- Cloud-Native Observability: Explained
- What is cloud-native observability?
- Why traditional observability tools don’t work for cloud-native architecture?
- How does cloud-native observability help?
- The paradigm shift in cloud-native observability
- How AIOps ensure cloud-native observability?
- Impact of high cloud-native observability on performance metrics
- In a nutshell
Server-based infrastructures are being replaced by cloud solutions across the global IT landscape. However, cloud transformation comes with its challenges. One such challenge is ensuring high observability into cloud infrastructure. The traditional observability systems cannot cope with the advancements of cloud environments. For cloud-native observability, you need better solutions that could handle the non-discrete architecture.
What is cloud-native observability?
Cloud-native observability is the ability to see the status of software systems and other components connected to the cloud environment. Telemetry data is collected from different locations and is analyzed to generate meaningful insights. IT teams must ensure high observability into the cloud environment for reading data flows and system logs. With cloud-native observability, you can know the performance status of your connected systems.
Why traditional observability tools don’t work for cloud-native architecture?
High observability can ensure better application performance monitoring. Since traditional monitoring tools don’t work well with a cloud environment, AI data analytics monitoring tools are in demand. The challenges with traditional observability tools for cloud monitoring are as follows:
- Traditional observability tools were developed when software systems were in silos. Nowadays, only a few servers are used for many software systems, and the interdependencies have increased. To enhance observability into the cloud environment, AI data analytics monitoring tools are needed.
- Cloud environment is dynamic where processes are wiped off the next second. Traditional observability tools were compatible when the processes were fixed and were carried out through the physical storage servers.
- Cloud environment is scalable and, traditional observability tools cannot cope with its scalability.
- Databases and processes that exist in the cloud environment only for some time cannot be recalled via traditional observability
- Traditional observability tools cannot provide any future predictions regarding the performance of components within the cloud architecture.
How does cloud-native observability help?
Due to the challenges stated above, organizations are using AI data analytics monitoring tools for cloud-native observability. Before you know about the role of AI, you should know the benefits of cloud-native observability:
- Cloud-native observability enhances the productivity of IT operations. Cloud-native observability ensures a holistic approach in finding incidents within the IT infrastructure. Issues that occur between different processes can be resolved quickly with cloud-native observability.
- Many processes run at a time in the cloud environment and, it is difficult to track all of them. With cloud-native observability, you can find issues that the system administrators might otherwise miss.
- Cloud-native observability ensures round-the-clock data availability. With a holistic approach, nothing is missed by AI-led cloud observability Every process within the cloud environment is recallable.
- Cloud-native observability can help in knowing future incidents and resolving them proactively. Something which traditional observability tools could never do.
The paradigm shift in cloud-native observability
Due to increased complexity, traditional observability tools cannot monitor system logs, containers, and data in the cloud environment. Without application performance monitoring, you cannot know the internal states of your software systems. In recent years, there has been a major paradigm shift in tools that were used for cloud monitoring. The evolution of new-age technologies like AI and ML have helped firms in enhancing cloud-native observability.
AIOps (Artificial Intelligence for IT Operations) has proved to be a solution for enhancing cloud-native observability. According to a global survey, businesses that are using AIOps based analytics platforms will increase by ten times by the end of 2024. More than 90% of IT professionals believe that high observability drives better organizational decisions. Business decisions and marketing strategies can be formed effectively with the deep insights offered by an AIOps based analytics platform.
How AIOps ensure cloud-native observability?
With AIOps, you can achieve cloud-native observability in the following means:
- Root cause analysis: A cloud environment has many infrastructural segments. Multiple processes run at the same time and it gets difficult to find the root cause of an incident. An AI automated root cause analysis solution can find the source of an incident within your cloud architecture. AIOps platforms ensure rigorous monitoring of the segments of cloud architecture for reporting incidents in real-time.
- Data collection: AI data analytics monitoring tools do not just collect the telemetry and log data. They also collect user data and business data to generate deep insights. Even a virtual database that existed only for a few seconds can be recalled with the aid of AIOps based analytics platforms.
- Application performance monitoring: There must be many software systems connected to your cloud architecture. You need to access the health and performance of crucial software systems to predict their exhaustive capacity. An AIOps based analytics platform can identify signals like traffic, latency, and saturation. With advanced analytics, it can then predict exhaustive capacity and, one can take proactive steps for avoiding power outages.
- Site Reliability Engineer (SRE): The workload of an SRE can be reduced via an AIOps based analytics platform. AIOps platforms can monitor the user experience at endpoints connected to your cloud architecture. AIOps platforms are good at tracing the user path to find the exact point where the error has occurred. With an AIOps based analytics platform, you can find out flaws in the end-user experience. You can provide personalized cloud-based applications or other services to your customers.
Impact of high cloud-native observability on performance metrics
If you enhance cloud-native observability with AIOps, you can significantly decrease your MTTD (Mean Time to Detect). You can quickly find the source of an issue within the cloud environment with AIOps. Since MTTD will decrease, the MTTR (Mean Time to Resolve) will also decrease.
In a nutshell
With the increasing number of businesses that are adopting cloud-based architecture, AIOps has proved to be a reliable solution. Cloud-native observability with AIOps can help you in making the best use of customer, system, and business data. Less time will be spent on resolving the incidents within the cloud infrastructure. Adopt AIOps for cloud monitoring now!