Towards Application-centric Fairness in Multi-tenant Clouds with Adaptive CPU Sharing Model

Anthony O. Ayodele, Jia Rao, Terrance E. Boult
2016 2016 IEEE 9th International Conference on Cloud Computing (CLOUD)  
The performance of cloud application is often quite disappointing due to unmanaged consolidation. Therefore, efforts are required to reduce co-tenants interference and provide predictable application performance in multi-tenant cloud environments. In this paper, we examined the complex interplay among cloud tenants as they compete for CPU time, and shared hardware resources. We propose Adaptive CPU Sharing (ACS) approach that reduces co-tenants interference and provides predictable application
more » ... erformance. Our approach is to monitor the progress of submitted applications at runtime, tracks the slowdown of individual application and applies adjustment until convergence. Thus, when an application suffered more slowdown, we allocate more CPU to reduce unfairness. In establishing system support for fine-grained profiling, we report system level activities at sub-second granularity. We predicted application performance degradation by creating a mathematical relationship between highlevel application performance and low-level machine events (i.e., CPU steal time and L2 caches miss rate). We validate the added value of our approach by comparing application performance slowdowns (average) with various datasets. Based on our experimental results, our approach helps mitigate co-tenant interference and reduces unfairness by minimizing the overall application slowdowns.
doi:10.1109/cloud.2016.0056 dblp:conf/IEEEcloud/AyodeleRB16 fatcat:ltkkh23jmncxpcadgzxgjf4xfi