Category: IT operations analytics

How Observability Bolsters Site Reliability Engineering

Posted on by Subhadeep Datta

Are you familiar with the benefits of AIOps (Artificial Intelligence for IT Operation)? Well, artificial intelligence allows businesses to automate IT processes that require fewer human efforts. A key process for automating IT operations is SRE (Site Reliability Engineering). Read on to know about SRE and how it is affected by observability.

What is Site Reliability Engineering?

The IT operations in an organization are performed with the help of various software systems. These software systems are deployed on a large scale and need monitoring. There may be various types of large-scale software systems in an organization like supply-chain management, emergency response, etc. Businesses often hire expert system administrators to oversee the software systems.

To solve the underlying problem, site reliability engineering, a type of software engineering, was introduced. SRE can be termed as a better version of DevOps as it is not divided into multiple teams. SRE aids in building a reliable and scalable software system for an organization. It doesn’t just oversee the software development process but also reduces the friction between the development team and operations team.

Site reliability engineers induce required codes into the software to ensure that it does not need any human intervention The development team constantly wants to launch new updates or software(s). Contrary to that, the operation team only wants to launch an update/software after it is thoroughly tested. SRE removes this conflict between both teams and aids in developing reliable system software.

Understanding Observability in SRE?

How would you work with invisible software? It is difficult to measure the performance of any system software if you are not aware of its internal components that drive the performance. The outputs of system software are analyzed to know about the internal states of system software. High observability allows us to know about the internal states of system software. Observability is the measure of the degree that reflects how well the internal states of system software can be inferred.

Site reliability engineers willingly code their software to provide metrics and logs. These metrics are then used to know about the internal state of the software. Observability may seem similar to monitoring but, it is not. Besides telling about the functioning of the system software, it also provides required data to solve underlying problems in the system.

Due to enhanced observability, site reliability engineers do not have to eliminate a potential risk themselves. They can have access to data insights that contain the possible solution. AIOps with rich observability guide on eliminating a risk/problem in system software. The engineers will only know the external outputs of the system software and can still know about its internal state due to enhanced observability.

Pillars of Observability

The three pillars of observability are as follows:

  • Metrics: Metrics are used by reliability engineers to determine the health of the system. For example, there is a threshold for CPU consumption and, when the consumption goes beyond that number, it is being overused. A metric is defined for CPU consumption and alerts can be triggered when the consumption is more than usual. Site reliability engineers use metrics to find the threshold value and set trigger alerts.
  • Logs: Logs are mostly referred to at the time of any problem with the software system. It is a statement that describes the anomaly that occurred. A log can be generated as plain text or structured texts depending upon the requirements.
  • Traces: A trace helps us in determining the execution of a piece of code. Reliability engineers combine the traces and can find the code execution flow in a distributed software system. One can get to know about the part of code that is being executed in more time as compared to other code blocks.

Knowing about the pillars of observability also highlights its importance. Metrics, logs, and traces can help site reliability engineers to know more about the software system by asking questions from outside. While troubleshooting, reliability engineers do not analyze the pillars of observability separately. The pillars of observability should be analyzed together to gain a better understanding of the system and solve the underlying problems.

Benefits of Observability for Site Reliability Engineers

Some of the main benefits of observability for SRE are as follows:

  • Site reliability engineers study the observability pattern and can easily manage any incidents.
  • It will improve the uptime of software systems in an organization.
  • Observability provides a platform for inspection under SLO (Service -Level Objective).
  • When SLOs are not met and, the system software doesn’t live up to the expectation, observability is used to find the possible solution.
  • An employee has a limit to analyze and understand large chunks of data. With enhanced observability, the cognitive load of the data analysis team is reduced.
  • Observability brings together multiple autonomous teams to work together for a single goal.
  • Observability improves the productivity and innovation standards within an organization.
  • Site reliability engineers do not have to access the internal data of a software system to know about its performance. With enhanced observability, reliability engineers can easily find the performance of the software system by its outputs.

Observability Vs Monitoring

Observability definition may sound similar to monitoring but, it is not. As you delve deeper, you will understand that monitoring only informs us about the underlying problem and not the solution. Contrary to monitoring, observability allows us to find the possible solutions and measure the performance of a system.

The pillars of observability help us in making sure the system software is doing the job it was intended to do. Unlike monitoring, observability can identify and mitigate risks associated with system software(s).

In a Nutshell

For automating business processes and systems, first, you need to know about the underlying problems. Enhanced observability can determine the health of a system and can highlight underlying problems. AI-driven observability is also being preferred by businesses nowadays. Site reliability engineering is strengthened if the systems offer high observability. Go for high observability in your software system(s)!  

Algorithmic Alert Correlation

Posted on by ZIF

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.

Algorithmic Alert Correlation

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.

About the Authors –

Padmapriya Sridhar

Priya is part of the Marketing team at GAVS. She is passionate about Technology, Indian Classical Arts, Travel, and Yoga. She aspires to become a Yoga Instructor someday!

Gireesh Sreedhar KP

Gireesh is a part of the projects run in collaboration with IIT Madras for developing AI solutions and algorithms. His interest includes Data Science, Machine Learning, Financial markets, and Geo-politics. He believes that he is competing against himself to become better than who he was yesterday. He aspires to become a well-recognized subject matter expert in the field of Artificial Intelligence.

Cloud Adoption, Challenges, and Solution Through Monitoring, AI & Automation

Posted on by ZIF

Cloud Adoption

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.

Challenges

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,

  1. ROI vs Cost of Cloud Instance, Operation cost
  2. Architecture dependency of the application, i.e. whether it is monolithic or multi-tier or polyglot or hybrid cloud
  3. Requirement and need for elasticity and scalability
  4. Availability of right solution from the cloud provider
  5. 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 (Zero Incident FrameworkTM), 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.

6. Automation

  • 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
  • AWS
  • Google Cloud
  • Grafana Cloud
  • Docker
  • Kubernetes

About the Author –

Suresh Kumar Ramasamy-Picture

Suresh Kumar Ramasamy


Suresh heads the Monitor component of ZIF at GAVS. He has 20 years of experience in Native Applications, Web, Cloud, and Hybrid platforms from Engineering to Product Management. He has designed & hosted the monitoring solutions. He has been instrumental in conglomerating components to structure the Environment Performance Management suite of ZIF Monitor.

Suresh enjoys playing badminton with his children. He is passionate about gardening, especially medicinal plants.

Growing Importance of Business Service Reliability

Posted on by ZIF

Business services are a set of business activities delivered to an outside party, such as a customer or a partner. Successful delivery of business services often depends on one or more IT services. For example, an IT business service that would support “order to cash”, as an example could be “supply chain service”. The supply chain service could be delivered by an application such as SAP, with the customer of that service being an employee in finance/accounting using the application to perform customer-facing services such as accounts receivable, or the collection of cash from an outside party. A business service is not simply the application that the end-user sees – it is the entire chain that supports the delivery of the service, including physical and virtualized servers, databases, middleware, storage, and networks. A failure in any of these can affect the service – and so it is crucial that IT organizations have an integrated, accurate, and up-to-date view of these components and of how they work together to provide the service.

The technologies for Social Networking, Mobile Applications, Analytics, Cloud (SMAC), and Artificial Intelligence (AI) are redefining the business and the services that businesses provide. Their widespread usage is changing the business landscape, increasing reliability and availability to levels that were unimaginable even a few years ago.

Availability versus Reliability

At first glance, it might seem that if a service has a high availability then it should also have high reliability. However, this is not necessarily the case. Availability and Reliability have different meanings, serve different purposes, and require different strategies to maintain desired standards of service levels. Reliability is the measure of how long a business service performs its intended function, whereas availability is the measure of the percentage of time a business service is operable. For example, a business service may be available 90% of the time, but reliable only 75% of the time from a performance standpoint. Service reliability can be seen as:

  • Probability of success
  • Durability
  • Dependability
  • Quality over time
  • Availability to perform a function

Merely having a service available isn’t sufficient. When a business service is available, it should actually serve the intended purpose under varying and unexpected conditions. One way to measure this performance is to evaluate the reliability of the service that is available to consume. The performance of a business service is now rated not by its availability, but by how consistently reliable it is. Take the example of mobile services – 4 bars of signal strength on your smartphone does not guarantee that the quality of the call you received or going to make. Organizations need to measure how well the service fulfills the necessary business performance needs.

Recognizing the importance of reliability, Google initiated Site Reliability Engineering (SRE) practices with a mission to protect, provide for, and progress the software and systems behind all of Google’s public services — Google Search, Ads, Gmail, Android, YouTube, and App Engine, to name just a few — with an ever-watchful eye on their availability, latency, performance, and capacity.

Zero Incident FrameworkTM (ZIF)

GAVS Technologies developed an AIOps based TechOps platform – Zero Incident FrameworkTM (ZIF) that enables proactive detection and remediation of incidents. The ZIF Platform is, available in two versions for our customers to evaluate and experience the power of AI-driven Business Service Reliability: 

ZIF Business Xpress: ZIF Business Xpress has been engineered for enterprises to evaluate AIOps before adoption. 10 to 40 devices can be connected to ZIFBusiness Xpress, to experiment with the value proposition. 

ZIF Business: Targeted for enterprise-wide adoption.

For more details, please visit https://zif.ai

About the Author:

Sri Chaganty


Sri is a Serial Entrepreneur with over 30 years’ experience delivering creative, client-centric, value-driven solutions for bootstrapped, and venture-backed startups.

Inverse Reinforcement Learning

Posted on by ZIF

Naresh B

What is Inverse Reinforcement Learning(IRL)?

Inverse reinforcement learning is a recently developed Machine Learning framework that can solve the inverse problem of Reinforcement Learning (RL). Basically, IRL is about learning from humans. Inverse reinforcement learning is the field of learning an agent’s objectives, values, or rewards by observing its behavior.

Before getting into further details of IRL, let us recap RL.
Reinforcement learning is an area of Machine Learning (ML) that takes suitable actions to maximize rewards. The goal of reinforcement learning algorithms is to find the best possible action to take in a specific situation.

Challenges in RL

One of the hardest challenges in many reinforcement learning tasks is that it is often difficult to find a good reward function which is both learnable (i.e. rewards happen early and often enough) and correct (i.e. leads to the desired outcomes). Inverse reinforcement learning aims to deal with this problem by learning a reward function based on observations of expert behavior.

What distinguishes Inverse Reinforcement Learning from Reinforcement Learning?

In RL, our agent is provided with a reward function which, whenever it executes an action in some state, provides feedback about the agent’s performance. This reward function is used to obtain an optimal policy, one where the expected future reward (discounted by how far away it will occur) is maximal.

In IRL, the setting is (as the name suggests) inverse. We are now given some agent’s policy or a history of behavior and we try to find a reward function that explains the given behavior. Under the assumption that our agent acted optimally, i.e. always picks the best possible action for its reward function, we try to estimate a reward function that could have led to this behavior.

The biggest motivation for IRL

Maybe the biggest motivation for IRL is that it is often immensely difficult to manually specify a reward function for a task. So far, RL has been successfully applied in domains where the reward function is very clear. But in the real world, it is often not clear at all what the reward should be and there are rarely intrinsic reward signals such as a game score.

For example, consider we want to design an artificial intelligence for a self-driving car. A simple approach would be to create a reward function that captures the desired behavior of a driver, like stopping at red lights, staying off the sidewalk, avoiding pedestrians, and so on. In real life, this would require an exhaustive list of every behavior we’d want to consider, as well as a list of weights describing how important each behavior is.

Instead, in the IRL framework, the task is to take a set of human-generated driving data and extract an approximation of that human’s reward function for the task. Of course, this approximation necessarily deals with a simplified model of driving. Still, much of the information necessary for solving a problem is captured within the approximation of the true reward function. Since it quantifies how good or bad certain actions are. Once we have the right reward function, the problem is reduced to finding the right policy and can be solved with standard reinforcement learning methods.

For our self-driving car example, we’d be using human driving data to automatically learn the right feature weights for the reward. Since the task is described completely by the reward function, we do not even need to know the specifics of the human policy, so long as we have the right reward function to optimize. In the general case, algorithms that solve the IRL problem can be seen as a method for leveraging expert knowledge to convert a task description into a compact reward function.

Conclusion

The foundational methods of inverse reinforcement learning can achieve their results by leveraging information obtained from a policy executed by a human expert. However, in the long run, the goal is for machine learning systems to learn from a wide range of human data and perform tasks that are beyond the abilities of human experts.

References

About the Author

Naresh is a part of Location Zero at GAVS as an AI/ML solutions developer. His focus is on solving problems leveraging AI/ML. He strongly believes in making success as an habit rather than considering it a destination. In his free time, he likes to spend time with his pet dogs and likes sketching and gardening.

Automating IT ecosystems with ZIF Remediate

Posted on by ZIF
Alwinking N Rajamani

Alwinking N Rajamani


Zero Incident FrameworkTM (ZIF) is an AIOps based TechOps platform that enables proactive detection and remediation of incidents helping organizations drive towards a Zero Incident Enterprise™. ZIF comprises of 5 modules, as outlined below.

This article’s focus is on the Remediate function of ZIF. Most ITSM teams envision a future of ticketless ITSM, driven by AI and Automation.

Remediate being a key module ofZIF, has more than 500+ connectors to various ITSMtools, Monitoring, Security and Incident management tools, storage/backup tools and others.Few of the connectors are referenced below that enables quick automation building.

Key Features of Remediate

  • Truly Agent-less software.
  • 300+ readily available templates – intuitive workflow/activity-based tool for process automation from a rich repository of pre-coded activities/templates.
  • No coding or programming required to create/deploy automated workflows. Easy drag & drop to sequence activities for workflow design.
  • Workflow execution scheduling for pre-determined time or triggering from events/notifications via email or SMS alerts.
  • Can be installed on-premise or on the cloud, on physical or virtual servers
  • Self Service portal for end-users/admins/help-desk to handle tasks &remediation automatically
  • Fully automated service management life cycle from incident creation to resolution and automatic closure
  • Has integration packs for all leading ITSM tools

Key features for futuristic Automation Solutions

Although the COVID pandemic has landed us in unprecedented times, we have been able to continue supporting our customers and enabled their IT operations with ZIF Remediate.

  • Self-learning capability to deliver Predictive/Prescriptive actionable alerts.
  • Access to multiple data sources and types – events, metrics, thresholds, logs, event triggers e.g. mail or SMS.
  • Support for a wide range of automation
    • Interactive Automation – Web, SMS, and email
    • Non-interactive automation – Silent based on events/trigger points
  • Supporting a wide range of advanced heuristics.

Benefits of AIOPS driven Automation

  • Faster MTTR
  • Instant identification of threats and appropriate responses
  • Faster delivery of IT services
  • Quality services leading to Employee and Customer satisfaction
  • Fulfillment and Alignment of IT services to business performance

Interactive and Non-interactive automation

Through our automation journey so far, we have understood that the best automation empowers humans, rather than replacing them. By implementing ZIF Remediate, organizations can empower their people to focus their attention on critical thinking and value-added activities and let our platform handle mundane tasks by bringing data-driven insights for decision making.

  • Interactive Automation – Web portal, Chatbot and SMS based
  • Non-interactive automations – Event or trigger driven automation

Involved decision driven Automations

ZIF Remediate has its unique, interactive automation capabilities, where many automation tools do not allow interactive decision making. Need approvals built into an automated change management process that involves sensitive aspects of your environment? Need numerous decision points that demand expert approval or oversight? We have the solution for you. Take an example of Phishing automation, here a domain or IP is blocked based on insights derived by mimicking an SOC engineer’s actions – parsing the observables i.e. URL, suspicious links or attachments in a phish mail and have those observables validated for threat against threat response tools, virus total, and others.

Some of the key benefits realized by our customers which include one of the largest manufacturing organizations, a financial services company, a large PR firm, health care organizations, and others.

  • Reduction of MTTR by 30% across various service requests.
  • Reduction of 40% of incidents/tickets, thus enabling productivity improvements.
  • Ticket triaging process automation resulting in a reduction of time taken by 50%.
  • Reclaiming TBs of storage space every week through snapshot monitoring and approval-driven model for a large virtualized environment.
  • Eliminating manual threat analysis by Phishing Automation, leading to man-hours being redirected towards more critical work.
  • Reduction of potential P1 outages by 40% through self-healing automations.

For more detailed information on ZIF Remediate, or to request a demo please visit https://zif.ai/products/remediate/

About the Author:

Alwin leads the Product Engineering for ZIF Remediate and zIrrus. He has over 20 years of IT experience spanning across Program & Portfolio Management for large customer accounts of various business verticals.

In his free time, Alwin loves going for long drives, travelling to scenic locales, doing social work and reading & meditating the Bible.

Assess Your Organization’s Maturity in Adopting AIOps

Posted on by ZIF

Artificial Intelligence for IT operations (AIOps) is adopted by organizations to deliver tangible Business Outcomes. These business outcomes have a direct impact on companies’ revenue and customer satisfaction.

A survey from AIOps Exchange 2019, reports that 84% of Business Owners who attended the survey, confirmed that they are actively evaluating AIOps to be adopted in their organizations.

So, is AIOps just automation? Absolutely NOT!!

Artificial Intelligence for IT operations implies the implementation of true Autonomous Artificial Intelligence in ITOps, which needs to be adopted as an organization-wide strategy. Organizations will have to assess their existing landscape, processes, and decide where to start. That is the only way to achieve the true implementation of AIOps.

Every organization trying to evaluate AIOps as a strategy should read through this article to understand their current maturity, and then move forward to reach the pinnacle of Artificial Intelligence in IT Operations.

The primary Success Factor in adopting AIOps is derived from the Business Outcomes the organization is trying to achieve by implementing AIOps –that is the only way to calculate ROI.

There are 4 levels of Maturity in AIOps adoption. Based on our experience in developing an AIOps platform and implementing the platform across multiple industries, we have arrived at these 4 levels. Assessing an organization against each of these levels helps in achieving the goal of TRUE Artificial Intelligence in IT Operations.

Level 1: Knee-jerk

Events, logs are generated in silos and collected from various applications and devices in the infrastructure. These are used to generate alerts that are commissioned to command centres to escalate as per the SOPs (standard operating procedures) defined. The engineering teams work in silos, not aware of the business impact that these alerts could potentially create. Here, operations are very reactive which could cost the organization millions of dollars.

Level 2: Unified

Have integrated all events, logs, and alerts into one central locale. ITSM process has been unified. This helps in breaking silos and engineering teams are better prepared to tackle business impacts. SOPs have been adjusted since the process is unified, but this is still reactive incident management.

Level 3: Intelligent

Machine Learning algorithms (either supervised or unsupervised) have been implemented on the unified data to derive insights. There are baseline metrics that are calibrated and will be used as a reference for future events. With more data, the metrics get richer. IT operations team can correlate incidents/events with business impacts by leveraging AI & ML. If Mean Time To Resolve (MTTR) an incident has been reduced by automated identification of the root cause, then the organization has attained level 3 maturity in AIOps.

Level 4: Predictive & Autonomous

The pinnacle of AIOps is level 4. If incidents and performance degradation of applications can be predicted by leveraging Artificial Intelligence, it implies improved application availability. Autonomousremediation bots can be triggered spontaneously based on the predictive insights, to fix incidents that are prone to happen in the enterprise. Level 4 is a paradigm shift in IT operations – moving operations entirely from being reactive, to becoming proactive.

Conclusion:

As IT operations teams move up each level, the essential goal to keep in mind is the long-term strategy that needs to be attained by adopting AIOps. Artificial Intelligence has matured over the past few decades, and it is up to AIOps platforms to embrace it effectively. While choosing an AIOps platform, measure the maturity of the platform’s artificial intelligent coefficient.

About the Author:

Anoop Aravindakshan (Principal Consultant Manager) at GAVS Technologies.


An evangelist of Zero Incident FrameworkTM, Anoop has been a part of the product engineering team for long and has recently forayed into product marketing. He has over 14 years of experience in Information Technology across various verticals, which include Banking, Healthcare, Aerospace, Manufacturing, CRM, Gaming, and Mobile.

Modern IT Infrastructure

Posted on by ZIF

Infrastructure today has grown beyond the physical confines of the traditional data center, has spread its wings to the cloud, and is increasingly distributed, virtual, and abstract. With the cloud gaining wide acceptance, most enterprises have their workloads spread across data centers, colocations, multi-cloud, and edge locations. On-premise infrastructure is also being replaced by Hyperconverged Infrastructure (HCI) where software-defined, virtualized compute, storage, and network are in one single system, greatly simplifying IT operations. Infrastructure is also becoming increasingly elastic, scales & shrinks on demand and doesn’t have to be provisioned upfront.

Let’s look at a few interesting technologies that are steering the modern IT landscape.

Containers and Serverless

Traditional application deployment on physical servers comes with the overhead of managing the infrastructure, middleware, development tools, and everything in between. Application developers would rather have this grunt work be handled by someone else, so they could focus on just their applications. This is where containers and serverless technologies come into picture. Both are cloud-based offerings and provide different levels of abstraction, in a way that hides layers beyond the front end, from the developer. They typically deploy smaller components of monolithic applications, microservices, and functions.

A Container is like an all-in-one-box, containing the app, and all its dependencies like libraries, executables & config files. The containerized application is highly portable, will run anywhere the container runtime is installed, and behave the same regardless of the OS or hardware it is deployed on. Containers give developers great flexibility and control since they cater to specific application requirements like the OS, S/W versions. The flip side is that there is still a need for manual maintenance of the runtime environment, like security patches, software updates, etc. Secondly, the flexibility it affords translates into high operational costs, since it lacks agility in scaling.

Serverless technologies provide much greater abstraction of the OS and infrastructure. ‘Serverless’ though, does not imply that there are no servers, it just means application developers do not have to worry about the underlying OS, the server environment, or the infra that their applications will be deployed on. Serverless is event-driven and is based on the premise that the application is split into functions that get executed based on events. The developer only needs to deploy function code and define the event(s) that will trigger them! The rest of the magic is done by the cloud service provider (with the help of third parties). 

The biggest advantage of serverless is that consumers are billed only for the running time of the function instances or the number of times the function gets executed, depending on the provider. Since it has zero administrative overhead, it guarantees rapid iterative deployment and faster time to market. Since the architecture is intrinsically auto-scaling, it is a perfect fit for applications with undefinable usage patterns. The other side of the coin is that developers need to deal with a black box back-end environment, so, holistic testing, debugging of the application becomes a challenge. Vendor lock-in is a real problem since the consumer is restricted by the technology stack supported by the vendor. Since serverless best practices dictate light, isolated functions with limited scope, building complex applications can get difficult. Function as a Service (FaaS) is a subset of serverless computing.

Internet of Things (IoT)

IoT is about connecting everyday things – beyond just computing devices or smartphones – to the internet. It is possible to convert practically anything into an IoT device, with a computer chip installation & internet access, and have it communicate independently with the internet – without any human intervention. But why would we want everyday things like for instance a watch or a light bulb, to become IoT devices? It’s in a bid to bridge the chasm between the physical and digital worlds and make the environment around us more intelligent, communicative, and responsive to our needs.

IoT’s use cases are just about everywhere; in personal devices, self-driving cars, smart homes, smart workspaces, smart cities, and industries across all verticals. For instance, live data from sensors in products while in use, gives good visibility into their operations on the ground, helps remediate issues proactively & aids improvements in design/manufacturing processes.

The Industrial Internet of Things (IIoT) is the use of IoT data in business, in tandem with Big Data, AI, Analytics, Cloud, and High-speed networks, with the primary goal of finding efficient business models to improve productivity & optimize expenditure. The need for real-time response to sensor data and advanced analytics to power insights has increased the demand for 5G networks for speed, cloud technologies for storage and computing, edge computing to reduce latency, and hyper-scale data centers for rapid scaling.

With IoT devices extending an organization’s infrastructure landscape, and the likelihood that IT staff may not even be aware of all the IoT devices in it is a security nightmare that could open corporate networks & sensitive data for attacks. Global standards and regulations for IoT device security are in the works. Until then, it is up to the enterprise security team to safeguard against IoT-related vulnerabilities.

Hyperscaling

The ability of infrastructure to rapidly scale out on a massive level is called hyperscaling.

Unprecedented needs for high-power computing and on-demand massive scalability has given rise to a new breed of hyperscale computing architectures, where traditional elements are replaced by hyper-converged, software-defined infrastructure with a high degree of virtualization. These hyperscale environments are characterized by high-density server racks, with software designed and specifically built for scale-out environments. Since high-density implies heavy power consumption, heating problems need to be handled by specialized cooling solutions like liquid cooling. Hyperscale data centre operators usually look for renewable energy options to save on power & cooling.

Today, there are several hundred hyperscale data centers in the world, with the dominant players being Microsoft, Google, Apple, Amazon & Facebook.

Edge Computing

Edge computing as the name indicates means moving data processing away from distant servers or the cloud, closer to the source of data.  This is to reduce latency and network bandwidth used for back & forth communication between the data source and the server. Edge, also called the network edge refers to where the data source connects to the internet. The explosive growth of IoT and applications like self-driving cars, virtual reality, smart cities for instance, that require real-time computing and analytics are paving the way for edge computing. Most cloud providers now provide geographically distributed edge servers. As with IoT devices, data at the edge can be a ticking security time bomb necessitating appropriate security mechanisms.

The evolution of IT technologies continuously raises the bar for the IT team. IT personnel have been forced to move beyond legacy practices and mindsets & constantly up-skill themselves to be able to ride the wave. For customers pampered by sophisticated technologies, round the clock availability of systems and immersive experiences have become baseline expectations. With more & more digitalization, there is increasing reliance on IT infrastructure and hence lesser tolerance for outages. The responsibilities of maintaining a high-performing IT infrastructure with near-zero downtime fall on the shoulders of the IT operations team.

This has underscored the importance of AI in IT operations since IT needs have now surpassed human capabilities. Gavs’ AI-powered Platform for IT operations, ZIF, caters to the entire ITOps spectrum, right from automated discovery of the landscape, monitoring, to predictive and prescriptive analytics that proactively drive the organization towards zero incidents. For more details, please visit https://zif.ai

About the Author:

Padmapriya Sridhar

Priya is part of the Marketing team at GAVS. She is passionate about Technology, Indian Classical Arts, Travel, and Yoga. She aspires to become a Yoga Instructor someday!

Prediction for Business Service Assurance

Posted on by ZIF

Artificial Intelligence for IT operations or AIOps has exploded over the past few years. As more and more enterprises set about their digital transformation journeys, AIOps becomes imperative to keep their businesses running smoothly. 

AIOps uses several technologies like Machine Learning and Big Data to automate the identification and resolution of common Information Technology (IT) problems. The systems, services, and applications in a large enterprise produce volumes of log and performance data. AIOps uses this data to monitor the assets and gain visibility into the behaviour and dependencies among these assets.

According to a Gartner publication, the adoption of AIOps by large enterprises would rise to 30% by 2023.

ZIF – The ideal AIOps platform of choice

Zero Incident FrameworkTM (ZIF) is an AIOps based TechOps platform that enables proactive detection and remediation of incidents helping organizations drive towards a Zero Incident Enterprise™.

ZIF comprises of 5 modules, as outlined below.

At the heart of ZIF, lies its Analyze and Predict (A&P) modules which are powered by Artificial Intelligence and Machine Learning techniques. From the business perspective, the primary goal of A&P would be 100% availability of applications and business processes.

Let us understand more about thePredict module of ZIF.

Predictive Analytics is one of the main USP of the ZIF platform. ZIF encompassesSupervised, Unsupervised and Reinforcement Learning algorithms for realization of various business use cases (as shown below).

How does the Predict Module of ZIF work?

Through its data ingestion capabilities, the ZIF platform can receive and process all types of data (both structured and unstructured) from various tools in the enterprise. The types of data can be related to alerts, events, logs, performance of devices, relations of devices, workload topologies, network topologies etc. By analyzing all these data, the platform predicts the anomalies that can occur in the environment. These anomalies get presented as ‘Opportunity Cards’ so that suitable action can be taken ahead of time to eliminate any undesired incidents from occurring. Since this is ‘Proactive’ and not ‘Reactive’, it brings about a paradigm shift to any organization’s endeavour to achieve 100% availability of their enterprise systems and platforms. Predictions are done at multiple levels – application level, business process level, device level etc.

Sub-functions of Prediction Module

How does the Predict module manifest to enterprise users of the platform?

Predict module categorizes the opportunity cards into three swim lanes.

  1. Warning swim lane – Opportunity Cards that have an “Expected Time of Impact” (ETI) beyond 60 minutes.
  2. Critical swim lane – Opportunity Cards that have an ETI within 60 minutes.
  3. Processed / Lost– Opportunity Cards that have been processed or lost without taking any action.

Few of the enterprises that realized the power of ZIF’s Prediction Module

  • A manufacturing giant in the US
  • A large non-profit mental health and social service provider in New York
  • A large mortgage loan service provider in the US
  • Two of the largest private sector banks in India

For more detailed information on GAVS’ Analyze, or to request a demo please visithttps://zif.ai/products/predict/

References:https://www.gartner.com/smarterwithgartner/how-to-get-started-with-aiops/

About the Author:

Vasudevan Gopalan

Vasu heads Engineering function for A&P. He is a Digital Transformation leader with ~20 years of IT industry experience spanning across Product Engineering, Portfolio Delivery, Large Program Management etc. Vasu has designed and delivered Open Systems, Core Banking, Web / Mobile Applications etc.

Outside of his professional role, Vasu enjoys playing badminton and focusses on fitness routines.

Discover, Monitor, Analyze & Predict COVID-19

Posted on by ZIF

“Uber, the world’s largest taxi company, owns no vehicles. Facebook, the world’s most popular media owner, creates no content. Alibaba, the most valuable retailer, has no inventory. Netflix, the world’s largest movie house, own no cinemas. And Airbnb, the world’s largest accommodation provider, owns no real estate. Something interesting is happening.”

– Tom Goodwin, an executive at the French media group Havas.

This new breed of companies is the fastest growing in history because they own the customer interface layer. It is the platform where all the value and profit is. “Platform business” is a more wholesome termfor this model for which data is the fuel; Big Data & AI/ML technologies are the harbinger of new waves of productivity growth and innovation.

With Big data and AI/ML is making a big difference in the area of public health, let’s see how it is helping us tackle the global emergency of coronavirus formally known as COVID-19.

“With rapidly spreading disease, a two-week lag is an eternity.”

DISCOVERING/ DETECTING

Chinese technology giant Alibaba has developed an AI system for detecting the COVID-19 in CT scans of patients’ chests with 96% accuracy against viral pneumonia cases. It only takes 20 seconds for the AI to decide, whereas humans generally take about 15 minutes to diagnose the illness as there can be upwards of 300 images to evaluate.The system was trained on images and data from 5,000 confirmed coronavirus cases and has been tested in hospitals throughout China. Per a report, at least 100 healthcare facilities are currently employing Alibaba’s AI to detect COVID-19.

Ping An Insurance (Group) Company of China, Ltd (Ping An) aims to address the issue of lack of radiologists by introducing the COVID-19 smart image-reading system. This image-reading system can read the huge volumes of CT scans in epidemic areas.

Ping An Smart Healthcare uses clinical data to train the AI model of the COVID-19 smart image-reading system. The AI analysis engine conducts a comparative analysis of multiple CT scan images of the same patient and measures the changes in lesions. It helps in tracking the development of the disease, evaluation of the treatment and in prognosis of patients.Ultimately it assists doctors to diagnose, triage and evaluate COVID-19 patients swiftly and effectively.

Ping An Smart Healthcare’s COVID-19 smart image-reading system also supports AI image-reading remotely by medical professionals outside the epidemic areas.Since its launch, the smart image-reading system has provided services to more than 1,500 medical institutions. More than 5,000 patients have received smart image-reading services for free.

The more solutions the better. At least when it comes to helping overwhelmed doctors provide better diagnoses and, thus, better outcomes.

MONITORING

  • AI based Temperature monitoring & scanning

In Beijing, China, subway passengers are being screened for symptoms of coronavirus, but not by health authorities. Instead, artificial intelligence is in-charge.

Two Chinese AI giants, Megvii and Baidu, have introduced temperature-scanning. They have implemented scanners to detect body temperature and send alerts to company workers if a person’s body temperature is high enough to constitute a fever.

Megvii’s AI system detects body temperatures for up to 15 people per second andup to 16 feet. It monitors as many as 16 checkpoints in a single station. The system integrates body detection, face detection, and dual sensing via infrared cameras and visible light. The system can accurately detect and flag high body temperature even when people are wearing masks, hats, or covering their faces with other items. Megvii’s system also sends alerts to an on-site staff member.

Baidu, one of the largest search-engine companies in China, screens subway passengers at the Qinghe station with infrared scanners. It also uses a facial-recognition system, taking photographs of passengers’ faces. If the Baidu system detects a body temperature of at least 99-degrees Fahrenheit, it sends an alert to the staff member for another screening. The technology can scan the temperatures of more than 200 people per minute.

  • AI based Social Media Monitoring

An international team is using machine learning to scour through social media posts, news reports, data from official public health channels, and information supplied by doctors for warning signs of the virus across geographies.The program is looking for social media posts that mention specific symptoms, like respiratory problems and fever, from a geographic area where doctors have reported potential cases. Natural language processing is used to parse the text posted on social media, for example, to distinguish between someone discussing the news and someone complaining about how they feel.

The approach has proven capable of spotting a coronavirus needle in a haystack of big data. This technique could help experts learn how the virus behaves. It may be possible to determine the age, gender, and location of those most at risk quicker than using official medical sources.

PREDICTING

Data from hospitals, airports, and other public locations are being used to predict disease spread and risk. Hospitals can also use the data to plan for the impact of an outbreak on their operations.

Kalman Filter

Kalman filter was pioneered by Rudolf Emil Kalman in 1960, originally designed and developed to solve the navigation problem in the Apollo Project. Since then, it has been applied to numerous cases such as guidance, navigation, and control of vehicles, computer vision’s object tracking, trajectory optimization, time series analysis in signal processing, econometrics and more.

Kalman filter is a recursive algorithm which uses time-series measurement over time, containing statistical noise and produce estimations of unknown variables.

For the one-day prediction Kalman filter can be used, while for the long-term forecast a linear model is used where its main features are Kalman predictors, infected rate relative to population, time-depended features, and weather history and forecasting.

The one-day Kalman prediction is very accurate and powerful while a longer period prediction is more challenging but provides a future trend.Long term prediction does not guarantee full accuracy but provides a fair estimation following the recent trend. The model should re-run daily to gain better results.

GitHub Link: https://github.com/Rank23/COVID19

ANALYZING

The Center for Systems Science and Engineering at Johns Hopkins University has developed an interactive, web-based dashboard that tracks the status of COVID-19 around the world. The resource provides a visualization of the location and number of confirmed COVID-19 cases, deaths and recoveries for all affected countries.

The primary data source for the tool is DXY, a Chinese platform that aggregates local media and government reports to provide COVID-19 cumulative case totals in near real-time at the province level in China and country level otherwise. Additional data comes from Twitter feeds, online news services and direct communication sent through the dashboard. Johns Hopkins then confirms the case numbers with regional and local health departments. This kind of Data analytics platform plays a pivotal role in addressing the coronavirus outbreak.

All data from the dashboard is also freely available in the following GitHub repository.

GitHub Link:https://bit.ly/2Wmmbp8

Mobile version: https://bit.ly/2WjyK4d

Web version: https://bit.ly/2xLyT6v

Conclusion

One of AI’s core strengths when working on identifying and limiting the effects of virus outbreaks is its incredibly insistent nature. AIsystems never tire, can sift through enormous amounts of data, and identify possible correlations and causations that humans can’t.

However, there are limits to AI’s ability to both identify virus outbreaks and predict how they will spread. Perhaps the best-known example comes from the neighboring field of big data analytics. At its launch, Google Flu Trends was heralded as a great leap forward in relation to identifying and estimating the spread of the flu—until it underestimated the 2013 flu season by a whopping 140 percent and was quietly put to rest.Poor data quality was identified as one of the main reasons Google Flu Trends failed. Unreliable or faulty data can wreak havoc on the prediction power of AI.

References:

About the Author:

Bargunan Somasundaram

Bargunan Somasundaram

Bargunan is a Big Data Engineer and a programming enthusiast. His passion is to share his knowledge by writing his experiences about them. He believes “Gaining knowledge is the first step to wisdom and sharing it is the first step to humanity.”