Combining VM Preemption Schemes to Improve Vertical Memory Elasticity Scheduling in Clouds

Abstract

Server consolidation and resource elasticity are among two of the most important resource management features in cloud and edge computing. One of two forms of elasticity is often adopted. While horizontal elasticity is concerned with the acquisition and release of computational nodes in accordance with demand, vertical elasticity focuses on the distribution of a node's resources among its hosted virtual machines (VMs) or containers, by adjusting the capacity of the resource types allocated to each individual VM in accordance with its respective application's needs. In the case of vertical elasticity, when insufficient resources are available to allocate to a given VM, its application's performance may suffer degradation. For online applications, the only alternative is to live-migrate the VM to another server. On the other hand, when running batch jobs, the resource-constrained VM could also be suspended or saved to disk and revived elsewhere or on the same host, when resources become available. Given that memory availability has a significant influence on performance and system throughput, this paper investigates the viability of integrating VM migration, pausing and suspension schemes as part of a VM scheduling strategy to support the execution of both online and batch applications in a virtualized infrastructure employing memory elasticity. Results show that combining such schemes can provide utilization benefits for cloud service providers when memory is scarce.