Kubernetes Cluster Analysis

Problem: High Costs and Inefficiency in Cloud Computing

A prominent company was facing a significant challenge with their flagship product. Their Azure cloud infrastructure, consisting of 60 Class D Virtual Machines (VMs), was incurring a hefty monthly expense of $50,000. The primary issue lay in the memory usage, which was constantly hitting the upper limits, averaging between 80% to 100%. This heavy memory utilization triggered frequent auto-scaling events, leading to the deployment of additional VMs and further escalating costs. Despite this, the compute utilization remained surprisingly low, averaging only 20% to 30%, indicating a mismatch between the selected resources and actual computing needs.

Industry:

FinOps

Location:

Services Used :

Data-Driven Optimization

FinOps Optimization

Discounts and Savings

Multidisciplinary team engaging in strategic planning over AKS cluster, enhancing operational efficiency.

Tech professionals collaboratively analyzing AKS cluster, showcasing the power of teamwork in technology innovation.

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Sharp decline in IT operational costs visualized through a descending line graph, reflecting strategic financial planning.

Illustration of effective cost management in IT with a downward cost graph, highlighting successful budget optimization.

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Solution: Strategic Analysis and Transition to Class E VMs

Clear FinOps stepped in to conduct a comprehensive analysis of the current setup. Our investigation revealed the core issue: the company's reliance on Class D VMs, which, while sufficient in computing power, fell short in memory capacity for their specific requirements.

To address this, Clear FinOps proposed a strategic shift to Class E VMs. These memory-optimized machines offered over double the memory capacity compared to the Class D VMs, without a significant compromise in computing power. This change was crucial as it aligned more closely with the company's actual usage patterns, where memory, not compute power, was the bottleneck.

The transition plan also involved reducing the total number of VMs from 60 to 35. This reduction was possible because the Class E VMs' enhanced memory capacity adequately handled the workload that previously required a larger number of less efficient VMs.

Results: Cost Reduction and Enhanced Efficiency

The shift to Class E VMs yielded remarkable results:

Reduced Monthly Expenditure: The company's monthly cloud expenditure dropped significantly, aligning more closely with their actual usage and needs.
Decreased VM Count: The total number of VMs was successfully reduced from 60 to 35, leading to lower operational complexity and maintenance requirements.
Optimized Resource Utilization: With the new setup, memory utilization was balanced, eliminating the need for frequent auto-scaling and thus preventing unnecessary cost surges.
Increased System Stability: The enhanced memory capacity of Class E VMs meant that the system could handle peak loads more effectively, leading to improved performance and reliability.

In conclusion, the strategic intervention by Clear FinOps not only led to significant cost savings but also optimized the company's cloud resource utilization, demonstrating the value of aligning cloud infrastructure with actual workload requirements.

Explore Clear FinOps Services for more expert guidance and support in mastering Azure cost management.