Kubernetes Deployment Could Be Uneconomical, Promoting Cloud Wastage
Poorly managed Kubernetes could mean a great deal of time and resources wasted.
A startup massively invested in investigating cloud-related performance and resources with artificial intelligence and machine learning, StormForge revealed that organizations are over-provisioning infrastructure and provoking cloud waste.
After a detailed survey of 105 IT professionals in North America, StormForge revealed that 48% of cloud resource spending ends up being wasted.
StormForge's survey revealed over $17 billion annual waste on cloud resources.
Kubernetes plays the lead role in cloud complexity with a 62% contribution.
One significant contributor and challenge of climate change is the cloud industry. Data centers drastically took their place as major energy consumers and emitters of the carbon population, exuding roughly 100 million metric tons of CO2 into the atmosphere annually, contributing as high as 3% of the world's cumulative energy consumption.
Over-provisioning in IT is an activity as old as IT itself. Developers and IT teams always spend too much to ensure every application never lacks resources to maintain availability and durability. Keeping these standards come at massive costs.
Placing 105 North IT professionals under the microscope, StormForge reveals a cloud resources wastage of 48%, which is just slightly less than half. The survey finds that 76% of organizations have taken it upon themselves to deplete this waste; it hinges high on the to-do list of 33% of organizations in the cloud.
A third of respondents hinted significant increase in cloud spends over the next 12 months, which correlates with the focus on cloud wastage mitigation.
StormForge Vice President of Marketing Jacques Penicaud mentioned that a high volume of that increased spending revolves around the deployment of cloud-native applications on Kubernetes clusters.
A survey explains that about three-quarters of respondents run Kubernetes clusters, with only below half running 11 clusters or more. 33%, a third of these respondents rely on default configuration from cloud service providers to shape their clusters, while 31% use the trial-and-error method. 27%, surprisingly, mentioned reliance on machine learning algorithms in shaping their kubernetes clusters.
55% of companies make their IT Ops, or Cloud Ops teams do the Kubernetes deployment, while about 29% have their development and engineering teams doing it. No one ever seems to get it particularly right due to the utter complexity of the container orchestrator. There are lots of crucial decisions to be made when deploying cloud-native applications on kubernetes. Resource allocation, CPU requests and limits, memory requests and limits, and replicas. What's more, garbage collection and Java Virtual Machine heap size do these for each container, not to mention the multidimensional optimization difficulty. Failing to implement all these acutely takes its toll on performance and reliability.
Last year alone, organizations expended over $17 billion in cloud spend on stagnant resources.
Penicaud depicted that as automation architecture and machine learning algorithm testing tools become widely embraced, over-provisioning should hit low levels due to the ease in Kubernetes deployment and management.
94% of respondents claim knowledge of monthly cloud spend, meaning predictability is not an issue. The issue comes down to control over cloud spend.
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