Machine Learning for Cloud Resource Allocation




Wissen Team


May 28, 2024

Cloud management, particularly resource allocation, has become increasingly complex and time-consuming. Issues like over or under-resource-provisioning, managing cloud inefficiencies, lack of streamlined authorization for multiple service providers, and resource diversity stop IT professionals from allocating cloud resources efficiently.

Over 30% of cloud spend goes to waste due to inefficient resource allocation. To add to the woes, the IT teams manually provision resources to prevent lags and crashes and ensure stability. They must also re-evaluate the cloud resources whenever a new version is released to avoid over-provisioning these resources.

With so much effort spent managing and optimizing resources, the IT teams have little time to focus on driving business value. Companies can be left behind in innovation because of these misplaced priorities.

One way to address these issues is leveraging Artificial Intelligence (AI) and Machine Learning (ML) to manage and optimize cloud resources. AI-driven cloud management can help IT teams streamline the provisioning, monitoring, and optimization.

Let’s learn more about this.

How AI/ML Can Streamline Cloud Management?

1. Automate Resource Optimization

As demands fluctuate, IT teams face difficulty optimizing cloud resources on time. This could lead to crashes, unplanned downtimes, and even bad customer experience. However, they can solve this problem by automating the resource optimization process with the help of AI/ML.

AI can provision and adjust resources automatically through 24/7 monitoring and analysis and real-time reporting. Depending on the demand, it can generate new instances or retire existing ones to optimize resource allocation and maintain scalability.

2. Ensure Preventive Maintenance

Imagine it’s the holiday season, and too many customers are using your app to purchase different products. Suddenly, the app crashes, and the customers cannot place orders. Some may wait until you rectify the problem, while others may abandon the app. Either way, you lose customers and revenue.

Now, there could be various reasons for such unpredictable snags. One of them is the inability to meet the surge in resource demand. Another reason could be the inability to predict potential cloud performance issues before they occur. Luckily, both problems can be solved with AI/ML.

With AI/ML, the IT team can analyze historical and real-time data to predict traffic spikes and determine how much storage and computing the application requires at different times. AI/ML is also crucial for detecting anomalies or deviations preventing the cloud from functioning optimally and threatening the application’s security. With AI/ML, the IT teams can detect threats and vulnerabilities early, mitigate the risks proactively, and improve the overall app’s security.

3. Optimize Resources with Real-Time Response

It is impossible to monitor the resources manually 24/7, even if the company hires an army of skilled IT specialists. However, AI/ML can monitor continuously and respond in real-time whenever there is a sudden surge in demand for computing resources. It will automatically allocate resources to meet the demand. Alternately, it will also scale down resources when not required.

AI/ML also offers a granular view of resource allocation to IT teams to make this process more efficient. This helps them detect inefficiencies and bottlenecks and make adjustments to improve scalability and prevent unnecessary downtimes.

4. Reduce Cloud Wastage, Inefficiencies, and Costs

Most companies face cloud wastage issues as the IT team provisions additional resources in anticipation of meeting the surging demands. While autoscaling and dynamic provisioning become essential here, they, too, strain the cloud budget. 

Besides, with over 76% of companies adopting multi and hybrid-cloud approaches, managing different environments has become increasingly difficult. Security concerns, cost management, and governance are the topmost concerns for companies. At the end of the day, companies spend an enormous amount on cloud management and are worried about it.

AI/ML can solve these problems by:

  • Detecting instances of over-provisioned and under-utilized resources and recommending actions to shut down the ones not in use
  • Automatically reducing, adjusting, and managing the cloud workloads depending on the demand
  • Predicting cloud costs or cost overruns based on usage patterns and helping the IT teams make informed decisions about resource allocation to manage the budget
  • Preventing financial losses due to security breaches by continuously monitoring and safeguarding cloud assets

Invest in AI-Driven Cloud Management to Optimize Resource Allocation

Managing cloud resources has become increasingly difficult as more companies shift their workloads to the cloud. At a time when companies demand innovation from their IT teams, old methods of cloud management will leave them with no time to perform high-value tasks.

IT teams must move away from manual processes and embrace AI/ML to optimize cloud resource allocation. AI/ML will optimize resources based on demand, reduce cloud wastage and unnecessary costs, and enable companies to make the most out of their cloud investments.

At Wissen, we help companies manage their cloud resources and solve utilization issues to increase productivity, security, and compliance.

To know more about how you can optimize cloud resource allocation with AI/ML, contact us.