Rosario De Chiara

Distributed Load Balancing for Parallel Agent-Based Simulations

We focus on agent-based simulations where a large number of agents move in the space, obeying to some simple rules. Since such kind of simulations are computational intensive, it is challenging, for such a contest, to let the number of agents to grow and to increase the quality of the simulation. A fascinating way to answer to this need is by exploiting parallel architectures. In this paper, we present a novel distributed load balancing schema for a parallel implementation of such simulations. The purpose of such schema is to achieve an high scalability. Our approach to load balancing is designed to be lightweight and totally distributed: the calculations for the balancing take place at each computational step, and in?uences the successive step. To the best of our knowledge, our approach is the ?rst distributed load balancing schema in this context. We present both the design and the implementation that allowed us to perform a number of experiments, with up-to 1, 000, 000 agents. Tests show that, in spite of the fact that the load balancing algorithm is local, the workload distribution is balanced while the communication overhead is negligible.

Bringing together efficiency and effectiveness in distributed simulations: The experience with D-Mason

Agent-based simulation models are an increasingly popular tool for research and management in many fields. In executing such simulations “speed” is one of the most general and important issues because of the size and complexity of simulations. But another important issue is the effectiveness of the solution, which consists of how easily usable and portable the solutions are for the users, i.e. the programmers of the distributed simulation. Our study, then, is aimed at efficient and effective distribute simulations by adopting a framework-level approach, with our design and implementation of a framework, D-Mason, which is a parallel version of the Mason library for writing and running simulations of agent-based simulation models. In particular, besides the efficiency due to workload distribution with small overhead, D-Mason at a framework level proves itself effective since it enables the scientists that use the framework (domain expert but with limited knowledge of distributed programming) only minimally aware of the fact that the simulation is running on a distributed environment. Then, we present tests that compare D-Mason against Mason in order to assess the improved scalability and D-Mason capability to exploit heterogeneous distributed hardware. Our tests also show that several massive simulations that are impossible to execute on Mason (e.g. because of CPU and/or memory requirements) can be easily performed using D-Mason.

A System for Virtual Directories Using Euler Diagrams

In this paper, we describe how to use Euler Diagrams to represent virtual directories. i.e. collection of files that are computed on demand and satisfy a number of constraints. We, then, briefly describe the state of VennFS project that is currently modified to include this new capability. In particular, we show a data structure designed to answer queries about a given Euler Diagram and its sets. The data structure EulerTree described here is based on the R-Tree (see [Pankaj K. Agarwal, Mark de Berg, Joachim Gudmundsson, Mikael Hammar and Herman J. Haverkort, Box-trees and R-trees with near-optimal query time, in: Symposium on Computational Geometry, 2001, pp. 124-133]), a data structure designed for answering range queries over a family of shapes in the 2-dimensional space.

On scheduling dag s for volatile computing platforms: Area-maximizing schedules

Many modern computing platforms-notably clouds and desktop grids-exhibit dynamic heterogeneity: the availability and computing power of their constituent resources can change unexpectedly and dynamically, even in the midst of a computation. We introduce a new quality metric, area, for schedules that execute computations having interdependent constituent chores (jobs, tasks, etc.) on such platforms. Area measures the average number of tasks that a schedule renders eligible for execution at each step of a computation. Even though the definition of area does not mention and properties of host platforms (such as volatility), intuition suggests that rendering tasks eligible at a faster rate will have a benign impact on the performance of volatile platforms-and we report on simulation experiments that support this intuition. We derive the basic properties of the area metric and show how to efficiently craft area-maximizing (A-M) schedules for several classes of significant computations. Simulations that compare A-M scheduling against heuristics ranging from lightweight ones (e.g., FIFO) to computationally intensive ones suggest that A-M schedules complete computations on volatile heterogeneous platforms faster than their competition, by percentages that vary with computation structure and platform behavior-but are often in the double digits.

Real Positioning in Virtual Environments Using Game Engines

Immersive virtual environments offer a natural setting for educational an d instructive experiences for users, and game engine technology offers an interesting, cost-effective and efficien t solution for building them. In this paper we describe an ongoing project whose goal is to provide a v irtual environment where the “real” location of the user is used to position the user’s avatar into the virtual enviro nment.

Interactive visual classification with Euler diagrams

We present the theoretical foundation, the design and the implementation of a library, called EulerVC to interactively handle Euler diagrams for the purposes of resource management. Fast on-line algorithms to interpret wellformed diagrams have been developed utilising a new notion of marked points to keep track of the zone sets. The interface allows the construction of overlapping ellipses to represent categories together with the drag and drop of resources in order to categorise them. A visual indicator can be used to show if the diagram under construction is not wellformed to assist in reducing user mistakes, and sets of tags can be assigned to resources upon export. The generic approach is demonstrated via an integration with the bookmarking site del.icio.us.

Face to Face Cooperation with CoFFEE

Co-located collaboration in classroom is the topic we tackle in this paper. In particular we will describe how CoFFEE implements this kind of collaboration. CoFFEE is an extensible platform on which to implement different collaborative tools. Every tool renders a different kind cooperation between users. In this paper we will also provide further details in about the newly implemented tools for collaboration, the Repository, the Positionometer and the Co-Writer.

An AREA-Oriented Heuristic for Scheduling DAGs on Volatile Computing Platforms

Many modern computing platforms-notably clouds and desktop grids-exhibit dynamic heterogeneity: the availability and computing power of their constituent resources can change unexpectedly and dynamically, even in the midst of a computation. We introduce a new quality metric, AREA, for schedules that execute computations having interdependent constituent chores (jobs, tasks, etc.) on such platforms. AREA measures the average number of chores that a schedule renders eligible for execution at each step of a computation. Even though the definition of AREA does not mention any properties of host platforms (such as volatility), intuition suggests that rendering chores eligible at a faster rate will have a benign impact on the performance of volatile platforms. We report on simulation experiments that support this intuition. Earlier work has derived the basic properties of the AREA metric and has shown how to efficiently craft AREA-maximizing (A-M) schedules for several classes of significant computations. Even though A-M schedules always exist for every computation, it is not always known how to derive such schedules efficiently. In response, the current study develops an efficient algorithm that produces AREA-Oriented (A-O) schedules, which aim to efficiently approximate the AREAs of A-M schedules on arbitrary computations. The simulation experiments reported on here suggest that, in common with A-M schedules, A-O schedules complete computations on volatile heterogeneous platforms faster than a variety of heuristics that range from lightweight ones to computationally intensive ones-albeit not to the same degree as A-M schedules do. Our experiments suggest that schedules having larger AREAs have smaller completion times-but no proof of that yet exists.

Some considerations on the design of a P2P infrastructure for massive simulations

Massive Multiuser Virtual Environments (MMVEs) are rapidly expanding both in the number of users and complexity of interactions. Their needs of computational resources offer new challenges for the computer scientists. In this paper we present some ideas on the implementation of a Massive Simulation Environments, a particular MMVE, distributed over a Peer-to-Peer infrastructure. We analyze some of the problems related to the workload balancing on such distributed environments. In particular we discuss an hybrid Peer-to-Peer architecture in order to provide an efficient load balancing strategy. By some assumptions on temporal and spatial coherence, we use a predictor component which exploits previous phase workload as an estimate for next phase workload for load balancing purposes.

A Flexible and Tailorable Architecture for Scripts in F2F Collaboration

In this paper we introduce the architecture of the script engine of a collaborative co-located discussion support system, named CoFFEE , and, in particular, we describe its extendibility and flexibility as a macro-script engine for CSCL activities [7].