Performance Specific I/O Scheduling Framework for Cloud Storage

Abstract

Virtualization is one of the important enabling technologies for Cloud Computing which facilitates sharing of resources among the virtual machines. However, it incurs performance overheads due to contention of physical devices such as disk and network bandwidth. Various I/O applications having different latency requirements may be executing concurrently on different virtual machines provisioned on a single server in Cloud data-centers. It is pertinent that the performance SLAs of such applications are satisfied through intelligent scheduling and allocation of disk resources. The underlying disk scheduler at the server is unable to distinguish between the application requests being oblivious to the characteristics of these applications. Therefore, all the applica- tions are provided best effort services by default. This may lead to performance degradation for the latency sensitive applications. In this work, we propose a novel disk scheduling framework PriDyn (Dynamic Priority) which provides differentiated services to various I/O applications co-located on a single host based on their latency attributes and desired performance. The framework employs a scheduling algorithm which dynamically computes latency estimates for all concurrent I/O applications for a given system state. Based on these, an appropriate pri- ority assignment for the applications is determined which is taken into consideration by the underlying disk scheduler at the host while scheduling the I/O applications on the physical disk. The proposed scheduling framework is able to successfully satisfy QoS requirements for the concurrent I/O applications within system constraints. This has been verified through ex- tensive experimental analysis. In order to realize the benefits of differentiated services provided by the PriDyn scheduler, proper combination of I/O applications must be ensured for the servers through intelligent meta-scheduling techniques at the Cloud data-center level. For achieving this, in the second part of this work, we extended the PriDyn framework to design a proactive admission control and scheduling framework PCOS (P rescient C loud I/O S cheduler). It aims to maximize to Utilization of disk resources without adversely affecting the performance of the applications scheduled on the systems. By anticipating the performance of the systems running multiple I/O applications, PCOS prevents the scheduling of undesirable workloads on them in order to maintain the necessary balance between resource consolidation and application performance guarantees. The PCOS framework includes the PriDyn scheduler as an important component and utilizes the dynamic disk resource allocation capabilities of PriDyn for meeting its goals. Experimental validations performed on real world I/O traces demonstrate that the proposed framework achieves appreciable enhancements in I/O performance through selection of optimal I/O workload combinations, indicating that this approach is a promising step towards enabling QoS guarantees for Cloud data-centers.