Abstract-As most on-line services are now hosted on the cloud, customers are requesting Service Level Agreements (SLAs) in order to use cloud services with acceptable Quality of Service. Nonetheless, the cloud is based on provisioning resources on demand (known as cloud elasticity). Hence, it is of primary importance to design multi-tenant cloud storage solutions that can provide storage services with guarantees equivalent or close to bare-metal deployments.In this paper, we address the problem of scheduling volume create requests to backend hosts. We design and implement SLAaware scheduling policies based on the distributed OpenStack scheduling model. We compare and contrast the existing scheduling storage policies by performing a simulation experiment. We demonstrate that a new SLA-aware scheduling policy that takes into account both the available capacity but also the I/O throughput of the backend nodes is needed to offer quality storage services. Our SLA-aware scheduling policy is able to achieve more than 20% improvement in the rate of SLA violations. Furthermore, it requires fewer storage nodes (hence lower capital expenses) and can provide higher volume I/O throughput performance compared to the default policies.
The increasing demand for elastic and scalable cloud block storage requires flexible and efficient ways to provision volumes. The scheduling of volume requests in physical storage nodes or virtualized storage pools is usually based on a single criterion, such as the available capacity or the number of volumes per backend. Those properties are exposed to the cloud block storage scheduler through drivers, and may vary based on the workload. Hence, most cloud storage providers refrain from describing Service Level Objectives (SLOs). In this paper, we present the design and implementation of a new scheduling algorithm for block storage systems that has the following advantages over the currently implemented scheduler in OpenStack. It provides guaranteed SLOs even in a dynamic workload, it increases the I/O throughput of the volumes that have been already provisioned in the backend systems, it can be scalable to a higher arrival rate for the volume requests, and finally it can minimize the number of active hosts (or else the energy consumption). The volume placement process is based on an APX-hard multi-dimensional Vector Bin Packing (V BP d ) algorithm. In order to reduce the complexity we propose a heuristic named Modified Vector Best Fit Decreasing (MVBFD). Our scheduler design for block storage systems is based on the principles of the OpenStack's Cinder scheduler; hence it can be deployed with only minor modifications to an OpenStack block storage deployment.
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