Ensure Service Availability and Reliability with ZIF

To survive in the current climate, most enterprises have already embarked on their digital transformation journeys. This is leading to uncertainty in the way applications and services supporting the applications are being monitored and managed. Inadequate information is leading to downtime in service availability for end-users eventually resulting in unhappy users and revenue loss.

Zero Incident Framework™ has been architected to address the IT Ops issues of today and tomorrow.

Leveraging the power of Artificial Intelligence on telemetry data ingested in real-time, ZIF can provide insights and resolve forecasted issues – resulting in the availability of application service when end-user wants the service at the right time.

Business Value delivered to customers from ZIF

  • Minimum 40% reduction in capital expenses and a minimum 50% reduction in IT operational cost
  • Faster resolution by 60% (MTTR)
  • Service availability of 99.99%
  • ZIF bots to increase productivity by a minimum of 80%
  • Increased user experience measured by metrics (UEI) User Experience Index
AI in operations management service

ICEBERG STATE IN ITOps

Many IT operations are in an ‘ICEBERG’ state even today. Do not be surprised if your organization is also one of them. Issues and incidents that surfaces to the top are the ones that are known to the team. But the unknown issues are not uncovered.

Therefore, enterprises have started to embark on artificial intelligence to help them identify and track the unknown issues within the complex IT landscape.

OBSERVABILITY USING ZIF

ZIF, architected and developed on the premise of observability, not only helps with visibility but also enables discovering deeper insights, thus freeing up more time for more strategic initiatives. This becomes critical to the overall success of Site Reliability Engineering (SRE) in enterprises.

Externalizing the internal state of systems, services, and application to the maximum, helps in complete observability.

Monitoring Vs. Observability?

automated discovery of networked services

Pillars of Observability – Events | Metrics | Traces

Ensure SERVICE RELIABILITY

“Reliability is defined as the probability that an application, system, or service will perform its intended function adequately for a specified period or will operate in a defined environment without failure.”

ZIF has mastered the art of predicting device, application & service failure, or performance degradation. This unique proposition from ZIF gives IT engineers the edge on service reliability of all applications, systems, or services that they are responsible for. ZIF’s auto-remediation bots can resolve predicted issues to make sure the intended function performs as and when expected by users.

SERVICE AVAILABILITY

Availability is measured as the percentage of time your service or system or application is available.

A small variation in availability percentage will have to be addressed on priority. A 99.999% availability allows only 5.26 minutes of downtime a year, whereas 99% availability allows downtime of 3.65 days a year.

ZIF helps IT engineers achieve the agreed-upon availability of application or system by learning the usage of the system and application from the metrics that are collected from the environment. Collecting the right metrics helps in getting the right availability. With the help of unsupervised algorithms, patterns are learned which helps in discovering when the application or system is required the most and then predicting any potential downtime. With above 95% accuracy in prediction, ZIF can achieve 99.99% availability for application and devices which allows 52.56 minutes downtime a year.

ZIF’s goal has always been to deliver the right business outcomes for the stakeholders. Users have the privilege to choose what business outcomes are expected from the platform and the respective features are deployed in the enterprise to deliver the chosen outcome.

About the Author

Anoop Aravindakshan

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.

Is AR the Future of our Increasingly Digital World?

Imagine a device which, when used to look at something, throws up information on whatever you’re pointing at. Menus for restaurants, dates of establishment for institutes, and so on. These are the sort of possibilities afforded by Augmented Reality (AR).

What is AR?

AR is a science fiction idea that successfully made the transition to reality. The fundamental idea behind augmented reality is to add something extra to your experience of reality. So, if you are watching a movie or playing a game, AR adds to that experience in some way or form to turn the experience immersive and interactive. AR basically superimposes computer-generated information (audio, visual, haptic, etc.) on the real-world objects.

AR can be defined as a system that fulfills three basic features: a combination of real and virtual worlds, real-time interaction, and accurate 3D registration of virtual and real objects.

How does AR work?

A camera-equipped device is essential for an AR experience. Upon pointing the device at an object, computer vision technology is used to recognize it. The device then downloads information about the object from the cloud, in much the same way that a web browser loads a page via a URL. In this case, the information is presented in a 3-D experience.

ai automated root cause analysis solution

AR can provide a view of the real-time data flowing from products and allow users to control them by touchscreen, voice, or gesture. An operator using an AR headset to interact with an industrial robot might see superimposed data about the robot’s performance and gain access to its controls.

The size and orientation of objects viewed through the AR display adjusts/changes in real-time. New graphical or text information comes into view while other information passes out of view as the user moves about. In industrial settings, users in different roles, such as a machine operator and a maintenance technician, can look at the same object but be presented with different AR experiences that are tailored to their needs.

AR – a novel way of shopping

A 2018 Gartner report stated, “By 2020, 100 million consumers will shop in AR online and in-store.” The current global pandemic has put a damper on consumer sentiments worldwide and we may not see those numbers. But AR can help make the in-store shopping experience more secure by reducing the need to touch a lot of objects and surfaces.

Brands like American Apparel, Uniqlo, and Lacoste already have showrooms and fitting rooms that provide try-before-you-buy options in AR spaces. Smart mirror technologies that scan RFID tags also offer the ability to bring recommendations to the brick-and-mortar shopping experience.

ai data analytics monitoring tools

IKEA customers have access to an app that permits them to point their phones at spaces and see what different products would look like in their own homes.

In the current global climate, fashion and lifestyle brands stand to gain from technologies that handle facial recognition, adapt to local lighting conditions, and provide personalized recommendations.

ai devops platform management services

According to a BRP report, 48% consumers said they would be more inclined to buy from a retailer that provided AR experiences. Retailers may be able to attract more customers with an immersive and secure shopping experience in a post-Corona world.

35% of sales on Amazon are derived from its recommendation engine, which is powered by Machine Learning. Leveraging this in the real world also has immense commercial potential.

AR for Navigation Solutions

Map services from Google and Apple have already found mass acceptance, indoor navigation is next. Apps based on ARKit and ARCore can enable navigating inside spaces like airports, malls, hospitals, etc. Gatwick Airport has already deployed its own smartphone solution that provides routes to terminals and gates based on a user’s flight number.

In 2019, a beta version of AR walking directions feature was launched for Google Maps for all AR-compatible iOS and Android mobile devices. You could view information about your surroundings by pointing your phone’s camera towards it.

AR in Automotive Industry

AR can be used in a breadth of ways in the automotive industry. Starting with dashboard-mounted heads-up displays to interactive experiences in showrooms and more.

AR is also employed by some carmakers to help aid in car maintenance (Volkswagen’s Marta app) and car manufacturing and selling processes (Volvo’s project with Microsoft HoloLens).

The heads-up display is one of the most popular uses of AR in this industry. Not only can drivers get directions and alerts on hazards, but also information on landmarks and nearby locations.

Hyundai has been a leader in AR research that goes beyond the cockpit-style view of the motorist’s experience. They have reimagined maintenance manuals with AR and has apps to point their phones at their cars to get information. Mercedes has a similar app, but its version adds a chatbot to provide virtual assistance.

AR in Healthcare

Applications of AR is opening up new opportunities in the healthcare industry. It’s expected that the global market will reach a value of $1.5B. By enabling healthcare workers with real-time data and patient information, AR can aid in more accurate diagnoses and more precise surgeries.

application performance management solutions

AR can also bring huge value to practicing medicine and education by allowing students and trainee physicians to better visualize health issues and scenarios that they one day will be treating. The benefit that AR can bring to the healthcare industry can be ground-breaking and we are just witnessing the beginning of what is to come from AR in the field of medicine.

AR-powered Solutions for Enterprises

Smart glasses are quickly gaining popularity. Military, medical and enterprise solutions, however, are beginning to prove the value of combining AR with headsets and smart glasses.

Microsoft HoloLens 2 was likely the most anticipated product in this space in 2019. The company hopes to roll out its technology to great fanfare by demonstrating improvements in raw processing power, battery life, and wear ability. The U.S. Army has awarded a $480 contract to Microsoft, and they are also working with the industrial IoT firm PTC to streamline the development of both augmented and mixed reality products.

applications of predictive analytics in business

Walmart and Tyson are testing programs that will transition traditional training methods into mixed reality (MR) settings. This will bring about new ways to learn about compliance and safety issues by looking around mixed-reality environments and identifying problems in a way that’s practical and engaging. Integration with other recent workplace training trends, especially gamification, may compound the returns that AR and MR solutions generate. Per ABI Research, AR-based training in enterprise will be a $6 billion industry by 2022.

Improvements in prototyping, testing, troubleshooting, and quality control are expected to emerge from this trend, too, as workers will be able to make on-the-fly comparisons of real-world items against available documentation and specifications. Jobs that call for workers’ hands to be free will also benefit significantly from AR headsets and glasses.

Augmented reality is the next ‘BIG THING’, it will absolutely revolutionize almost every aspect of life. Everything from medicine to education to construction to entertainment. AR application has already started to appear on the world’s laptops, tablets, and smartphones.

References

https://www.sciencedirect.com/topics/computer-science/augmented-reality

https://www.mantralabsglobal.com/blog/disruptive-augmented-reality-use-cases/

https://www.vxchnge.com/blog/augmented-reality-statistics

About the Author

Kalpana Vijayakumar

Kalpana is a database developer. She strongly believes that “It’s not that we use technology, we live technology.”
Outside of her professional role, Kalpana is passionate about travelling and watching movies.

Algorithmic Alert Correlation

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

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.

Automating IT ecosystems with ZIF Remediate

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

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

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!

GAVS’ commitment during COVID-19

MARCH 23. 2020

Dear Client leaders & Partners,

I do hope all of you, your family and colleagues are keeping good health, as we are wading through this existential crisis of COVID 19.

This is the time for shared vulnerabilities and in all humility, we want to thank you for your business and continued trust. For us, the well being of our employees and the continuity of clients’ operations are our key focus. 

I am especially inspired by my GAVS colleagues who are supporting some of the healthcare providers in NYC. The GAVS leaders truly believe that they are integral members of these  institutions and it is incumbent upon them to support our Healthcare clients during these trying times.

We would like to confirm that 100% of our client operations are continuing without any interruptions and 100% of our offshore employees are successfully executing their responsibilities remotely using GAVS ZDesk, Skype, collaborating through online Azure ALM Agile Portal. GAVS ZIF customers are 100% supported 24X7 through ROTA schedule & fall back mechanism as a backup.

Most of GAVS Customer Success Managers, Client Representative Leaders, and Corporate Leaders have reached out to you with GAVS Business Continuity Plan and the approach that we have adopted to address the present crisis. We have put communication, governance, and rigor in place for client support and monitoring.  

GAVS is also reaching out to communities and hospitals as a part of our Corporate Social Responsibility.  

We have got some approvals from the local Chennai police authorities in Chennai to support the movement of our leaders from and to the GAVS facility and we have, through US India Strategic Partnership Forum applied for GAVS to be considered an Essential Service Provider in India.  

I have always maintained that GAVS is an IT Service concierge to all of our clients and we individually as leaders and members of GAVS are committed to our clients. We shall also ensure that our employees are safe. 

Thank you, 

Sumit Ganguli
GAVS Technologies


Heroes of GAVS | BronxCare

gavs

“Every day we witness these heroic acts: one example out of many this week was our own Kishore going into our ICU to move a computer without full PPE (we have a PPE shortage). The GAVS technicians who come into our hospital every day are, like our doctors and healthcare workers,  the true heroes of our time.” – Ivan Durbak, CIO, BronxCare

“I am especially inspired by my GAVS colleagues who are supporting some of the healthcare providers in NYC. The GAVS leaders truly believe that they are integral members of these institutions and it is incumbent upon them to support our Healthcare clients during these trying times. We thank the Doctors, Nurses and Medical Professionals of Bronx Care and we are privileged to be associated with them. We would like to confirm that 100% of our client operations are continuing without any interruptions and 100% of our offshore employees are successfully executing their responsibilities remotely using GAVS ZDesk, and other tools.” – Sumit Ganguli, CEO

The Hands that rock the cradle, also crack the code

It was an unguarded moment for my church-going, straight-laced handyman & landscaper, “ I am not sure if I am ready to trust a woman leader”, and finally the loss of first woman Presidential candidate in the US, that led me to ruminate about Women and Leadership and indulge in my most “ time suck” activities, google and peruse through Wikipedia.

I had known about this, but I was fascinated to reconfirm that the first programmer in the world was a woman, and daughter of the famed poet, Lord Byron, no less. The first Programmer in the World, Augusta Ada King-Noel, Countess of Lovelace nee Byron; was born in 1815 and was the only legitimate child of the poet laureate, Lord Byron and his wife Annabella. A month after Ada was born, Byron separated from his wife and forever left England. Ada’s mother remained bitter towards Lord Byron and promoted Ada’s interest in mathematics and logic in an effort to prevent her from developing what she saw as the insanity seen in her father.

Ada grew up being trained and tutored by famous mathematicians and scientists. She established a relationship with various scientists and authors, like Charles Dickens, etc..   Ada described her approach as “poetical science”[6] and herself as an “Analyst & Metaphysician”.

As a teenager, Ada’s prodigious mathematical talents, led her to have British mathematician Charles Babbage, as her mentor. By then Babbage had become very famous and had come to be known as ‘the father of computers’. Babbage was reputed to have developed the Analytical Engine. Between 1842 and 1843, Ada translated an article on the Analytical Engine, which she supplemented with an elaborate set of notes, simply called Notes. These notes contain what many consider to be the first computer program—that is, an algorithm designed to be carried out by a machine. As a result, she is often regarded as the first computer programmer. Ada died at a very young age of 36.

As an ode to her, the mathematical program used in the Defense Industry has been named Ada. And to celebrate our first Programmer, the second Tuesday of October has been named Ada Lovelace Day. ALD celebrates the achievement of women in Science, Technology and Engineering and Math (STEM). It aims to increase the profile of women in STEM and, in doing so, create new role models who will encourage more girls into STEM careers and support women already working in STEM.

Most of us applauded Benedict Cumberbatch’s turn as Alan Turing in the movie,  Imitation Game. We got to know about the contribution, that Alan Turning and his code breaking team at the Bletchley Park, played in singularly cracking the German Enigma code and how the code helped them to proactively know when the Germans were about to attack the Allied sites and in the process could conduct preemptive strikes. In the movie, Kiera Knightly played the role of Joan Clark Joan was an English code-breaker at the British Intelligence wing, MI5, at Bletchley Park during the World War II. She was appointed a Member of the Order of the British Empire (MBE) in 1947, because of the important part she essayed in decoding the famed German Enigma code along with Alan Turing and the team.

Joan Clark attended Cambridge University with a scholarship and there she gained a double first degree in mathematics. But the irony of it all was that she was denied a full degree, as till 1948, Cambridge only awarded degrees to men. The head of the Code-breakers group, Hugh Alexander,  described her as “one of the best in the section”, yet while promoting Joan Clark, they had initially given her a job title of a typist, as women were not allowed to be a Crypto Analyst. Clarke became deputy head of British Intelligence unit, Hut 8 in 1944.  She was paid less than the men and in the later years she believed that she was prevented from progressing further because of her gender.

In World War II the  US Army was tasked with a Herculean job to calculate the trajectories of ballistic missiles. The problem was that each equation took 30 hours to complete, and the Army needed thousands of them. So the Army, started to recruit every mathematician they could find. They placed ads in newspapers;  first in Philadelphia, then in New York City, then in far out west in places like Missouri, seeking women “computers” who could hand-compute the equations using mechanical desktop calculators. The selected applicants would be stationed at the  University of Pennsylvania in Philly. At the height of this program, the US Army employed more than 100 women calculators. One of the last women to join the team was a farm girl named Jean Jennings. To support the project, the US Army-funded an experimental project to automate the trajectory calculations. Engineers John Presper Eckert and John W. Mauchly, who are often termed as the Inventors of Mainframe computers, began designing the Electronic Numerical Integrator and Computer, or ENIAC as it was called.  That experimenting paid off: The 80-foot long, 8-foot tall, black metal behemoth, which contained hundreds of wires, 18,000 vacuum tubes, 40 8-foot cables, and 3000 switches, would become the first all-electric computer called ENIAC.

When the ENIAC was nearing completion in the spring of 1945, the US Army randomly selected six women, computer programmers,  out of the 100 or so workers and tasked them with programming the ENIAC. The engineers handed the women the logistical diagrams of ENIAC’s 40 panels and the women learned from there. They had no programming languages or compilers. Their job was to program ENIAC to perform the firing table equations they knew so well.

The six women—Francis “Betty” Snyder Holberton, Betty “Jean” Jennings Bartik, Kathleen McNulty Mauchly Antonelli, Marlyn Wescoff Meltzer, Ruth Lichterman Teitelbaum, and Frances Bilas Spence—had no documentation and no schematics to work with.

There was no language, no operating system, the women had to figure out what the computer was, how to interface with it, and then break down a complicated mathematical problem into very small steps that the ENIAC could then perform.  They physically hand-wired the machine,  using switches, cables, and digit trays to route data and program pulses. This might have been a very complicated and arduous task. The ballistic calculations went from taking 30 hours to complete by hand to taking mere seconds to complete on the ENIAC.

Unfortunately, ENIAC was not completed in time, hence could not be used during World War II. But 6 months after the end of the war, on February 14, 1946 The ENIAC was announced as a modern marvel in the US. There was praise and publicity for the Moore School of Electrical Engineering at the University of Pennsylvania, Eckert and Mauchly were heralded as geniuses. However, none of the key programmers, all the women were not introduced in the event. Some of the women appeared in photographs later, but everyone assumed they were just models, perfunctorily placed to embellish the photograph.

After the war, the government ran a campaign asking women to leave their jobs at the factories and the farms so returning soldiers could have their old jobs back. Most women did, leaving careers in the 1940s and 1950s and perforce were required to become homemakers. Unfortunately, none of the returning soldiers knew how to program the ENIAC.

All of these women programmers had gone to college at a time when most men in this country didn’t even go to college. So the Army strongly encouraged them to stay, and for the most part, they did, becoming the first professional programmers, the first teachers of modern programming, and the inventors of tools that paved the way for modern software.

The Army opened the ENIAC up to perform other types of non-military calculations after the war and Betty Holberton and Jean Jennings converted it to a stored-program machine. Betty went on to invent the first sort routine and help design the first commercial computers, the UNIVAC and the BINAC, alongside Jean. These were the first mainframe computers in the world.

Today the Indian IT  industry is at $ 160 B and is at 7.7 %age of the Indian GDP and employs approximately 2.5 Million direct employees and a very high percentage of them are women. Ginni Rommeti, Meg Whitman are the CEOs of IBM and HP while Sheryl Sandberg is the COO of Facebook. They along with Padmasree Warrior, ex CTO of CISCO have been able to crack the glass ceiling.    India boasts of Senior Leadership in leading IT companies like Facebook, IBM, CapGemini, HP, Intel  etc.. who happen to be women. At our company, GAVS, we are making an effort to put in policies, practices, culture that attract, retain, and nurture women leaders in IT. The IT industry can definitely be a major change agent in terms of employing a large segment of women in India and can be a transformative force for new vibrant India. We must be having our Indian Ada, Joan, Jean and Betty and they are working at ISRO, at Bangalore and Sriharikota, at the Nuclear Plants at Tarapur.

ABOUT THE AUTHOR

Sumit Ganguli

Sumit Ganguli

Understanding Reinforcement Learning in five minutes

Reinforcement learning (RL) is an area of Machine Learning (ML) that takes suitable actions to maximize rewards situations. The goal of reinforcement learning algorithms is to find the best possible action to take in a specific situation. Just like the human brain, it is rewarded for good choices and penalized for bad choices and learns from each choice. RL tries to mimic the way that humans learn new things, not from a teacher but via interaction with the environment. At the end, the RL learns to achieve a goal in an uncertain, potentially complex environment.

Understanding Reinforcement Learning

How does one learn cycling? How does a baby learn to walk? How do we become better at doing something with more practice? Let us explore learning to cycle to illustrate the idea behind RL.

Did somebody tell you how to cycle or gave you steps to follow? Or did you learn it by spending hours watching videos of people cycling? All these will surely give you an idea about cycling; but will it be enough to actually get you cycling? The answer is no. You learn to cycle only by cycling (action). Through trials and errors (practice), and going through all the positive experiences (positive reward) and negative experiences (negative rewards or punishments), before getting your balance and control right (maximum reward or best outcome). This analogy of how our brain learns cycling applies to reinforcement learning. Through trials, errors, and rewards, it finds the best course of action.

Components of Reinforcement Learning

The major components of RL are as detailed below:

  • Agent: Agent is the part of RL which takes actions, receives rewards for actions and gets a new environment state as a result of the action taken. In the cycling analogy, the agent is a human brain that decides what action to take and gets rewarded (falling is negative and riding is positive).
  • Environment: The environment represents the outside world (only relevant part of the world which the agent needs to know about to take actions) that interacts with agents. In the cycling analogy, the environment is the cycling track and the objects as seen by the rider.
  • State: State is the condition or position in which the agent is currently exhibiting or residing. In the cycling analogy, it will be the speed of cycle, tilting of the handle, tilting of the cycle, etc.
  • Action: What the agent does while interacting with the environment is referred to as action. In the cycling analogy, it will be to peddle harder (if the decision is to increase speed), apply brakes (if the decision is to reduce speed), tilt handle, tilt body, etc.
  • Rewards: Reward is an indicator to the agent on how good or bad the action taken was. In the cycling analogy, it can be +1 for not falling, -10 for hitting obstacles and -100 for falling, the reward for outcomes (+1, -10, -100) are defined while building the RL agent. Since the agent wants to maximize rewards, it avoids hitting and always tries to avoid falling.

Characteristics of Reinforcement Learning

Instead of simply scanning the datasets to find a mathematical equation that can reproduce historical outcomes like other Machine Learning techniques, reinforcement learning is focused on discovering the optimal actions that will lead to the desired outcome.

There are no supervisors to guide the model on how well it is doing. The RL agent gets a scalar reward and tries to figure out how good the action was.

Feedback is delayed. The agent gets an instant reward for action, however, the long-term effect of an action is known only later. Just like a move in chess may seem good at the time it is made, but may turn out to be a bad long term move as the game progress.

Time matters (sequential). People who are familiar with supervised and unsupervised learning will know that the sequence in which data is used for training does not matter for the outcome. However, for RL, since action and reward at current state influence future state and action, the time and sequence of data matters.

Action affects subsequent data RL agent receives.

Why Reinforcement Learning

The type of problems that reinforcement learning solves are simply beyond human capabilities. They are even beyond the solving capabilities of ML techniques. Besides, RL eliminates the need for data to learn, as the agent learns by interacting with the environment. This is a great advantage to solve problems where data availability or data collection is an issue.

Reinforcement Learning applications

RL is the darling of ML researchers now. It is advancing with incredible pace, to solve business and industrial problems and garnering a lot of attention due to its potential. Going forward, RL will be core to organizations’ AI strategies.

Reinforcement Learning at GAVS

Reinforcement Learning is core to GAVS’ AI strategy and is being actively pursued to power the IP led AIOps platform – Zero Incident FrameworkTM (ZIF). We had our first success on RL; developing an RL agent for automated log rotation in servers.

References:

Reinforcement Learning: An Introduction second edition by Richard S. Sutton and Andrew G. Barto

https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf

About the Author:

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.