Cloud networking specialist Alkira has launched Cloud Insights, a free tool to improve the networking and security of enterprise cloud deployments.
Nearly a third (32%) of cloud spending is wasted, according to Flexera’s State of the Cloud Report 2022, which identifies optimisation of existing cloud resources as the top priority for enterprises.
Cloud spending and network complexity accelerated during the pandemic, putting further pressure on businesses to optimise their cloud deployments. Growth shows no sign of slowing this year, as IDC forecasts a 21.7% increase in cloud infrastructure spending for the rest of 2022.
Alkira Cloud Insights helps cloud architects and networking admins regain control of their cloud infrastructure. The tool provides tailored recommendations on how to improve security, optimise spend and boost cloud networking performance in Amazon AWS and Microsoft Azure environments. It can help uncover duplicate IP addresses, unsanctioned internet access, unused networks and security resources, misconfigured security group settings, and unaccounted for shadow IT resources, among other things.
“Many organisations dramatically increased their cloud deployments at the beginning of Covid to support remote work and digital transformation,” said Amir Khan, CEO and founder of Alkira. “Because the shift was so sudden, many organisations’ initial focus was on just making things work; the deployments were far from optimised. Alkira Cloud Insights allows businesses to do what equates to a bit of ‘spring cleaning’ for their environments. The tool helps to streamline cloud deployments, improve security posture and eliminate costly waste.”
Cloud Insights performs a rapid discovery of an organisation’s AWS and Azure cloud networking environments. Companies will get a complete and automated inventory of their networking and security resources. They’ll also receive actionable data, including recommendations to improve cloud usage, security and spend. The insights and findings are delivered in an easy to use, centralised dashboard. There is also the ability to configure automated reporting to keep up to date on dynamic changes made to the cloud network environment.
Brad Casemore, Research Vice President, Datacenter and Multicloud Networks, IDC, said: “Networks that enable and support hybrid and multicloud should be sufficiently intelligent to perform optimally across an increasingly distributed landscape of cloud workloads and end points. But it all starts with meaningful audit and discovery, determining and understanding what workloads are running in which clouds, and providing insight into the networks and security postures associated with those workloads.
“Only with that foreknowledge can enterprises gain actionable insights that yield valuable business benefits and outcomes. With Cloud Insights, Alkira offers customers a free tool that helps to provide a foundational understanding of their cloud environments, setting the stage for informed enhancements that improve the agility, efficiency, performance, resiliency, and security of cloud networking.”
Existing customers get immediate access to Cloud Insights through the Alkira portal. The company is also making it freely available at https://www.alkira.com/cloud-insights to other enterprise teams looking for a quicker and easier way to audit their cloud estate.
How Businesses Can Automate Root Cause Analysis (RCA) With Machine Learning
In the event of a severe incident for your business, you need to analyze what exactly changed (the root cause) to understand its impact.
Using machine learning for root cause analysis can help identify the event that caused the change quickly and easily.
Things can sometimes go wrong in your business’s daily operations. It can be a minor issue, such as a system outage lasting for a couple of minutes. Or it can be something severe as a cyberattack.
Generally, such events result from a chain of actions that eventually culminate in the event. Identifying the root cause is the best way to solve the issue. But manual root cause analysis takes time and often doesn’t provide the exact cause of a mishap. Using machine learning for root cause analysis can automate the process, helping identify the underlying cause quickly and with higher accuracy.
Power of Machine Learning for Root Cause Analysis
To understand why an issue occurred, you need to identify the root cause. But root cause analysis can often be complex and provide inaccurate results. Using machine learning for root cause analysis helps solve this issue.
Using machine learning for root cause analysis can help zero in on the exact location of the problem. You don’t have to scroll through infinite logs to identify which components were impacted and when. The machine learning program can automatically and quickly find the root cause by analyzing a given log data set.
Moreover, the machine learning program can even predict future incidents as more and more data is available. The program compares real-time data with historical data to predict future outcomes and warns you of any unwanted incident beforehand. This will help improve your incident response, reduce downtime and improve productivity.
Benefits of Using Machine Learning for Root Cause Analysis
There are many benefits of using machine learning for root cause analysis. It can help teams take the right action at the right time, minimizing your losses. Some of the benefits are discussed below.
The cost of solving the issue is reduced as your teams don’t have to guess and work around blind spots. Machine learning tools locate the exact line of code responsible for a performance issue, and your team can start working on fixing it right away.
The time spent fixing the issue is significantly reduced as it helps solve business pain faster by locating the cause quickly and accurately.
Provides Long-Lasting Solutions
Machine learning tools provide a permanent solution for your problems and foster a productive and proactive approach.
Grows Your Business
Using machine learning for root cause analysis helps improve the efficiency and productivity of your organization, which eventually leads to business growth.
No system is perfect. Incidents will happen, no matter what. But what you do afterward is in your control. Root cause analysis should be done as soon as possible. Using machine learning for root cause analysis not only improves your incident response, but over time, it can also help prevent incidents from happening in the first place.
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