ITune: Engineering the Performance of Xen Hypervisor via Autonomous and Dynamic Scheduler Reconfiguration

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Despite the widespread use of server virtualization technologies in cloud data centers, system administrators experience multiple challenges in configuring the hypervisor’s scheduler parameters to optimize its performance. Manually tuning the scheduler’s parameters is a common practice, however, this approach is not effective particularly when dealing with dynamically changing workload and resource utilizations on the host machines. This problem becomes even harder if cloud resources are overbooked while hosting both latency-sensitive and batched applications. To address these issues, this paper presents iTune, which is a framework for engineering the performance of a hypervisor intelligently via autonomous scheduler configurations. Concretely, iTune optimizes the Xen hypervisor’s scheduler configuration parameters autonomously through a three phase process comprising: (1) Discoverer, which monitors and saves the resource usage history of the host machines and groups set of related host machine workloads, (2) Optimizer, where optimum Xen scheduler configuration parameters for each workload cluster are explored by employing a simulated annealing machine learning algorithm, and (3) Observer, where iTune monitors the resource usage of host machines online, classifies them into one of the categories found in the Discoverer phase, and loads the optimum scheduler parameters determined in the Optimizer phase. Experimental results validate our claims.