Raiffeisen Bank Aval's Cloud Resource Alignment with Densify: A Case Study
Technology Category
- Infrastructure as a Service (IaaS) - Private Cloud
- Infrastructure as a Service (IaaS) - Virtual Private Cloud
Applicable Industries
- Cement
- Finance & Insurance
Applicable Functions
- Procurement
Use Cases
- Construction Management
- Infrastructure Inspection
Services
- Cloud Planning, Design & Implementation Services
- Hardware Design & Engineering Services
The Customer
Raiffeisen Bank Aval
About The Customer
Raiffeisen Bank Aval is one of the top five commercial banks in Ukraine. The bank offers a wide range of standard and up-to-date banking services to more than 2.5 million customers. As a large financial institution, Raiffeisen Bank Aval has a significant need for efficient and cost-effective cloud infrastructure. The bank was in the process of modernizing their private cloud infrastructure when they encountered difficulties with validating hardware requirements against purchase recommendations made by the hardware vendor. This led to an expected increase in hardware costs for the transformation project.
The Challenge
Raiffeisen Bank Aval, one of the top five commercial banks in Ukraine, faced a significant challenge in modernizing their private cloud infrastructure. The bank was struggling with validating hardware requirements against purchase recommendations made by the hardware vendor. This resulted in an expected additional 33% in hardware costs for the transformation project. The existing cloud environment had a VM-to-host ratio of 24:1, and it consisted of 38 VMware hosts and 923 virtual machines. The bank needed a solution that would help them avoid these additional expenses and ensure that their private cloud resources were perfectly aligned to workload demand patterns and business cycles.
The Solution
Raiffeisen Bank Aval partnered with Densify to address their challenge. Densify ingested all historical VM utilization metrics across the private cloud infrastructure and leveraged machine-learning-driven analytics to model the utilization patterns. This established unique workload profiles for 923 of Raiffeisen’s VMs. With a clear understanding of the workload profiles and new hardware benchmarks, Densify performed in-depth predictive analysis across the infrastructure. This allowed them to scientifically place these workloads onto fewer hosts, while at the same time rightsizing the VMs based on demand patterns. Densify considered factors such as CPU and memory thresholds and overcommit ratios, time-of-day utilization patterns, recurring transaction activities, production versus test and dev environments, Windows and Linux software licensing, benchmarks of new HPE Synergy 480 Gen10 hardware, and high availability during multidimensional permutation analysis.
Operational Impact
Quantitative Benefit
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