24,854 research outputs found

    Volunteer Computing Simulation Using Repast And Mason

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    Volunteer environments usually consist of a large number of computing nodes,with highly dynamic characteristics, therefore reliable models for a planning ofthe whole computing are highly desired. An easy to implement approach to mo-delling and simulation of such environments may employ agent-based universalsimulation frameworks, such as RePast or MASON. In the course of the paperthe above-mentioned simulation frameworks are adapted to support simulationof volunteer computing. After giving implementation details, selected resultsconcerning computing time and speedup are given and are compared with theones obtained from an actual volunteer environment

    Considering Disaster Volunteer Behavior and the Work Environment in Managerial Decision Making

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    Over the last two decades, large-scale disaster events have significantly increased in frequency and intensity, causing tremendous loss of lives and property. A large number of relief organizations rely on their volunteers to respond to many disasters around the globe, serving people and communities in need. While their contributions are priceless, turnover among disaster volunteers has become a significant problem for these relief organizations. Work environment factors, such as volunteers being mismatched with tasks, unsuitable workloads, and conflict within groups of volunteers may give rise to turnover intentions, which may in turn lead to actual turnover. The link between work environment factors and volunteer turnover intentions in these situations has not yet received considerable attention in terms of quantitative research. Therefore, the purpose of this dissertation is to develop quantitative models that consider the factors that may cause turnover or turnover intentions. The goal of these models is to help decision makers for non-governmental organizations (NGOs) better manage their disaster volunteers during relief efforts, with the aim of satisfying community needs and improving volunteer retention rates. The first study addresses a gap in volunteer staff planning and scheduling where volunteer training is first presented, with volunteer turnover represented as a percentage of volunteer–task mismatch. We have developed a mixed-integer programming model for assigning optimal volunteer assignments based on a range of possible short- and long-term community need scenarios. The objective is to minimize the costs of unmet community needs, volunteer attrition due to mismatch assignments, and volunteer expenses. Under different demand scenarios, the optimum solution of volunteer assignment is to allow unskilled volunteers to start training early so that they can help skilled volunteers when a peak of long-term skilled task demand is expected. The second study investigates the effects of work environment factors on the satisfaction level and turnover intentions of disaster volunteers. Using an online survey, data from 386 disaster volunteers are collected and analyzed. Confirmatory factor analysis (CFA) and structural equation modeling are used to test the measurement model and answer research questions focused on volunteer behavior. After assessing and confirming the measurement model, we use the structural model to test the hypotheses and provide prediction equations. Job-fit, training, workload, volunteer group, and supervisor are the key work environment factors considered in this study. The findings suggest that these work environment factors have a positive significant relationship with satisfaction and a negative significant relationship with turnover intentions. The last study focuses on developing a simulation modeling approach that considers a volunteer’s satisfaction and turnover intentions in relation to management decisions of an NGO during a relief event. We use a survey to gather information from disaster volunteer managers about how they manage their volunteer teams and use this information and the findings from the second study to model a realistic relief event. We develop a hybrid simulation model, agent based and discrete event (AB-DE), that handles volunteer task and location assignments, as well as workload. Using data analysis from the surveys, we also introduce a group conflict variable within the simulation model. We evaluate the impact of different management decisions on unmet community needs, as well as on volunteer satisfaction and turnover intentions from the organization. This study uses a numerical example based on the survey data. Considering the scenario in which disaster volunteer managers do not assign heavy workload to disaster volunteers, the results of this study suggest that as a surplus of available volunteers’ increases, the overall satisfaction increases while the turnover intention decreases due to dissatisfaction with a non-essential workload as well as from group conflict. In contrast, when the number of volunteers is less than what is needed, disaster volunteers’ satisfaction and turnover intentions were not affected even if there is high group conflict due to the positive effect of the workload that offsets the negative impact of the group conflict

    Investigating grid computing technologies for use with commercial simulation packages

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    As simulation experimentation in industry become more computationally demanding, grid computing can be seen as a promising technology that has the potential to bind together the computational resources needed to quickly execute such simulations. To investigate how this might be possible, this paper reviews the grid technologies that can be used together with commercial-off-the-shelf simulation packages (CSPs) used in industry. The paper identifies two specific forms of grid computing (Public Resource Computing and Enterprise-wide Desktop Grid Computing) and the middleware associated with them (BOINC and Condor) as being suitable for grid-enabling existing CSPs. It further proposes three different CSP-grid integration approaches and identifies one of them to be the most appropriate. It is hoped that this research will encourage simulation practitioners to consider grid computing as a technologically viable means of executing CSP-based experiments faster

    A cooperative approach for distributed task execution in autonomic clouds

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    Virtualization and distributed computing are two key pillars that guarantee scalability of applications deployed in the Cloud. In Autonomous Cooperative Cloud-based Platforms, autonomous computing nodes cooperate to offer a PaaS Cloud for the deployment of user applications. Each node must allocate the necessary resources for customer applications to be executed with certain QoS guarantees. If the QoS of an application cannot be guaranteed a node has mainly two options: to allocate more resources (if it is possible) or to rely on the collaboration of other nodes. Making a decision is not trivial since it involves many factors (e.g. the cost of setting up virtual machines, migrating applications, discovering collaborators). In this paper we present a model of such scenarios and experimental results validating the convenience of cooperative strategies over selfish ones, where nodes do not help each other. We describe the architecture of the platform of autonomous clouds and the main features of the model, which has been implemented and evaluated in the DEUS discrete-event simulator. From the experimental evaluation, based on workload data from the Google Cloud Backend, we can conclude that (modulo our assumptions and simplifications) the performance of a volunteer cloud can be compared to that of a Google Cluster
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