Performance Predictions for 2017

The year 2016 witnessed amazing progress in the field of technology – from Internet of Things (IoT) and augmented/virtual reality going mainstream, to innovations like testing the first-ever reusable rocket. The IT industry also saw a number of advancements including the rise of Cloud and serverless computing and the growing implementation of DevOps that has redefined IT operations. In this article, we look at some of the major developments expected in the world of APM for the year ahead.

Monitoring for IOT

This past year saw the evolution of IOT – connecting different gadgets and machines we use to automate multiple tasks. This year could see continuing advances in IOT, consumers have adopted smart devices with ease; from wearables to home appliances, the innovators in the IT industry are exploring new areas of implementation. This leads to questions about scalability and reliability. Connected technology calls for constant monitoring to ensure the smart devices function without errors or connectivity issues.

Monitoring tools must be upgraded to handle the complexity posed by highly interconnected networks – IOT devices use multiple sensors to collect data which are streamed to huge data stores. This makes it important to ensure high-speed connectivity. The monitoring tool should be able to detect failures and alert administrators of any unusual spikes in resource utilization. The full potential of IOT can only be tapped if we have all the right tools to monitor and diagnose root causes which could otherwise become a major roadblock in taking IOT forward.

Data Analysis: In-Depth and Predictive

Networks continue to become more complex with more diverse components including CDN providers, DNS providers, third party Ad servers, etc. Each of these components is vital to the overall digital experience and a failure of any one of the components can impact the entire network and eventually the business. APM tools generate GBs and GBs of data, troubleshooting the data can be difficult and figuring out the root cause would require the user to examine all the data available.

This year should see APM tools deliver comprehensive data from different network components across different time spans, all summarized in a single dashboard – this gives the user a 360-degree perspective of the whole network set up. In-depth analysis of the performance data will help predict future failures, the user is armed with the necessary tools to handle different failure scenarios.

Consolidated Dashboard – Better Performance Visibility

Companies use multiple analytics and APM tools to monitor every aspect of the application – from the front-end code to the back-end infrastructure. Analyzing the data collected through different monitoring channels can be overwhelming, especially when faced with a sudden outage or failure.

There is a rising need for a service that aggregates data from different monitoring tools, the end-to-end visibility of a consolidated dashboard helps to correlate data and how each component affects the digital experience. This year could see a rise in the usage of such services. Users can get a snapshot of the application’s health and easily identify points of failure or potential failure.

Monitoring Docker Containers

The year 2017 will see more enterprises adopting Docker containers to run applications. Microservices and containerized applications will work optimally only when resources are allocated efficiently and the process load is balanced. Traditional monitoring tools will not be enough to track containers as each container is an isolated/self-contained environment which can be run irrespective of the host architecture. APM tools will need to monitor resource utilization of each Docker container and measure the performance of every containerized application which will then help identify those processes that require optimization.

Monitoring and Machine Learning

Machine learning was one of the major technology topics of 2016 and it will continue to make an impact this year. Advanced analytics can become even more powerful when combined with Machine Learning. It can be used to analyze Big Data more effectively providing deeper insights into performance. APM tools will not be limited to monitoring and collecting performance data, it will be able to recognize data trends and predict failures. This can also lead to a more efficient and “intelligent” alerting system which helps IT teams respond faster and better to outages or failures.