Fine-Grained Multi-Resource Scheduling in Cloud

Abstract—Cloud datacenters typically require tenants to spec-ify the resource demands for the virtual machines (VMs) they create using a set of pre-defined, fixed configurations, to ease the resource allocation problem. Unfortunately, this leads to low resource utilization of cloud datacenters as tenants are obligated to conservatively predict the maximum resource demand of their applications. We argue that instead of such a static VM resource allocation, a finer-grained dynamic resource allocation and scheduling can substantially improve the utilization of the datacenter resources by increasing the number of jobs accom-modated and correspondingly, the cloud datacenter provider’s revenue. The dynamic real-time scheduling of jobs can also ensure that the performance goals for the tenant VMs are achieved. Examining a typical publicly available cluster data center trace, we observe that a large number of jobs are short. Only a small proportion of jobs are long and which require substantial compute or memory resources. We propose an optimization based approach that exploits this division between the short and long jobs to dynamically allocate a cloud datacenter’s resources to achieve significantly better utilization by increasing the number of jobs accommodated by the datacenter. We use a constraint programming solution to schedule the long jobs, and use simple heuristics to quickly, yet quite accurately schedule the short jobs. Using trace-driven simulations based on public traces collected on provider cluster we show that the overall revenue for the cloud provider can be improved by 30 % over the traditional static VM resource allocation based on the coarse granularity specifications. We are able to increase the number of jobs accommodated using dynamic scheduling by 18%. We also compare the performance of our approach to multi-resource (CPU and memory) first-fit and best-fit algorithms and to the optimal offline solution, and demonstrate that our solution achieves within 76 % of the offline optimal solution. I