems

In this paper we address several network design, clustering and Quality of Service (QoS) optimization problems and present novel, efficient, offline algorithms which compute optimal or near-optimal solutions. The QoS optimization problems consist of reliability improvement (by computing backup shortest paths) and network link upgrades (in order to reduce the latency on several paths). The network design problems consist of determining small diameter networks, as well as very well connected and regular network topologies. The network clustering problems consider only the restricted model of static and mobile path networks, for which we were able to develop optimal algorithms.

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Data Distribution Optimization using Offline Algorithms and a Peer-to-Peer Small Diameter Tree Architecture with Bounded Node Degrees

Multicast data transfers occur in many distributed systems and applications (e.g. IPTV, Grids, content delivery networks). Because of this, efficient multicast data distribution optimization techniques are required. In the first part of this paper we present a small diameter, bounded degree, collaborative peer-to-peer multicast tree architecture, which supports dynamic node arrivals and departures making local decisions only. The architecture is fault tolerant and, at low arrival and departure rates, converges towards a theoretically optimal structure. In the second part of the paper we consider several offline data distribution optimization problems, for which we present novel and time-efficient algorithmic solutions.

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Algorithmic Solutions to Some Transportation Optimization Problems with Applications in the Metallurgical Industry

In this paper we address several constrained transportation optimization problems (e.g. vehicle routing, shortest Hamiltonian path), for which we present novel algorithmic solutions and extensions, considering several optimization objectives, like minimizing costs and resource usage. All the considered problems are motivated by practical situations arising, for instance, in the mining and metallurgical industry or in data communication. We restrict our attention to transportation networks with path, tree or geometric structures, for which the developed polynomial-time algorithms are optimal or nearly optimal.

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