EAERS: An Enhanced Version of Autonomic and Elastic Resource Scheduling Framework for Cloud Applications
Zhida Yin, Haopeng Chen, Jianyu Sun, Fei Hu · 2017
Because of the popularity of cloud computing, Cloud Service Providers (CSPs) can rent virtual machines (VMs) from Cloud Providers (CPs) conveniently. In our previous work, we proposed an autonomic and elastic resource scheduling framework, named AERS, which made full use of both proactive and reactive controllers in the field of dynamic resource provision and was integrated with an availability-aware and communication overhead optimized placement strategy. In this paper, we propose an enhanced version of AERS, named EAERS. It eliminates modeling the specific cloud application and instead determines the relationship between workloads and the number of virtual machines (VMs) through self-learning so that the whole scheduling can be carried out in a more transparent manner. Dynamic consolidation is also designed, implemented and integrated into EAERS. Experiments on OpenStack show that EAERS performs as expected.