A Distributed Self-Learning Approach for Elastic Provisioning of Virtualized Cloud Resources

Although cloud computing has gained sufficient popularity recently, there are still some key impediments to enterprise adoption. Cloud management is one of the top challenges. The ability of on-the-fly partitioning hardware resources into virtual machine(VM) instances facilitates elastic computing environment to users. But the extra layer of resource virtualization poses challenges on effective cloud management. The factors of time-varying user demand, complicated interplay between co-hosted VMs and the arbitrary deployment of multitier applications make it difficult for administrators to plan good VM configurations. In this paper, we propose a distributed learning mechanism that facilitates self-adaptive virtual machines resource provisioning. We treat cloud resource allocation as a distributed learning task, in which each VM being a highly autonomous agent submits resource requests according to its own benefit. The mechanism evaluates the requests and replies with feedback. We develop a reinforcement learning algorithm with a highly efficient representation of experiences as the heart of the VM side learning engine. We prototype the mechanism and the distributed learning algorithm in an iBalloon system. Experiment results on an Xen-based cloud test bed demonstrate the effectiveness of iBalloon. The distributed VM agents are able to reach near-optimal configuration decisions in 7 iteration step sat no more than 5% performance cost. Most importantly, iBalloon shows good scalability on resource allocation by scaling to 128 correlated VMs.