Placement of Workloads from Advanced RDBMS Architectures into Complex Cloud Infrastructure

Abstract

Capacity planning is an essential activity in the procurement and daily running of any multi-server computer system. Workload placement is a well known problem and there are several solutions to help address capacity planning problems of knowing where , when and how much resource is needed to place work-loads of varying shapes (resources consumed). Bin-packing algorithms are used extensively in addressing workload placement problems, however, we propose that extensions to existing bin-packing algorithms are required when dealing with workloads from advanced computational architectures such as clustering and consolidation (pluggable), or workloads that exhibit complex data patterns in their signals , such as seasonality, trend and/or shocks (exogenous or otherwise). These extentions are especially needed when consolidating workloads together, for example, consolidation of multiple databases into one ( pluggable databases ) to reduce database server sprawl on estates. In this paper we address bin-packing for singular or clustered environments and propose new algorithms that introduce a time element, giving a richer understanding of the resources requested when workloads are consolidated together, ensuring High Availability (HA) for workloads obtained from advanced database configurations. An experimental evaluation shows that the approach we propose reduces the risk of provisioning wastage in pay-as-you-go cloud architectures.