Cloud computing has modified the approach with which organizations tend to build and deploy applications. As cloud computing models have evolved, it’s important that technology leaders take effective decisions regarding which cloud services, platforms and payment models deliver the most effective results. Once businesses try to begin their cloud migration journey, they usually miss out on putting in a framework for the cloud price management. This sometimes leads to uncontrolled explosion of an organization’s cloud instances, services or cloud service providers that eventually ends up in high cloud prices. Hence, organizations need to deal with a number of common challenges to include cloud optimization in their cloud strategy..
Significance of Cloud Cost Optimization
No matter wherever organizations are in their cloud journey, cloud cost optimisation remains to be an essential and top-rated concern. As organizations move additional workloads to the cloud, there is a high possibility to lose sight of overall cloud environment and the costs associated with it. Implementation of cloud services offers powerful agility advantages despite a strategy to keep an eye for managing cloud costs, they tend to quickly spiral out of control. Therefore, it's essential to optimize clod costs so as to maximise ROI and cut back TCO.
The 4 components of cloud that leads to Waste
Let’s understand the practical causes for over costing when in cloud, organizations need to consider the following four components while evaluating their Cloud Economics.
Complexity in cloud pricing models
While cloud valuation will appear easy with it’s pay as you go model, where organizations can choose — an defined price for a cloud instance or a price per GB-month for storage — the truth is that there's a dizzying array of choices available that may seem simple on the surface but have a layer of complications underneath which organizations easily tend to miss.
There are ample number of costs only for virtual machines across most of the leading cloud service providers. Instances will have vital value variations based on the location they run in. Older versions of instance families are costlier than their substitutes. Depending upon the add-on characteristics, there will be a great cost difference even for the instnaces with same amount of computational power and memory. Storage is even more with its many alternative tiers and categories. Selecting storage categories on the far side what's required may result in considerably higher prices.
Challenges in choosing the appropriate instance sizes
During the deployment of applications, IT staff must determine instances and sizes to provision it. In several cases they will be unaware of the performance characteristics of the applications or cloud instances. In most of the cases organizations cloud infrastructure is over provisioned, due to the lack of understanding of the IT staff on finding the equivalent instance sizes while migrating from On-Premises to the cloud.
Lack of full visibility into value implications
At the time of provisioning within the cloud, management usually has very little to no visibility into what their applications can value in the cloud environment. The hourly value of cloud instances will appear low, in order that they might not perceive the total impact after they run instances for weeks, months, or years.
This restricted visibility into prices can even be exacerbated throughout agile development processes once teams are mechanically provisioning and razing deployments for development and QA. If templates or machine-controlled scripts are used, instances are repeatedly
overprovisioned, which implies a continuous repetition of wasted prices. And once applications are running, management might not receive coverage that allows them to envision the value implications of this overprovisioning.
Lack of automation to optimize workloads
Optimization is an ever ending process in organizations and its always challenging. Even when waste is known and resolved, as usage of cloud is dynamic there is a high chance of waste reoccurrence.
Therefore automation is important to continuously monitor and counter the waste. For cloud governance teams to make sure efficient cloud use, they need automatic tools that run across all of their cloud resource pools. These tools help spot specific areas of waste and collaborate with management to take automatic action to cut back waste.
The Cost Optimization Life Cycle
As discussed earlier, cost optimization is an ongoing process, not a destination. Let’s understand cost optimization from a universal perspective. There are four common phases of the cloud optimization lifecycle, they are, cost analysis, cost optimization, cloud governance, and cloud usage coupled with cost monitoring.
A crucial phase in exploring the cost trends in line with the usage, unused and over-provisioned resources. This phase also explores the self-managed workloads, region and service selection and accounts and tagging
This phase not just focused on the cost, but also on the, Business goals and metrics, Tagging and cost attribution, Resource consistency and forecasting. The other few aspects that are covered in this phase are, account provisioning baseline, budget reporting and alerts, coverage and utilization thresholds, governance Policies and automation.
In this phase the IT teams will actually terminate the resources which are unused and find the appropriate provisioning with rightsizing by architecting for costs. Creating a private pricing model so as to individually define the costs for each and every instance been used, it also does data transfer optimization by making purchase/modify reservations.
Usage & Cost Monitoring
In this phase,cloud governance teams will sort out the average usage of the cloud applications and data tracks and the associated costing around it. Cost allocations and Reporting and alerts are a part of this phase.
Cloud Optimization Features:
Spend & Resource Optimization
- Right sizing recommendations
- Reserved instance purchase recommendations
- Alerting idle & unused resource
- Machine-Learning/Al-Based Engine
Monitoring & Metering
- Drift analysis
- Consolidated monitoring logs
- Customized Budget and utilization alerts
- Automated remediation
- Automated environment checks & alerts
- Self-Healing Automation
- Amazon EKS
- Azure Kubernetes Service (AKS)
- Google Kubernetes Engine (GKE)
- Chargeback and Showback
- Spend analysis
- Single pane of glass for multi cloud environment
Security & Identity
- Security & Policy mapping
- IAM reporting for access key rotation & password changes
- Continuous security monitoring
Having the best data-driven insights at hand Locuz helps you and your team build a leaner infrastructure, simplifying cloud management in return for your business. Also, over time there is the risk of drifting into technical debt, an outdated or compromised security posture. Locuz can help you evaluate your cloud environment, help you fill the security gaps, and help you optimize efficiency, make it more performance driven at a lesser cost.Mounika Raghavarapu November 26, 2020