762088900Digitalization of IT Application & Infrastructure Operations
Digitalization of IT Application & Infrastructure Operations
Ashish Gupta (EPGP-09-093)
Avinish Agrawal (EPGP-09-096)
Manu Goutham P S (EPGP-09-122)
Nilesh Kasare (EPGP-09-127)
Raunak Kumar Sinha (EPGP-09-059)
TOC o “1-3” h z u 1.Acknowledgement PAGEREF _Toc530924405 h 32.Introduction PAGEREF _Toc530924406 h 33.Evolution of Monitoring PAGEREF _Toc530924407 h 44.Case Study PAGEREF _Toc530924408 h 65.Mapping Monitoring Generation to Gartner Digital Transformation stages PAGEREF _Toc530924409 h 116.Conclusion PAGEREF _Toc530924410 h 117.References PAGEREF _Toc530924411 h 11
We would like to express my sincere thanks of gratitude to our teacher Professor A.K. Swain for imparting wonderful insight on IT Strategy and Business Transformation (ITSBT) subject. His Imparted teachings has successful triggered a desire to know more about this subject, which otherwise was completely new to me.
We would also like to express our sincere thanks to Concerned IIM K Professors who have developed such Pedagogy and associated methodologies where projects paves way to have deep understanding on the subject and thus easy to be implemented in our related professional obligations.
This Project – “Digitalization of IT Application & Infrastructure Operations”, is reflection of our deep interest for this subject. It has helped us in doing a lot of Research on this subject due to which we can correlate many theories and their practical implications.
I would also like to thank my family, friends and colleagues who have provided their valuable feedback, support time and guidance as a part of project completion.
Introduction’Perfection would be a fatal flaw for evolution’
In technology, as in life, everything evolves, grows, develops, mutates and diminishes for being replaced with improved versions. During past several decades, application development has evolved and now Applications have become centric part of many businesses, even those that traditionally were not software players. (Walmart’s sophisticated inventory management software is a good example of this.)
Fig-1 Evolution of monitoring compared to Human Evolution
Several of the drivers that contributed to the evolution of application development have, at the same time, increased application complexity.
Cloud – while simplifying the development, deployment and economy of scale of applications, the use of the cloud also added a range of elements that didn’t exist before (variable amount of servers, elasticity, PaaS, etc.) and at the same time reduced development’s visibility into application performance and their ability to replicate and troubleshoot issues.
Agile development – the ever-increasing demand for new features has created the need for agile development. Agile increases the velocity and number of application releases. Resulting in application instability and more bugs which, in turn, drove quick fixes, reduced documentation and reduced control.
Outsourcing – utilizing teams of developers worldwide has created cost-effective ways to speed up development. It has also decentralized an organization’s ability to easily troubleshoot problems. Today’s complex applications have many different components working in parallel requiring many different types of expertise to keep them working as designed. As companies can’t afford to keep unnecessary people on staff they are expecting the developers to be more involved and the management tools they are using to become more intelligent.
Evolution of MonitoringThe evolution of applications has also drove the necessary evolution of application monitoring tools that provide details to assist with identifying, defining, detailing, and troubleshooting application issues.
1st Generation – Watching Infrastructure
In the early stages of server/client application, application monitoring really focused on infrastructure monitoring. Servers uptime, server load, network equipment, and storage. Important logs and errors were simply dumped to text files. When an issue occurred, the operation or support engineer had to view the data in tools like excel or notepad and analyze it manually in order to understand the root cause of an issue.
2nd Generation – Gathering Information
The second generation of application monitoring tools came with the creation of many point tools – each answering a different need that was not covered by traditional monitoring tools, among them you can find log management tools, error aggregation tools, notification tools, APM (Application Performance Management) tools, website monitoring tools, transaction monitoring tools and many others. With this generation an engineer or support manager could get a lot more info than in the first generation and a lot faster. However, trying to correlate problems and train an entire IT team on these all tools is a daunting task.
Acquiring all of these tools can easily cost thousands of dollars of month even for small IT projects. Deploying, supporting, and maintaining these tools also requires a lot of time by already busy system administrators.
But even if we look past costs, the end result is ‘death by tools’ where, with so many tools, when something happens, it is very hard to use all the capabilities of the many tools at hand and engineers are retreating to the simple ‘notepad’ methods.
3rd Generation – Contextual Intelligence
These second generation issues were the trigger for the third generation of application monitoring, that not only unify all the point products into an integrated platform, so there is no need for point products, but be application-aware enough to also correlate the information collected to provide context and intelligence around not just what happened and when, but where, how often and the elusive why.
It’s important to note that in earlier generations of monitoring, it’s the operations engineer, and not the application developer, that has access to the infrastructure, monitoring tools and the resulting monitoring data. Supporting today’s complex and ever changing web applications requires developers to be much more involved in application support. This drives the need for 3rd generation solutions to serve both operations and development teams.
Getting to Contextual Intelligence
As logs, errors, app performance, server performance, database performance, and custom app metrics are becoming integrated and ‘aware’ of each other, the users of these 3rd gen platforms can now get a more complete picture.
Application errors no longer need to be only a few lines with limited information, but rather can be seen as a collection of the error itself, log data, stack traces, web requests, process data, headers, relevant variables affected, and server performance metrics at the time of the error – all giving a more complete picture to developers and making issue diagnostics faster and more efficient.
For example, in the past, server load may or may not have been easily correlated to an issue with, say, web page performance. With an app aware platform, it is easier to see that these things are tied together and happen at the same time, making time-to-resolution shorter.
Until now, the evolution of application development has historically outpaced an organization’s ability to support and troubleshoot those very same applications.
4th Generation – Digital & Intelligent IT Operations
The runtime management of the infrastructure providing service-based systems is a complex task, up to the point where manual operation struggles to be cost effective. As the functionality is provided by a set of dynamically composed distributed services, in order to achieve a management objective multiple operations have to be applied over the distributed elements of the managed infrastructure. Moreover, the manager must cope with the highly heterogeneous characteristics and management interfaces of the runtime resources. With this in mind, this paper proposes to support the configuration and deployment of services with an automated closed control loop. The automation is enabled by the definition of a generic information model, which captures all the information relevant to the management of the services with the same abstractions, describing the runtime elements, service dependencies, and business objectives. On top of that, a technique based on satisfiability is described which automatically diagnoses the state of the managed environment and obtains the required changes for correcting it (e.g., installation, service binding, update, or configuration). The results from a set of case studies extracted from the banking domain are provided to validate the feasibility of this proposal.
Fig-2 Autonomous IT Operations
We have analyzed how Digitalization has Impacted the business, In this case Study an IT Service Provider has took its one of the client through all 4 phases, However for this discussion we would represent its journey for Primarily 3rd and 4th generation of Monitoring
3rd Generation: Converged Application and Infrastructure Monitoring
Using disparate monitoring tools to aggregate application and infrastructure metrics and getting a correlated end-toend view can be difficult. Collecting the alerts and events from multiple tool sets creates a lot of noise for the support staff who then need to make decisions and create some type of repeatable processes for their teams to follow. Organizations need a monitoring solution that will collect, assimilate, and correlate all the events in the application and infrastructure environment, and allow for easy troubleshooting. Understanding the dependencies between the application and underlying infrastructure is imperative for root cause diagnosis. Pinpointing code-level issues using business transaction tracing is one thing; but fault domain isolation when an application slowdown occurs can simplify troubleshooting for IT teams.
A converged application and infrastructure monitoring solution provides unified visibility of the entire application. Then, as a next step, work with IT Operations and begin to automate the incident flow by tying this into your current ITSM and service support platform (e.g. ServiceNow, Remedy, etc.). The solution should be easy enough for the frontline support teams to use (i.e. service desk) and dynamic enough for the Tier 2 support teams to use and get value out of it quickly when troubleshooting. Since events can crop up anywhere in the application delivery chain having more of an app-centric view of the infrastructure is extremely helpful for problem triage
Pls refer the scope of the Monitoring Parameters included in the scope of Converged Application and Infrastructure Monitoring.
Fig-3 Scope of Converged IT Application and Infra Monitoring
As a result of changed scope of parameters which can be monitored, following tools stack were deployed to get an effective business outcome and achieve customer satisfaction.
Fig-4 Tool stack view of Converged IT Application and Infra Monitoring
4th Generation: – Digital & Intelligent IT Operations.
With scope remain the same as in 3rd Generation, following enhancement were performed in tool stack like addition of Robotics and IoT layer to focus fundamentally on interactions between People, Business and Things.
Fig-5 Tool stack view of Digital IT Application and Infra Monitoring
As a result of the Implementation on 4th Generation Monitoring, Following changes in various IT Key Performance Indicators were observed,
Fig-6 Key KPI* Trends across various monitoring generations
Various KPI (Key Performance Indicators) and their meaning
% Reactive Incident: Incidents reported by end users
% Proactive Incidents : Incidents Generated proactively by Monitoring tool, this help to Identify incident before user could notice an issue and report a reactive Incident.
% Parameters Monitored : Number of Parameters being monitored like Availability, Performance etc.
% Incident Auto Resolved: % of Incidents getting auto resolved without human intervention
% Cust Satisfaction: % of overall users who are satisfied by the enhanced services being offered due to implementation of 4th Gen Monitoring.
Mapping Monitoring Generation to Gartner Digital Transformation stages
Below Mapping represents how effectively various monitoring stages could be mapped to Gartner Digital Transformation stages
ConclusionDigital business is an overarching theme that covers how the blurring of the physical and virtual worlds is transforming business designs, industries, markets and organizations. The continuing digital business evolution exploits new digital models to align more closely the physical and digital worlds for employees, partners and customers. Technology will be embedded in everything in the digital business of the future. Rich digital services will be delivered to everything and intelligence will be embedded in everything behind the scenes