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Jobs in modern parallel-computing frameworks, such as Hadoop and Spark, are subject to several constraints. In these frameworks, the data are typically distributed across a cluster of machines and is processed in multiple stages. Therefore, tasks that belong to the same stage (job) have a collective completion time that is determined by the slowest task in the collection. Furthermore, a task's processing time is machine dependent, and each machine is capable of processing multiple tasks at a time subject to its capacity. In "Scheduling Parallel-Task Jobs Subject to Packing and Placement Constraints," by Mehrnoosh Shafiee and Javad Ghaderi, multiple approximation algorithms with theoretical guarantees are provided to solve the problem under preemptive and nonpreemptive scenarios. The numerical results, using a real traffic trace, demonstrate that the algorithms yield significant gains over the prior approaches. Motivated by modern parallel computing applications, we consider the problem of scheduling parallel-task jobs with heterogeneous resource requirements in a cluster of machines. Each job consists of a set of tasks that can be processed in parallel; however, the job is considered completed only when all its tasks finish their processing, which we refer to as the synchronization constraint. Furthermore, assignment of tasks to machines is subject to placement constraints, that is, each task can be processed only on a subset of machines, and processing times can also be machine dependent. Once a task is scheduled on a machine, it requires a certain amount of resource from that machine for the duration of its processing. A machine can process (pack) multiple tasks at the same time; however, the cumulative resource requirement of the tasks should not exceed the machine's capacity. Our objective is to minimize the weighted average of the jobs' completion times. The problem, subject to synchronization, packing, and placement constraints, is NP-hard, and prior theoretical results only concern much simpler models. For the case that migration of tasks among the placement-feasible machines is allowed, we propose a preemptive algorithm with an approximation ratio of (6 + ϵ). In the special case that only one machine can process each task, we design an algorithm with an improved approximation ratio of four. Finally, in the case that migrations (and preemptions) are not allowed, we design an algorithm with an approximation ratio of 24. Our algorithms use a combination of linear program relaxation and greedy packing techniques. We present extensive simulation results, using a real traffic trace, that demonstrate that our algorithms yield significant gains over the prior approaches. Funding: This work was supported by the National Science Foundation [Grants CNS-1652115 and CNS-1717867]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/opre.2021.2198. [ABSTRACT FROM AUTHOR]

Background: Street children around the world are accompanying a wide range of risky behaviors. The most common ones include risky sexual behavior, substance and alcohol abuse, and violence. This study aimed to assess risk behaviors and HIV knowledge of street children in Shiraz. Methods: A total of 329 street children (7-18 years of age who spend days or nights on streets with or without their family for earning money) were interviewed through 2014-2016 in Shiraz. Data were collected through a structured interview about high-risk behaviors and HIV/AIDS Knowledge based on a form and questionnaire. Street children were asked to identify HIV/AIDS mode of transmission. All correct answers were scored as one (1), and incorrect, "don't know" responses and no responses scored as zero. The data were analyzed by SPSS software 16 (SPSS, Inc. Chicago, USA) using the Independent t-test and chi-square test, and Pearson's correlation test. P value< 0.05 was considered as statistically significant Results: The mean ± SD age was 13.46±3.09. A total of 86.6% of them were boys. A total of 97.6% of them reported staying with their parents. Street children reported sleeping place as follow: with their parents (n=312, 94.8%), sharing accommodation with other kids (n=13, %4), sleeping in parks (n=2, 7%), and one with relatives. The frequency of smoking, alcohol drinking, and drug abuse were 35 (10.6%), 47 (14. 3%), and 6 (1.8%) respectively. A total of 43 (13.1%) street children reported sexual activity, among them 30 (9.1%) had sexual activity without a condom. Mean ± SD HIV/AIDS knowledge scoring of street children was, 4.1±3.9. Conclusion: Special programs should be implemented in order to reduce high-risk behavior among street children. Intervention should include increasing awareness about alcohol and drug abuse, HIV/AIDS knowledge, sexual and verbal abuse through an organized system with the help of peer education., Competing Interests: Conflicts of Interest: None declared, (© 2020 Iran University of Medical Sciences.)