Increasingly, large organizations are experimenting with internal social media (e.g., blogs, forums) as a platform for widespread distributed collaboration. Contributions to their counterparts outside the organization's firewall are driven by attention from strangers, in addition to sharing among friends. However, employees in a workplace under time pressures may be reluctant to participate-and the audience for their contributions is comparatively smaller. Participation rates also vary widely from group to group. So what influences people to contribute in this environment?In this paper, we present the results of a year-long empirical study of internal social media participation at a large technology company, and analyze the impact attention, feedback, and managers' and coworkers' participation have on employees' behavior. We find feedback in the form of posted comments is highly correlated with a user's subsequent participation. Recent manager and coworker activity relate to users initiating or resuming participation in social media. These findings extend, to an aggregate level, the results from prior interviews about blogging at the company and offer design and policy implications for organizations seeking to encourage social media adoption.
We present the Dynamic Priority (DP) parallel task scheduler for Hadoop. It allows users to control their allocated capacity by adjusting their spending over time. This simple mechanism allows the scheduler to make more efficient decisions about which jobs and users to prioritize and gives users the tool to optimize and customize their allocations to fit the importance and requirements of their jobs. Additionally, it gives users the incentive to scale back their jobs when demand is high, since the cost of running on a slot is then also more expensive. We envision our scheduler to be used by deadline or budget optimizing agents on behalf of users. We describe the design and implementation of the DP scheduler and experimental results. We show that our scheduler enforces service levels more accurately and also scales to more users with distinct service levels than existing schedulers. In addition, our scheduler implementation is smaller and less complex than existing schedulers and it does not rely on job-specific heuristics, while still providing preemption and work conservation efficiently. The economic mechanism enables cost driven scheduling across Hadoop clusters potentially operated from different sites and administrative domains.
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