Bulk Scheduling with the DIANA Scheduler

Results from the research and development of a Data Intensive and Network Aware (DIANA) scheduling engine, to be used primarily for data intensive sciences such as physics analysis, are described. In Grid analyses, tasks can involve thousands of computing, data handling, and network resources. The central problem in the scheduling of these resources is the coordinated management of computation and data at multiple locations and not just data replication or movement. However, this can prove to be a rather costly operation and efficient sing can be a challenge if compute and data resources are mapped without considering network costs. We have implemented an adaptive algorithm within the so-called DIANA Scheduler which takes into account data location and size, network performance and computation capability in order to enable efficient global scheduling. DIANA is a performance-aware and economy-guided Meta Scheduler. It iteratively allocates each job to the site that is most likely to produce the best performance as well as optimizing the global queue for any remaining jobs. Therefore it is equally suitable whether a single job is being submitted or bulk scheduling is being performed. Results indicate that considerable performance improvements can be gained by adopting the DIANA scheduling approach.Comment: 12 pages, 11 figures. To be published in the IEEE Transactions in Nuclear Science, IEEE Press. 200

Peer Effects and Alcohol Use Among College Students

This paper examines a natural experiment in which students at a large state university were randomly assigned roommates through a lottery system. We find that on average, males assigned to roommates who reported drinking in the year prior to entering college had one quarter-point lower GPA than those assigned to non-drinking roommates. The 10th percentile of their college GPA is half a point lower than among males assigned non-drinking roommates. For males who themselves drank frequently prior to college, assignment to a roommate who drank frequently prior to college reduces GPA by two-thirds of a point. Since students who drink frequently are particularly influenced by frequent-drinking roommates, substance-free housing programs could potentially lower average GPA by segregating drinkers. The effect of initial assignment to a drinking roommate persists and possibly even grows over time. In contrast, students' college GPA is not influenced by roommates' high school grades, admission test scores, or family background. Females' GPAs are not affected by roommates' drinking prior to college. Overall, these findings are more consistent with models in which peers change preferences than models in which they change endowments.

Clustering and Sharing Incentives in BitTorrent Systems

Peer-to-peer protocols play an increasingly instrumental role in Internet content distribution. Consequently, it is important to gain a full understanding of how these protocols behave in practice and how their parameters impact overall performance. We present the first experimental investigation of the peer selection strategy of the popular BitTorrent protocol in an instrumented private torrent. By observing the decisions of more than 40 nodes, we validate three BitTorrent properties that, though widely believed to hold, have not been demonstrated experimentally. These include the clustering of similar-bandwidth peers, the effectiveness of BitTorrent's sharing incentives, and the peers' high average upload utilization. In addition, our results show that BitTorrent's new choking algorithm in seed state provides uniform service to all peers, and that an underprovisioned initial seed leads to the absence of peer clustering and less effective sharing incentives. Based on our observations, we provide guidelines for seed provisioning by content providers, and discuss a tracker protocol extension that addresses an identified limitation of the protocol

Mutual help groups for mental health problems: A review of effectiveness studies

This paper reviews empirical studies on whether participating in mutual help groups for people with mental health problems leads to improved psychological and social functioning. To be included, studies had to satisfy four sets of criteria, covering: (1) characteristics of the group, (2) target problems, (3) outcome measures, and (4) research design. The 12 studies meeting these criteria provide limited but promising evidence that mutual help groups benefit people with three types of problems: chronic mental illness, depression/anxiety, and bereavement. Seven studies reported positive changes for those attending support groups. The strongest findings come from two randomized trials showing that the outcomes of mutual help groups were equivalent to those of substantially more costly professional interventions. Five of the 12 studies found no differences in mental health outcomes between mutual help group members and non-members; no studies showed evidence of negative effects. There was no indication that mutual help groups were differentially effective for certain types of problems. The studies varied in terms of design quality and reporting of results. More high-quality outcome research is needed to evaluate the effectiveness of mutual help groups across the spectrum of mental health problems

eCustoms Case Study: Mechanisms behind Co-operation Planning

Members of existing e-commerce trading networks constantly assess their network to identify opportunities for increased co-operation and integration of e-commerce IT systems. Failing to identify the mechanisms involved in co-operation compromises correct investment decisions. In this paper, we use Systems Thinking as a reasoning model that helps decision makers to uncover such mechanisms. We use Systems Thinking to analyse a real-world case called eCustoms, an inter-organisational network of customs organisations. The resulting model explains the mechanism of planning co-operation in terms of a feedback loop that comprises political support, operational potential, and information flow. This mechanism also explains why it is important to select potential partners for closer co-operation as early as possible, the importance of willingness to participate, and the gain or loss of decision power that joining a network implies

“It Takes All Kinds”: A Simulation Modeling Perspective on Motivation and Coordination in Libre Software Development Projects

This paper presents a stochastic simulation model to study implications of the mechanisms by which individual software developers’ efforts are allocated within large and complex open source software projects. It illuminates the role of different forms of “motivations-at-the-margin” in the micro-level resource allocation process of distributed and decentralized multi-agent engineering undertakings of this kind. We parameterize the model by isolating the parameter ranges in which it generates structures of code that share certain empirical regularities found to characterize actual projects. We find that, in this range, a variety of different motivations are represented within the community of developers. There is a correspondence between the indicated mixture of motivations and the distribution of avowed motivations for engaging in FLOSS development, found in the survey responses of developers who were participants in large projects.free and open source software (FLOSS), libre software engineering, maintainability, reliability, functional diversity, modularity, developers’ motivations, user-innovation, peer-esteem, reputational reward systems, agent-based modeling, stochastic simulation, stigmergy, morphogenesis.