Apr 21, 2021. By S V Aditya
Cloud computing adoption has grown a staggering amount in the last decade. Almost 60% of enterprises have attained advanced cloud maturity (Flexera 2021 State of the Cloud Report), and another 20% have intermediate cloud maturity. Gartner is projecting an 18% growth in spending on public cloud services in 2021, fuelled by increased global enterprise IT spending on cloud services. 70% of these organizations also plan to grow their spending post-2021. Long story short, cloud computing is growing strongly, and enterprises are more aware of the value proposition of scalable cloud models.
Challenges in Cloud Computing
However, this rapid adoption and growth are not without its challenges. Cloud computing adopters have frequently cited many unaddressed problems. One of their chief concerns is optimizing the high cost of cloud services, especially from IaaS and PaaS usage. These costs are also high because an average of 30% of cloud spend is wasted due to auto-scaling support, dynamic provisioning, and improper garbage collection. While enterprises use automation to cut costs, it is only at a basic level (like shutting down workloads after hours and deploying the right-size instances).
Costs aren't the only problem in cloud operations. 92% of enterprises are now using a multi-cloud deployment with many providers. Also, 80% of enterprises have a hybrid cloud strategy with private and public clouds. These architectures are more complex to manage, resulting in a proliferation of tools to handle them. These challenges are also exacerbated when migrating apps to the cloud. Finally, there is the growth of containerization. Docker and Kubernetes' adoption is growing as an alternative to VMs, which adds another layer of complexity in management with a new set of challenges. The huge volume of data to manage and the variety of cloud elements to handle are too much for CloudOps teams to handle manually. This can result in issues like Alert fatigue and weaken KPIs like MTTR and MTTD as operators work through multiple environments to figure out the problem. Moreover, this can result in higher ticket count generation as teams might miss important issues till users point them out.
There is also the challenge of security. Security in larger enterprises is much more complicated and requires a central cloud management team (CloudOps teams) to coordinate with multiple business units and technical teams. They also have to ensure end-user security with protection like IDSaaS. With the rapid growth in cloud adoption, CloudOps teams have higher workloads than ever. Simply put, there are too many challenges for cloud operations teams to manage, leading to high costs of cloud operations.
AIOps for Cloud Operations
However, these challenges aren't insurmountable. AIOps enables enterprises to address precisely these issues. AI-powered incident recognition allows rapid diagnosis of errors and finds hidden issues allowing the operators to solve them before they become user-facing problems. AI filters through all the noise generated by various alerts to find root cause events. Deep Reinforcement Learning-powered agents can recognize events created by incident recognition and work on an automated resolution. These models can be trained on the history of past actions of operators using audit trails.
AI also simplifies the management of multi-cloud deployments. AI anomaly detection models trained on the normal state of the system can quickly isolate unexpected behavior and identify faulty components. Moreover, it can enhance ITSM activities and auto-fulfill support requests to speed up ticket resolution times and reduce the workload of support teams. A good AIOps platform will also enable better governance of such complex systems. AIOps tools by necessity collect all IT operations data. This makes them great candidates to enable single-pane-of-glass monitoring for the entire IT environment - whether on cloud or on-premise. Moreover, this data can be used to assist in governance functions like capacity planning and compliance. Sequence models can accurately predict usage of cloud components weeks or even months ahead of time, enabling users to avoid resource wastage. Managers can use these platforms to set up and enforce customized rules to control costs.
So what does this all mean in practical terms? Firstly, AIOps can reduce tool proliferation and reduce alert noise - which is one of the pain points for cloud operations teams. Secondly, it simplifies the complexity of the management of multi-cloud systems. AI-powered automation can handle nuanced tasks and incidents which are not handled by multi-cloud management tools. In other words, AIOps adoption means faster adoption of containerization as well. By paving the way for more accessible cloud, VM and container adoption, AIOps enables the enterprise transition to serverless tech. Finally, AIOps makes the organization more capable of handling turnover and employee dependence. Technologies in cloud computing are evolving rapidly, and there's a lack of skilled technical talent that can manage cloud deployments. 75% of the Flexera State of the Cloud 2021 respondents indicated that lack of expertise is a severe hindrance in cloud operations. By using AI to handle all low-level management tasks, organizations can reduce their dependence on critical employees while leveraging complex tasks.
To learn more about Algomox CloudOps, please visit our AIOps CloudOps Page.