Teaching about Madrid: A Collaborative Agents-Based Distributed Learning Course

Interactive art courses require a huge amount of computational resources to be running on real time. These computational resources are even bigger if the course has been designed as a Virtual Environment with which students can interact. In this paper, we present an initiative that has been develop in a close collaboration between two Spanish Universities: Universidad Politécnica de Madrid and Universidad Rey Juan Carlos with the aim of join two previous research project: a Collaborative Awareness Model for Task-Balancing-Delivery (CAMT) in clusters and the “Teaching about Madrid” course, which provides a cultural interactive background of the capital of Spain

Simulating Autonomous Mobile Programs on Networks

Autonomous mobile programs (AMPs) have been proposed for load management in dynamic networks. An AMP is aware of its resource needs and periodically seeks a better location in the network to reduce execution time. AMPs have previously been measured using mobile Java Voyager on local area networks (LANs). We have constructed a simulation model of AMPs and reproduced 4 sets of experiments on homogeneous networks, i.e. networks where all locations have the same processor speed, and 2 sets of experiments on heterogeneous networks with collection of large and small AMPs. The results show that simulated collections of AMPs obtain similar balanced states to those reached in the real experiments, and have only minor differences from real experimental results. The simulation model gives an opportunity to explore the greedy effect that can be observed in the real experiments. This gives us confidence to apply the simulation model for further investigation of AMP behaviour, including behaviours on wide area networks

Способ иерархического планирования задач в Grid-системах

The improved method for hierarchical planning is proposed. Efficiency increasing of planning is achieved by physical links of processor’s compute node and ability to support duplex communication.Предложен усовершенствованный способ иерархического планирования. Повышение эффективности планирования достигается за счет физических каналов связи у процессоров вычислительного узла и, возможности поддержки дуплексного режима передачи информации

Load-Scheduling for Residential Hub Structure for Electricity Distribution

This paper presents models of residential energy hubs which can be readily incorporated into automated decision making technologies in smart grids, and can be solved efficiently in a real-time frame to optimally control all major residential energy loads, storage and production components while properly considering the customer preferences and comfort level. Novel mathematical models for major household demand, i.e., fridge, freezer, dishwasher, washer and dryer, stove, water heater, hot tub, and pool pumps are formulated. Also, mathematical models of other components of a residential energy system including lighting, heating, and air-conditioning are developed, and generic models for solar PV panels and energy storage/generation devices are proposed. The static and dyanamic Programming of minimizing energy consumption, total cost of electricity and gas, emissions, peak load, and/or any combination of these objectives, while considering end-user preferences. Several realistic case studies are carried out to examine the performance of the mathematical model, and experimental tests are carried out to find practical procedures to determine the parameters of the model. The application of the proposed model to a real household in Ontario, Canada is presented for various objective functions

A Novel Workload Allocation Strategy for Batch Jobs

The distribution of computational tasks across a diverse set of geographically distributed heterogeneous resources is a critical issue in the realisation of true computational grids. Conventionally, workload allocation algorithms are divided into static and dynamic approaches. Whilst dynamic approaches frequently outperform static schemes, they usually require the collection and processing of detailed system information at frequent intervals - a task that can be both time consuming and unreliable in the real-world. This paper introduces a novel workload allocation algorithm for optimally distributing the workload produced by the arrival of batches of jobs. Results show that, for the arrival of batches of jobs, this workload allocation algorithm outperforms other commonly used algorithms in the static case. A hybrid scheduling approach (using this workload allocation algorithm), where information about the speed of computational resources is inferred from previously completed jobs, is then introduced and the efficiency of this approach demonstrated using a real world computational grid. These results are compared to the same workload allocation algorithm used in the static case and it can be seen that this hybrid approach comprehensively outperforms the static approach

Efficient Generation of Parallel Spin-images Using Dynamic Loop Scheduling

High performance computing (HPC) systems underwent a significant increase in their processing capabilities. Modern HPC systems combine large numbers of homogeneous and heterogeneous computing resources. Scalability is, therefore, an essential aspect of scientific applications to efficiently exploit the massive parallelism of modern HPC systems. This work introduces an efficient version of the parallel spin-image algorithm (PSIA), called EPSIA. The PSIA is a parallel version of the spin-image algorithm (SIA). The (P)SIA is used in various domains, such as 3D object recognition, categorization, and 3D face recognition. EPSIA refers to the extended version of the PSIA that integrates various well-known dynamic loop scheduling (DLS) techniques. The present work: (1) Proposes EPSIA, a novel flexible version of PSIA; (2) Showcases the benefits of applying DLS techniques for optimizing the performance of the PSIA; (3) Assesses the performance of the proposed EPSIA by conducting several scalability experiments. The performance results are promising and show that using well-known DLS techniques, the performance of the EPSIA outperforms the performance of the PSIA by a factor of 1.2 and 2 for homogeneous and heterogeneous computing resources, respectively

Distributed and Centralized Task Allocation: When and Where to Use Them

Self-organisation is frequently advocated as the solution for managing large, dynamic systems. Distributed algorithms are implicitly designed for infinitely large problems, while small systems are regarded as being controllable using traditional, centralised approaches. Many real-world systems, however, do not fit conveniently into these "small" or "large" categories, resulting in a range of cases where the optimal solution is ambiguous. This difficulty is exacerbated by enthusiasts of either approach constructing problems that suit their preferred control architecture. We address this ambiguity by building an abstract model of task allocation in a community of specialised agents. We are inspired by the problem of work distribution in distributed satellite systems, but the model is also relevant to the resource allocation problems in distributed robotics, autonomic computing and wireless sensor networks. We compare the behaviour of a self-organising, market-based task allocation strategy to a classical approach that uses a central controller with global knowledge. The objective is not to prove one mechanism inherently superior to the other; instead we are interested in the regions of problem space where each of them dominates. Simulation is used to explore the trade-off between energy consumption and robustness in a system of intermediate size, with fixed communication costs and varying rates of component failure. We identify boundaries between regions in the parameter space where one or the other architecture will be favoured. This allows us to derive guidelines for system designers, thus contributing to the development of a disciplined approach to controlling distributed systems using self-organising mechanisms