Providing Fair Share Scheduling on Multicore Cloud Servers via Virtual Runtime-based Task Migration Algorithm

While Linux is the most favored operating system for an open source-based cloud data center, it falls short of expectations when it comes to fair share multicore scheduling. The primary task scheduler of the mainline Linux kernel, CFS, cannot provide a desired level of fairness in a multicore system. CFS uses a weight-based load balancing mechanism to evenly distribute task weights among all cores. Contrary to expectations, this mechanism cannot guarantee fair share scheduling since balancing loads among cores has nothing to do with bounding differences in the virtual runtimes of tasks. To make matters worse, CFS allows a persistent load imbalance among cores. This paper presents a virtual runtime-based task migration algorithm which directly bounds the maximum virtual runtime difference among tasks. For a given pair of cores, our algorithm periodically partitions run able tasks into two groups depending on their virtual runtimes and assigns each group to a dedicated core. In doing so, it bounds the load difference between two cores by the largest weight in the task set and makes the core with larger virtual runtimes receive a larger load and thus run more slowly. It bounds the virtual runtime difference of any pair of tasks running on these cores by a constant. We have implemented the algorithm into the Linux kernel 2.6.38.8. Experimental results show that the maximal virtual runtime difference is 50.53 time units while incurring only 0.14% more run-time overhead than CFS.