An Optimization Framework for Job Rotation to Better Assess the Impact on Overall Risk

We aim to study the potential of job rotation schemes to improve worker safety. To this end, we developed a novel optimization framework based on a recently proposed fatigue-failure model for musculoskeletal disorder (MSD) risk evaluation. We then employed it to conduct an illustrative case study. We demonstrate that the effect of job rotation is highly dependent on the composition of the job pool. Namely, if the job pool contains high-risk tasks (e.g., those carrying greater than 90% probability of developing a disorder), then it may be impossible to observe any risk improvement with rotations alone. On the other hand, if all jobs are already relatively low-risk, then a rotation may be very helpful in achieving risk equity.

Research on Hadoop Greedy Scheduler Based on the Fair

Job scheduling technology is one of the Hadoop platform’s key technologies, whose main function is to control the execute sequence of job and the distribution of computing resources, which directly relates to the Hadoop platform’s overall performance and system resources, usage. However, the existing job scheduling algorithms such as FIFO Scheduler, Fair Scheduler and Capacity Scheduler all have some defects. To overcome theses defects, this paper proposed a new algorithm Hadoop Greedy Scheduler Based on the Fair (HGSF). Firstly, the job pools are sorted by priority from high to low; pools in the same priority are sorted by their minimum requirements. Then their minimum requirements are met in turn. If have spare computing resources, it will be assigned to the job pool which has the highest priority and the minimum difference between the ideal requirement and the minimum requirement. Finally, the algorithm uses delay allocation strategy to improve the localization of the data for computing tasks. The experimental results testified the effectiveness of the proposed algorithm.