VLDB 2020, 46th International Conference on Very Large Data Bases, 31 August-4 September 2020, Tokyo, Japan (Virtual Conference) / To be published in Proceedings of the VLDB Endowment, Volume 13, N°12, August 2020
Online Transaction Processing (OLTP) deployments are migrating from on-premise to cloud settings in order to exploit the elasticity of cloud infrastructure which allows them to adapt to workload variations. However, cloud adaptation comes at the cost of redesigning the engine, which has led to the introduction of several, new, cloud-based transaction processing systems mainly focusing on: (i) the transaction coordination protocol, (ii) the data partitioning strategy, and, (iii) the resource isolation across multiple tenants. As a result, standalone OLTP engines cannot be easily deployed
with an elastic setting in the cloud and they need to migrate to another, specialized deployment. In this paper, we focus on workload variations that can be addressed by modern multi-socket, multi-core servers and we present a system design for providing ne-grained elasticity to multi-tenant, scale-up OLTP deployments. We introduce
novel components to the virtualization software stack hat enable on-demand addition and removal of computing and memory resources. We provide a bi-directional, lowoverhead communication stack between the virtual machine and the hypervisor, which allows the former to adapt to variations coming both from the workload and the resource availability. We show that our system achieves NUMAaware, millisecond-level, stateful and ne-grained elasticity, while it is not intrusive to the design of state-of-the-art, inmemory OLTP engines. We evaluate our system through novel use cases demonstrating that scale-up elasticity increases resource utilization, while allowing tenants to pay for actual use of resources and not just their reservation.
© ACM, 2020. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in VLDB 2020, 46th International Conference on Very Large Data Bases, 31 August-4 September 2020, Tokyo, Japan (Virtual Conference) / To be published in Proceedings of the VLDB Endowment, Volume 13, N°12, August 2020 https://doi.org/10.14778/3415478.3415536