R作业作业排队/工作负载管理
我花了一些时间在 Google 上寻找 R 的排队/负载均衡器包。
我想要实现的目标:
- 从远程前端执行多个相互独立的 R 函数
- 2 个双核服务器作为 R 后端,
利用 :
- 每个函数通常平均每 5 分钟处理一次
- (但可以同时),一组 8-15 个要执行的函数被发送到后端(排队等待处理:首先先入先出)。 5分钟是平均值,可以同时发送多组,并且
- 2x2 R实例已经在运行,加载了所需的包,它们总是相同的,因此不需要一直重新加载它们
- 传输的输入数据量非常低:最大 50k
这里没有代码并行化主题(snow、snowfall foreach、condor 和其他传统集群解决方案)
您知道为 R 设计的一个好的包/工具可以提供帮助吗?
多谢 !
I have been spending some time on Google looking for a queuing / load balancer package for R.
What I am trying to achieve:
- executing multiple independant from eachother R functions from remote front ends
- taking advantage of 2 dual-core servers as R backend
Knowing that:
- each functions is usually processed in 10 to 30 seconds
- every 5 min on average (but it can at the same time) a set of 8-15 functions to be executed is being sent to the backend (queued for processing: first in first out). The 5 min is an average, several sets can be sent at the same time as well
- the 2x2 R instance would already be running, with the required packages loaded, they are always the same, so no need to re-load them all the time
- input amount of data being transfered is very low: 50k max
There is no code parallelization subject here (snow, snowfall foreach, condor and other traditionnal cluster solutions)
Would you know a good package/tool designed for R which could help ?
Thanks a lot !
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这听起来像是使用 RApache 的合理上下文,它可以实例化多个 R 实例和必要的包。
This sounds like a reasonable context for using
RApache
, which can instantiate several R instances and necessary packages.