IT operations deal with a huge volume of data every day which puts tremendous pressure on the IT workforce. In addition, this also results in loss of optimization and real-time monitoring and resolution of issues. Artificial Intelligence in IT Operations or AIOps, efficiently handles these basic tasks to reduce the burden on IT operations and automates basic functions like monitoring, service desk, technical support amongst other solutions.
What is AIOps and how it can help your company?
AIOps works on three aspects: monitoring, engaging, and acting on big data. AIOps basically includes the application of machine learning and big data in IT operations. AIOps not just benefits IT and cloud-based companies but also see implementation in healthcare, finance, insurance, and other sectors.
Some use cases of AIOps include:
- Automation tools for service desk
- Realtime user monitoring tools
- Application performance monitoring
- Ingestion of data to recognize events and remediation
AIOps monitors data across IT systems, devices, and processes and helps companies control the ace at the following:
- Root-cause Analysis
- Anomaly Detection
- Realtime Notification
- Automated Event Management
- Dependency Mapping
This results in reduced costs for companies and less reliance on the human workforce. It also helps in scaling down errors and increasing the productivity of the workforce by organizing shifts for a smooth experience. It offers service reliability as the tool can be operated 24/7.
For example, AIOps in healthcare can easily replace the helpdesk and takeover booking appointments, generating triggers for issues, flagging important and emergency requests along with assigning them to the relevant teams. The prediction and remediation of issues can be a game-changer in the healthcare industry.
How to choose the right AIOps tool for your business
While the application of AIOps is very beneficial for a company, the implementation of the right tool is critical. So how do you identify the best AIOps solution for your business? All AIOps tools do not fit every business. Choosing the right tool depends on a match between your company’s IT goals and the features offered by the AIOps tool. The suitability of the two will determine whether an AIOps product works for your company.
Here are some factors to consider while picking an AIOps tool
- Complexity – The first factor is the level of complexity involved in your business. Higher complex environments require expensive AIOps to be deployed with better features. Understand what kind of features and functions are helpful for your business before implementing an AIOPs tool for your business. AIOps do not reduce complexity but give the company a tool to deal with large sets of data and process it in real-time for better decision making.
- Monitoring – The monitoring features of an AIOps tool are critical while selecting the right tool. However, it is not limited to only monitoring. A tool cannot entirely be considered AIOps if it offers only storage and retrieval of data.
- Connectivity – Connectivity to systems varies for every company and finding an AIOPs tool that offers connectivity to systems like Kubernetes, SAP and others is important. It isn’t easy to deploy such connectivity on your own. It is easy to determine what kind of connectivity your business needs. The factors involved include connectivity to a system and the ability to gather data while controlling that system.
- Return on investment – To measure returns on AIOps, you need historical data and monitor the progress. Typically, the ROI can be measured within 6 months of deployment. The result may not yield 100% results, but it definitely offers increased efficiency. One must also take into consideration the time taken to resolve issues using the human workforce to measure the value of your investment.
- Observability – Through observability, companies can monitor internal systems and use predictive analytics models to find anomalies and detect issues. After detection, the companies can then administer resolution and remediation of such issues. It also helps companies in being proactive in finding solutions for issues and predicting and detecting abnormal behaviors.
- Root-cause analysis – To know the origin of a failure or issue is one of the main features that help businesses trace and remedy an incident or event. Root-cause analysis helps businesses understand the primary cause even in complex and interdependent systems. AIOps tools that provide this feature, help companies that have multi-dependent and interwoven systems.
- Automation features – It is not enough for many businesses to ingest, correlate and understand data, events, and anomalies. The deployment of automated remedies not just saves time and effort but also reduces the costs involved. Automation features replace manual labor and save on human resource costs. It also helps in 24/7 monitoring and resolution which is beneficial to both the company and customers.
Choosing the right AIOps tools varies from company to company as their IT operating systems and requirements are different. However, understanding what your IT infrastructure needs, charting your AIOps transformation journey, and aligning it with your business goals can help you pick the right tool for your business.
AIOps (Artificial Intelligence for IT Operations) has helped businesses to induce automation in their essential business processes. From monitoring software systems to providing actionable insights for incident resolutions, AIOps has proved to be a boon for organizations. However, you cannot just add an AIOps based analytics platform to your IT infrastructure. For AIOps transformation, you need to have a predefined strategy along with addressing the challenges with AIOps adoption. If you do not have a predefined strategy, your AIOps transformation could fail and leave an impact on the service availability. Read on to know five reasons your AIOps transformation could fail and how to avoid them.
Non-compatibility with existing tools
Are your software systems able to exchange information and data seamlessly? An AIOps based analytics platform will require information from other software systems to generate meaningful insights. Interoperability with existing software systems can lead to the failure of your AIOps transformation. If your software systems do not allow you to work with other products or systems, it is time to consider an IT transformation first.
What’s the point of using a digital service desk AI software when the tickets generated by the service desk are ignored by your legacy tools? Make sure that your legacy tools are forwarding the tickets that are generated by the services desk for further analysis. If your legacy tools are compatible with an AIOps based analytics platform, it will automatically consume IT incidents from the service desk and generate actionable insights.
Not knowing the problematic areas
You are not undergoing AIOps transformation just for the sake of adopting a new-age technology. The main purpose of using AI in operations management services is to increase the productivity of your IT operations. Besides focusing on the latest trends in the AI industry, you should focus on the problematic areas for which an AIOps transformation is needed. Some of your IT operations might already be efficient and not require the support of an AIOps based analytics platform.
AIOps adoption can be costly and it is better to find the main problem areas that are decreasing the ROI (Return on Investment). Even the best AIOps tools and products have fixed use cases and can’t help you with something out of the box. AIOps transformation can be costly but will be profitable in the long run if used appropriately.
Lack of training data
An AIOps based analytics platform will require training data to be more efficient with time. Data is like fuel to AI/ML algorithms which helps them to learn about the IT processes. Organizations lack at providing training data to AIOps based analytics platforms which eventually leads to the failure of AIOps transformation. Even the big organizations fail to provide training data to AI/ML algorithms to make them better.
If your training data is messy and contains many outliers, your AIOps based analytics platform will not produce meaningful insights. The organizational data is always scattered across various software systems and is unstructured. Without getting a complete view of the organizational data, AIOps based analytics platforms cannot perform to their fullest.
Not knowing about performance metrics
How would you know that your AIOps transformation is going wrong? Well, one way is to wait and let the failed AIOps transformation impact your ROI. Another way is to use performance metrics to know the benefit of AIOps adoption. If you get to know about inefficient AI DevOps platform management services in time, perhaps you could switch to another transformation strategy. Some of the major performance metrics that can help in determining the impact of AIOps transformation are as follows:
- MTTD: MTTD (Mean Time to Detect) is the time invested in finding out an IT incident. If you have adopted AI for application monitoring, the MTTD should decrease.
- MTTR: MTTR (Mean Time to Detect) is the time taken to fix an IT incident. AI in operations management service should always reduce MTTR significantly.
- Service availability: AIOps platforms will always boost your service availability and reliability. If your service availability is not improving, you need a change in your AIOps strategy.
Failing to embrace the change in IT culture
Your IT culture will go through a major change due to AIOps adoption. At first, it would be hard for your employees to trust the decisions of AI data analytics monitoring tools. However, you can create awareness among your employees regarding the pros of AIOps adoption. You can use open box AI/ML tools that can be customized according to the current IT culture in your organization.
In a nutshell
Just like AIOps platforms offer enhanced observability into software systems, you should have observability into AIOps platforms. You can use various performance metrics for measuring the impact of AIOps on your organization. The global AIOps industry has a CAGR of more than 20% indicating the rise of AI in operations management services.
Would you like an automatic computer update in the middle of booking the only available plane ticket? Imagine that in the context of an organization. While updating or maintaining a software system, the whole IT infrastructure should not come to a standstill. Organizations must ensure service availability while updating or maintaining their software systems. For adding a new microservice, organizations cannot shut down the entire IT system as it would affect service reliability. This is where containers and Kubernetes come into action.
Due to the recent COVID pandemic, the demand for virtual desktop infrastructure solutions has increased. While virtual machines are helpful in the current remote working culture, there are issues with deploying multiple applications. If multiple applications are deployed on a VDI desktop virtualization software, changes to shared dependencies can cause system failures. In order to not compromise with their service availability, firms decided to deploy only one application per virtual machine. However, as evident, a firm using multiple applications cannot use too many virtual machines due to cost constraints.
Containers were introduced to solve the problem of conflicting dependencies while deploying applications on virtual machines. Each container has its own storage, processing power, CPU, and file systems. Since a container has its own operating system, it can be easily decoupled from other applications on a virtual machine. You do not have to affect your service availability each time for adding a new application to your VDI desktop virtualization software. It can run anything from a small microservice to a large application.
Kubernetes (K8s) is an open-source platform for managing the deployment of applications in containers. Launched by Google, K8s can help you run applications on virtual machines without affecting the service availability. The process of managing groups of containers is known as orchestration in the IT world. The functionalities of Kubernetes are as follows:
- K8s decide the appropriate place to deploy your containers by analyzing their resource needs.
- K8s always have a backup container if any container crashes during deployment.
- K8s can manipulate the number of instances based on the CPU requirements.
- The non-volatile storage used by applications inside containers can be managed by K8s.
- K8s are responsible for load balancing of IP address and DNS.
- During an update, K8s closely monitor the health of the instances that are being introduced. If the update crashes, K8s help in restoring the previous version immediately without hampering the service availability.
Why Use Kubernetes?
Kubernetes has many positives:
- Kubernetes is highly portable allowing IT teams to deploy newer applications easily. Firms do not have to change the architecture of their IT infrastructure for adding a new application to virtual machines.
- Besides virtual machines, you can use K8s for deploying containers on cloud environments. With several use cases, IT teams can scale much faster without hampering service reliability.
- K8s is open-source and comes with its cost benefits.
- K8s offer enhanced availability enabling organizations to improve their service availability.
Breaking down the Architecture of K8s
Kubernetes follows the master-slave architecture as it has one master and multiple worker nodes. The master and worker nodes of K8s are explained below:
- Kubernetes Master – For a collection of servers, Kubernetes Master is the central controlling unit. Across each cluster, the networking and communication aspect is managed by the Kubernetes Master. It uses an API server that manages requests from various worker nodes. It also consists of a controller manager to maintain the shared state of a group of servers. It is the main reason why K8s ensure service availability at all times. Other components of Kubernetes Master are Etcd storage and Kubernetes scheduler.
- Worker Nodes – Kubernetes Master decides the workload of various worker nodes. The worker nodes consist of Kubelet that is responsible for monitoring the health of containers. If a worker node fails during deployment, another healthy pod is launched immediately to maintain the service availability. A pod is the structural unit of K8s that represents the workloads that are to be deployed.
Why AIOps is being used with Containers?
AIOps (Artificial Intelligence for IT Operations) is known for its application performance monitoring capabilities. However, organizations are using Kubernetes with an AIOps based analytics platform to achieve better results. An AIOps based analytics platform will offer high observability inside containers. IT teams can correlate the data generated by Kubernetes and system alerts to find the root cause of a particular IT incident. Besides managing current issues with the deployment of containers, an AIOps based analytics platform will also help you in identifying future issues.
In a nutshell
The global Kubernetes solutions market has grown in recent years. The AIOps global market worth is also growing and will be around USD 20 billion by the end of 2025. Start using Kubernetes and AIOps to boost your service availability!
Financial institutions use business applications to provide services to their users. They have to continuously monitor the performance of business applications to enhance service reliability. During peak business time, the number of impactful incident increase that downgrades the performance of business applications. IT experts have to spend more time addressing the incidents one by one. Financial institutions can use an AI-based platform for maintaining business continuity and service reliability. Let us know how ZIF enhances services reliability for financial institutions via AIOps.
What is service reliability?
Financial institutions are undergoing digital transformation quickly. For providing a digital user experience, financial institutions use software systems, applications, etc. The business applications need to perform continuously according to their specifications. If the performance gets deteriorated, business applications may experience downtime. It will have a direct effect on the ROI (Return on Investment).
Service reliability ensures that all the business applications or software systems are error-free. It ensures the continuous performance of IT systems within any financial institution. Business applications should live up to their expectations without any technical error. Financial institutions that have better service reliability also have larger uptime. Service reliability is usually expressed in percentage by IT experts.
What is AIOps?
AIOps (Artificial Intelligence for IT Operations) is used for automating and enhancing IT processes. AIOps uses the mixture of AI and ML algorithms to induce automation in IT processes. In this competitive era, AIOps can help a business optimize its IT infrastructure. IT strategies can be deployed at a large scale using AIOps.
The use of AI in IT operations can reduce the toll on IT experts as they don’t have to work overtime. Any issues with the IT infrastructure can be addresses in real-time using AI. AIOps platforms have gained popularity in recent times due to the challenges posed by the COVID pandemic. Financial institutions can also use an AIOps platform for better DEM (Digital Experience Monitoring).
What is ZIF?
ZIF (Zero Incident Framework) is an AIOps platform launched by GAVS Technologies. The goal of ZIF is to lead organizations towards a zero-incidence scenario. Any incidents within the IT infrastructure can be solved in real-time via ZIF. ZIF is more than just an ordinary TechOps platform. It can help financial institutions to monitor the performance of business applications as well as automate incidence reporting.
Service reliability engineers have to spend hours solving an incidence within the IT infrastructure. The downtime experienced can cost a financial institution more than expected. ZIF is an AI-based platform and will help you in automating responses to incidents within the IT infrastructure. ZIF can help financial institutions gain an edge over their competitors and ensuring business continuity.
Why use ZIF for your financial institution?
ZIF has multiple use cases for a financial institution. If you are facing any of these below-mentioned challenges, you can use ZIF to solve them:
- A financial institution may receive alerts at frequent intervals from the current IT monitoring system. An institution may not have enough workforce or time to address such a high volume of alerts.
- Useful IT operations for a financial institution may face unexpected downtime. It not only impacts the ROI but also drives the customer away.
- High-impact incidents within the IT infrastructure may reduce the service reliability of a financial institution.
- A financial institution may have poor observability of the user experience. It will lead to the inability in providing a personalized digital experience to customers.
- The IT staff of a financial institution may burn out due to the excessive number of incidents being reported. Manual efforts will stop after a certain number of incidents.
How ZIF is the solution?
The functionalities of ZIF that can solve the above-mentioned challenges are as follows:
- ZIF can monitor all components of the IT infrastructure like storage, software system, server, and others. ZIF will perform full-stack monitoring of the IT infrastructure with less human effort.
- ZIF performs APM (Application Performance Monitoring) to measure the performance and accuracy of business applications.
- It can perform real-time APM for improving the user experience.
- It can take data from business applications and can identify relationships between the data. Event correlation alerts by ZIF will also inform you during system outages or failures.
- ZIF can make intelligent predictions for identifying future incidents.
- ZIF can help a financial institution in mitigating an IT issue before it leaves its impact on operations.
What are the outcomes and benefits of ZIF?
The outcomes of using ZIF for your financial institution are as follows:
- Efficiency: With ZIF, you can enhance the efficiency of your IT tools and technologies. When your IT framework is more efficient, you can experience better service reliability.
- Accuracy: ZIF will provide you with predictive insights that can increase the accuracy of business applications. IT operations can be led proactively with the aid of ZIF.
- Reduction in incidents: ZIF will help you in identifying frequent incidents and solving them once and for all. The number of incidents per user can be decreased by the use of ZIF.
- MTTD: ZIF can help you identifying incidents in real-time. Reduced MTTD (Mean Time to Detect) will have a direct impact on the service reliability.
- MTTR: ZIF will reduce the MTTR (Mean Time to Resolve) for your financial institution. With reduced MTTR, you can offer better service reliability.
- Cost optimization: ZIF can replace costly IT operations with cost-effective solutions. If any IT operation is not adding any value to your institution, it can be identified with the aid of ZIF.
ZIF can help you in automating various IT processes like monitoring, incident reporting, and others. Your employees can focus on providing diverse financial services to customers besides worrying about the user interface. ZIF is a cost-effective AIOps solution for your financial institution.
In a nutshell
The CAGR (Compound Annual Growth Rate) of the global AIOps industry is more than 25%. Financial institutions are also using AI for intelligent IT operations and better service reliability. Service reliability engineers in your organization will have to put fewer manual efforts with the help of ZIF. Use ZIF for enhancing service reliability!
In Google’s latest annual developer conference, Google I/O, CEO Sundar Pichai announced their latest breakthrough called “Language Model for Dialogue Applications” or LaMDA. LaMDA is a language AI technology that can chat about any topic. That’s something that even a normal chatbot can do, then what makes LaMDA special?
Modern conversational agents or chatbots follow a narrow pre-defined conversational path, while LaMDA can engage in a free-flowing open-ended conversation just like humans. Google plans to integrate this new technology with their search engine as well as other software like voice assistant, workplace, gmail, etc. so that people can retrieve any kind of information, in any format (text, visual or audio), from Google’s suite of products. LaMDA is an example of what is known as a Large Language Model (LLM).
Introduction and Capabilities
What is a language model (LM)? A language model is a statistical and probabilistic tool that determines the probability of a given sequence of words occurring in a sentence. Simply put, it is a tool that is trained to predict the next word in a sentence. It works like how a text message autocompletes works. Where weather models predict the 7-day forecast, language models try to find patterns in the human language, one of computer science’s most difficult puzzles as languages are ever-changing and adaptable.
A language model is called a large language model when it is trained on enormous amount of data. Some of the other examples of LLMs are Google’s BERT and OpenAI’s GPT-2 and GPT-3. GPT-3 is the largest language model known at the time with 175 billion parameters trained on 570 gigabytes of text. These models have capabilities ranging from writing a simple essay to generating complex computer codes – all with limited to no supervision.
Limitations and Impact on Society
As exciting as this technology may sound, it has some alarming shortcomings.
1. Biasness: Studies have shown that these models are embedded with racist, sexist, and discriminatory ideas. These models can also encourage people for genocide, self-harm, and child sexual abuse. Google is already using an LLM for its search engine which is rooted in biasness. Since Google is not only used as a primary knowledge base for general people but also provides an information infrastructure for various universities and institutions, such a biased result set can have very harmful consequences.
2. Environmental impact: LLMs also have an outsize impact on the environment as these emit shockingly high carbon dioxide – equivalent to nearly five times the lifetime emissions of an average car including manufacturing of the car.
3. Misinformation: Experts have also warned about the mass production of misinformation through these models as because of the model’s fluency, people can confuse into thinking that humans have produced the output. Some models have also excelled at writing convincing fake news articles.
4. Mishandling negative data: The world speaks different languages that are not prioritized by Silicon Valley. These languages are unaccounted for in the mainstream language technologies and hence, these communities are affected the most. When a platform uses an LLM which is not capable of handling these languages to automate its content moderation, the model struggles to control the misinformation. During extraordinary situations, like a riot, the amount of unfavorable data coming in is huge, and this ends up creating a hostile digital environment. The problem does not end here. When the fake news, hate speech, and all such negative text is not filtered, it is used as training data for the next generation of LLMs. These toxic linguistic patterns then parrot back on the internet.
Further Research for Better Models
Despite all these challenges, very little research is being done to understand how this technology can affect us or how better LLMs can be designed. In fact, the few big companies that have the required resources to train and maintain LLMs refuse or show no interest in investigating them. But it’s not just Google that is planning to use this technology. Facebook has developed its own LLMs for translation and content moderation while Microsoft has exclusively licensed GPT-3. Many startups have also started creating products and services based on these models.
While the big tech giants are trying to create private and mostly inaccessible models that cannot be used for research, a New York-based startup, called Hugging Face, is leading a research workshop to build an open-source LLM that will serve as a shared resource for the scientific community and can be used to learn more about the capabilities and limitations of these models. This one-year-long research (from May 2021 to May 2022) called the ‘Summer of Language Models 21’ (in short ‘BigScience’) has more than 500 researchers from around the world working together on a volunteer basis.
The collaborative is divided into multiple working groups, each investigating different aspects of model development. One of the groups will work on calculating the model’s environmental impact, while another will focus on responsible ways of sourcing the training data, free from toxic language. One working group is dedicated to the model’s multilingual character including minority language coverage. To start with, the team has selected eight language families which include English, Chinese, Arabic, Indic (including Hindi and Urdu), and Bantu (including Swahili).
Hopefully, the BigScience Project will help produce better tools and practices for building and deploying LLMs responsibly. The enthusiasm around these large language models cannot be curbed but it can surely be nudged in a direction that has lesser shortcomings. Soon enough, all our digital communications—be it emails, search results, or social media posts —will be filtered using LLMs. These large language models are the next frontier for artificial intelligence.
A pertinent question for the post COVID workforce is, can empathy be learnt? Should it be practiced only by the leaders, or by everyone – can it be seamlessly woven into the fabric of the organization? We are seeing that dynamics at play for remote teams is little unpredictable, making each day uniquely challenging. Empathy is manifested through mindful behaviours, where one’s action is recognized as genuine, personal, and specific to the situation. A few people can be empathetic all the time, a few, practice it consciously, and a few are unaware of it.
Empathy is a natural human response that can be practiced by everyone at work for nurturing an environment of trust. We often confuse empathy for sympathy – while sympathy is feeling sorry for one’s situation, empathy is understanding one’s feelings and needs, and putting the effort to offer authentic support. It requires a shift in perspective, and building trust, respect, and compassion at a deeper level. As Satya Nadella, CEO, Microsoft says, “Empathy is a muscle that needs to be exercised.”
Here are three ways to consciously practice empathy at work –
- Going beyond yourself
It takes a lot to forget how we feel that day, or what is priority for us. However, to be empathetic, one needs to be less judgemental. When one is consciously practicing empathy, one needs to be patient with yourself, your thoughts, and not compare yourself with the person you are empathizing with. If we get absorbed by our own needs, it gets difficult to be generous and compassionate. We need to remember empathy leads to influence and respect, and for that we should not get blind sighted by our perceptions.
- Being a mindful and intentional listener
While practicing empathy, one has refrain from criticism, and be mindful of not talking about one’s problems. We may get sympathetic and give unsolicited advice. Sometimes it only takes to be an intentional listener, by avoiding distractions, and having a very positive body language, and demeanour. This will enable us to ask right questions and collaborate towards a solution.
- Investing in the person
Very often, we support our colleagues and co-workers by responding to their email requests. However, by building positive workplace relationships, and knowing the person beyond his/her email id, makes it much easier to foster empathy. Compassion needs to be not just in words, but in action too, and that can happen only by knowing the person. Taking interest in a co-worker or a team member, beyond a professional capability, does not come out of thin air. It takes conscious continuous efforts to get to know the person, showing care and concern, which will help us to relate to the myriad challenges they go through – be it chronic illness, child care that correlates to his/her ability to engaged at work. It will enable us to personalize the experience, and see the person’s point of view, holistically.
When we take that genuine interest in how we make others feel and experience, we start mindfully practicing empathy. Empathy fosters respect. Empathy helps resolves conflicts better, empathy builds stronger teams, empathy inspires one another to work towards collective goals, and empathy breaks authority. Does it take that extra bit of time to consciously practice it? Yes, but it is all worth it.
Business Environment Overview
In this pandemic economy, the topmost priorities for most companies are to make sure the operations costs and business processes are optimized and streamlined. Organizations must be more proactive than ever and identify gaps that need to be acted upon at the earliest.
The industry has been striving towards efficiency and effectivity in its operations day in and day out. As a reliability check to ensure operational standards, many organizations consider the following levers:
- High Application Availability & Reliability
- Optimized Performance Tuning & Monitoring
- Operational gains & Cost Optimization
- Generation of Actionable Insights for Efficiency
- Workforce Productivity Improvement
Organizations that have prioritized the above levers in their daily operations require dedicated teams to analyze different silos and implement solutions that provide the result. Running projects of this complexity affects the scalability and monitoring of these systems. This is where AIOps platforms come in to provide customized solutions for the growing needs of all organizations, regardless of the size.
Deep Dive into AIOps
Artificial Intelligence for IT Operations (AIOps) is a platform that provides multilayers of functionalities that leverage machine learning and analytics. Gartner defines AIOps as a combination of big data and machine learning functionalities that empower IT functions, enabling scalability and robustness of its entire ecosystem.
These systems transform the existing landscape to analyze and correlate historical and real-time data to provide actionable intelligence in an automated fashion.
AIOps platforms are designed to handle large volumes of data. The tools offer various data collection methods, integration of multiple data sources, and generate visual analytical intelligence. These tools are centralized and flexible across directly and indirectly coupled IT operations for data insights.
The platform aims to bring an organization’s infrastructure monitoring, application performance monitoring, and IT systems management process under a single roof to enable big data analytics that give correlation and causality insights across all domains. These functionalities open different avenues for system engineers to proactively determine how to optimize application performance, quickly find the potential root causes, and design preventive steps to avoid issues from ever happening.
AIOps has transformed the culture of IT war rooms from reactive to proactive firefighting.
Industrial Inclination to Transformation
The pandemic economy has challenged the traditional way companies choose their transformational strategies. Machine learning powered automation for creating an autonomous IT environment is no longer a luxury. he usage of mathematical and logical algorithms to derive solutions and forecasts for issues have a direct correlation with the overall customer experience. In this pandemic economy, customer attrition has a serious impact on the annual recurring revenue. Hence, organizations must reposition their strategies to be more customer centric in everything they do. Thus, providing customers with the best-in-class service coupled with continuous availability and enhanced reliability has become an industry-standard.
As reliability and scalability are crucial factors for any company’s growth, cloud technologies have seen a growing demand. This shift of demand for cloud premises for core businesses has made AIOps platforms more accessible and easier to integrate. With the handshake between analytics and automation, AIOps has become a transformative technology investment that any organization can make.
As organizations scale in size, so does the workforce and the complexity of the processes. The increase in size often burdens organizations with time-pressed teams having high pressure on delivery and reactive housekeeping strategies. An organization must be ready to meet the present and future demands with systems and processes that scale seamlessly. This why AIOps platforms serve as a multilayered functional solution that integrates the existing systems to manage and automate tasks with efficiency and effectivity. When scaling results in process complexity, AIOps platforms convert the complexity to effort savings and productivity enhancements.
Across the industry, many organizations have implemented AIOps platforms as transformative solutions to help them embrace their present and future demand. Various studies have been conducted by different research groups that have quantified the effort savings and productivity improvements.
The AIOps Organizational Vision
As the digital transformation race has been in full throttle during the pandemic, AIOps platforms have also evolved. The industry did venture upon traditional event correlation and operations analytical tools that helped organizations reduce incidents and the overall MTTR. AIOps has been relatively new in the market as Gartner had coined the phrase in 2016. Today, AIOps has attracted a lot of attention from multiple industries to analyze its feasibility of implementation and the return of investment from the overall transformation. Google trends show a significant increase in user search results for AIOps during the last couple of years.
While taking a well-informed decision to include AIOps into the organization’s vision of growth, we must analyze the following:
- Understanding the feasibility and concerns for its future adoption
- Classification of business processes and use cases for AIOps intervention
- Quantification of operational gains from incident management using the functional AIOps tools
AIOps is truly visioned to provide tools that transform system engineers to reliability engineers to bring a system that trends towards zero incidents.
Because above all, Zero is the New Normal.
Today’s always-on businesses and 24×7 uptime demands have necessitated IT monitoring to go into overdrive. While constant monitoring is a good thing, the downside is that the flood of alerts generated can quickly get overwhelming. Constantly having to deal with thousands of alerts each day causes alert fatigue, and impacts the overall efficiency of the monitoring process.
Hence, chalking out an optimal strategy for alert generation & management becomes critical. Pattern-based thresholding is an important first step, since it tunes thresholds continuously, to adapt to what ‘normal’ is, for the real-time environment. Threshold accuracy eliminates false positives and prevents alerts from getting fired incorrectly. Selective alert suppression during routine IT Ops maintenance activities like backups, patches, or upgrades, is another. While there are many other strategies to keep alert numbers under control, a key process in alert management is the grouping of alerts, known as alert correlation. It groups similar alerts under one actionable incident, thereby reducing the number of alerts to be handled individually.
But, how is alert ‘similarity’ determined? One way to do this is through similarity definitions, in the context of that IT landscape. A definition, for instance, would group together alerts generated from applications on the same host, or connectivity issues from the same data center. This implies that similarity definitions depend on the physical and logical relationships in the environment – in other words – the topology map. Topology mappers detect dependencies between applications, processes, networks, infrastructure, etc., and construct an enterprise blueprint that is used for alert correlation.
But what about related alerts generated by entities that are neither physically nor logically linked? To give a hypothetical example, let’s say application A accesses a server S which is responding slowly, and so A triggers alert A1. This slow communication of A with S eats up host bandwidth, and hence affects another application B in the same host. Due to this, if a third application C from another host calls B, alert A2 is fired by C due to the delayed response from B. Now, although we see the link between alerts A1 & A2, they are neither physically nor logically related, so how can they be correlated? In reality, such situations could imply thousands of individual alerts that cannot be combined.
This is one of the many challenges in IT operations that we have been trying to solve at GAVS. The correlation engine of our AIOps Platform ZIF uses algorithmic alert correlation to find a solution for this problem. We are working on two unsupervised machine learning algorithms that are fundamentally different in their approach – one based on pattern recognition and the other based on spatial clustering. Both algorithms can function with or without a topology map, and work around what is supplied and available. The pattern learning algorithm derives associations based on learnings from historic patterns of alert relationships. The spatial clustering algorithm works on the principle of similarity based on multiple features of alerts, including problem similarity derived by applying Natural Language Processing (NLP), and relationships, among several others. Tuning parameters enable customization of algorithmic behavior to meet specific demands, without requiring modifications to the core algorithms. Time is also another important dimension factored into these algorithms, since the clustering of alerts generated over an extended period of time will not give meaningful results.
Traditional alert correlation has not been able to scale up to handle the volume and complexity of alerts generated by the modern-day hybrid and dynamic IT infrastructure. We have reached a point where our ITOps needs have surpassed the limits of human capabilities, and so, supplementing our intelligence with Artificial Intelligence and Machine Learning has now become indispensable.
Cloud computing is the delivery of computing services including Servers, Database, Storage, Networking & others over the internet. Public, Private & Hybrid clouds are different ways of deploying cloud computing.
- In public cloud, the cloud resources are owned by 3rd party cloud service provider
- A private cloud consists of computing resources exclusively by one business or organization
- Hybrid provides the best of both worlds, combines on-premises infrastructure, private cloud with public cloud
Microsoft, Google, Amazon, Oracle, IBM, and others are providing cloud platform to users to host and experience practical business solution. The worldwide public cloud services market is forecast to grow 17% in 2020 to total $266.4 billion and $354.6 billion in 2022, up from $227.8 billion in 2019, per Gartner, Inc.
There are various types of Instances, workloads & options available as part of cloud ecosystem, i.e. IaaS, PaaS, SaaS, Multi-cloud, Serverless.
When very large, large and medium enterprise decides to move their IT environment from on-premise to cloud, they try to move some/most of their on-premises into cloud and keep the rest under their control on-premise. There are various factors that impact the decision, to name a few,
- ROI vs Cost of Cloud Instance, Operation cost
- Architecture dependency of the application, i.e. whether it is monolithic or multi-tier or polyglot or hybrid cloud
- Requirement and need for elasticity and scalability
- Availability of right solution from the cloud provider
- Security of some key data
After crossing all, once the IT environment is cloud-enabled, the challenge comes in ensuring the monitoring of the Cloud-enabled IT environment. Here are some of the business and IT challenges
1. How to ensure the various workloads & Instances are working as expected?
While the cloud provider may give high availability & up time depending on the tier we choose, it is important that our IT team monitors the environment, as in the case of IaaS and to some extent in PaaS as well.
2. How to ensure the Instances are optimally used in terms of compute and storage?
Cloud providers give most of the metrics around the Instances, though it may not provide all metrics that we may need to make decision in all scenarios.
The disadvantage with this model is, cost, latency & not straight forward, e.g. the LOG analytics which comes in Azure involves cost for every MB/GB of data that is stored and the latency in getting the right metrics at right time, if there is latency/delay, you may not get a right result
3. How to ensure the Application or the components of a single solution that are spread across on-premise and Cloud environment is working as expected?
Some cloud providers give tools for integrating the metrics from on-premise to cloud environment to have a shared view.
The disadvantage with this model is, it is not possible to bring in all sorts of data together to get the insights straight. That is, observability is always a question. The ownership of getting the observability lies with the IT team who handles the data.
4. How to ensure the Multi-Cloud + On-Premise environment is effectively monitored & utilized to ensure the best End-user experience?
Multi-Cloud environment – With rapid growing Microservices Architecture & Container based cloud enabled model, it is quite natural that the Enterprise may choose the best from different cloud providers like Azure, AWS, Google & others.
There is little support from cloud provider on this space. In fact, some cloud providers do not even support this scenario.
5. How to get a single panel of view for troubleshooting & root cause analysis?
Especially when problem occurs in Application, Database, Middle Tier, Network & 3rd party layers that are spread across multi-cluster, multi-cloud, elastic environment, it is very important to get a Unified view of entire environment.
ZIF, provides a single platform for Cloud Monitoring.
ZIF has Discovery, Monitoring, Prediction & Remediate that seamlessly fits for a cloud enabled solution. ZIF provides the unified dashboard with insights across all layers of IT infrastructure that is distributed across On-premise host, Cloud Instance & Containers.
Core features & benefits of ZIF for Cloud Monitoring are,
1. Discovery & Topology
- Discovers and provides dynamic mapping of resources across all layers.
- Provides real-time mapping of applications and its dependent layers irrespective of whether the components live on-premise, or on cloud or containerized in cloud.
- Dynamically built topology of all layers which helps in taking effective decisions.
2. Observability across Multi-Cloud, Hybrid-Cloud & On-Premise tiers
- It is not just about collecting metrics; it is very important to analyze the monitored data and provide meaningful insights.
- When the IT infrastructure is spread across multiple cloud platform like Azure, AWS, Google Cloud, and others, it is important to get a unified view of your entire environment along with the on-premise servers.
- Health of each layers are represented in topology format, this helps to understand the impact and take necessary actions.
3. Prediction driven decision for resource optimization
- Prediction engine analyses the metrics of cloud resources and predicts the resource usage. This helps the resource owner to make proactive action rather than being reactive.
- Provides meaningful insights and alerts in terms of the surge in the load, the growth in number of VMs, containers, and the usage of resource across other workloads.
- Authorize the Elasticity & Scalability through real-time metrics.
4. Container & Microservice support
- Understand the resource utilization of your containers that are hosted in Cloud & On-Premise.
- Know the bottlenecks around the Microservices and tune your environment for the spikes in load.
- Provides full support for monitoring applications distributed across your local host & containers in cloud in a multi-cluster setup.
5. Root cause analysis made simple
- Quick root cause analysis by analysing various causes captured by ZIF Monitor instead of going through layer by layer. This saves time to focus on problem-solving and arresting instead of spending effort on identifying the root cause.
- Provides insights across your workload including the impact due to 3rd party layers as well.
- Irrespective of whether the workload and instance is on-premise or on Azure or AWS or other provider, the ZIF automation module can automate the basics to complex activities
7. Ensure End User Experience
- Helps to improve the end-user experience who gets served by the workload from cloud.
- The ZIF tracing helps to trace each & every request of each & every user, thereby it is quite natural for ZIF to unearth the performance bottleneck across all layers, which in turn helps to address the problem and thereby improve the User Experience
Cloud and Container Platform Support
ZIF Seamlessly integrates with following Cloud & Container environments,
- Microsoft Azure
- Google Cloud
- Grafana Cloud