Monitoring for Success

Do you know if your end users are happy?

(In the context of users of Applications (desktop, web or cloud-based), Services, Servers and components of IT environment, directly or indirectly.)

The question may sound trivial, but it has a significant impact on the success of a company. The user experience is a journey, from the time they use the application or service, till after they complete the interaction. Experience can be determined based on factors like Speed, Performance, Flawlessness, Ease of use, Security, Resolution time, among others. Hence, monitoring the ‘Wow’ & ‘Woe’ moments of the users is vital.

Monitor is a component of GAVS’ AIOps Platform, Zero Incident FrameworkTM (ZIF). One of the key objectives of the Monitor platform is to measure and improve end-user experience. This component monitors all the layers (includes but not limited to application, database, server, APIs, end-points, and network devices) in real-time that are involved in the user experience. Ultimately,this helps to drive the environment towards Zero Incidents.

This figure shows the capability of ZIF monitoring that cut across all layers starting from end-user to storage and how it is linked to other the components of the platform

Key Features of ZIF Monitor are,

  • Unified solution for all IT environment monitoring needs: The platform covers the end-to-end monitoring of an IT landscape. The key focus is to ensure all verticals of IT are brought under thorough monitoring. The deeper the monitoring, the closer an organization is to attaining a Zero Incident EnterpriseTM.
  • Agents with self-intelligence: The intelligent agents capture various health parameters about the environment. When the target environment is already running under low resource, the agent will not task it with more load. It will collect the health-related metrics and communicate through the telemetry channel efficiently and effectively. The intelligence is applied in terms of parameters to be collected, the period of collection and many more.
  • Depth of monitoring: The core strength of Monitor is it comes with a list of performance counters which are defined by SMEs across all layers of the IT environment. This is a key differentiator; the monitoring parameters can be dynamically configured for the target environment. Parameters can be added or removed on a need basis.
  • Agent & Agentless (Remote): The customers can choose from Agent & Agentless options for the solutions. The remote solution is called as Centralized Remote Monitoring Solution (CRMS). Each monitoring parameter can be remotely controlled and defined from the CRMS. Even the agents that are running in the target environment can be controlled from the server console.
  • Compliance: Plays a key role in terms of the compliance of the environment. Compliance ranges from ensuring the availability of necessary services and processes in the target environment and defines the standard of what Application, Make, Version, Provider, Size, etc. that are allowed in the target environment.
  • Auto discovery: Monitor can auto-discover the newer elements (servers, endpoints, databases, devices, etc.) that are getting added to the environment. It can automatically add those newer elements into the purview of monitoring.
  • Auto scale: Centralized Remote Monitoring Solution (CRMS) can auto-scale on its own when newer elements are added for monitoring through auto-discovery. The auto scale includes various aspects, like load on channel, load on individual polling engine, and load on each agentless solution.
  • Real time user & Synthetic Monitoring: Real-time user monitoring is to monitor the environment when the user is active. Synthetic monitoring is through simulated techniques. It doesn’t wait for the user to make a transaction or use the system. Instead, it simulates the scenario and provide insights to make decision proactively.
  • Availability & status of devices connected: Monitor also includes the monitoring of availability and control of USB and COM port devices that are connected.
  • Black box monitoring: It is not always possible to instrument the application to get insights.Hence, the Black Box technique is used. Here the application is treated as a black box and it is monitored in terms of its interaction with the Kernel & OS through performance counters.
High level overview of Monitor’s components,

  • Agents, Agentless: These are the means through which monitoring is done at the target environment, like user devices, servers, network devices, load balancers, virtualized environment, API layers, databases, replications, storage devices, etc.
  • ZIF Telemetry Channel: The performance telemetry that are collected from source to target are passed through this channel to the big data platform.
  • Telemetry Data: Refers to the performance data and other metrics collected from all over the environment.
  • Telemetry Database:This is the big data platform, in which the telemetry data from all sources are captured and stored.
  • Intelligence Engine: This parses the telemetry data in near real time and raises notifications based on rule-based threshold and as well as through dynamic threshold.
  • Dashboard&Alerting Mechanism: These are the means through which the results of monitoring are conveyed as metrics in dashboard and as well as notifications.
  • Integration with Analyze, Predict & Remediate components: Monitoring module communicates the telemetry to Analyze & Predict components of the ZIF platform for it to use the data for analysis and apply Machine Learning for prediction. Both Monitor & Predict components, communicate with Remediate platform to trigger remediation.

The Monitor component works in tandem with Analyze, Predict and Remediate components of the ZIF platform to achieve an incident free IT environment. Implementation of ZIF is the right step to driving an enterprise towards Zero Incidents. ZIF is the only platform in the industry which comes from the single product platform owner who owns the end-to-end IP of the solution with products developed from scratch.

For more detailed information on GAVS’ Monitor, or to request a demo please visit zif.ai/products/monitor/

(To be continued…)

About the Author

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.

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Cleaning up our Digital Dirt

Now, what exactly is digital dirt, in the context of enterprises? It is highly complex and ambiguous to precisely identify digital dirt, let alone address the related issues. Chandra Mouleswaran S, Head of Infra Services at GAVS Technologies says that not all the applications that run in an organization are actually required to run. The applications that exist, but not used by internal or external users or internal or external applications contribute to digital dirt. Such dormant applications get accumulated over time due to the uncertainty of their usage and lack of clarity in sunsetting them. They stay in the organization forever and waste resources, time and effort. Such hidden applications burden the system, hence they need to be discovered and removed to improve operational efficiency.

Are we prepared to clean the trash? The process of eliminating digital dirt can be cumbersome. We cannot fix what we do not find. So, the first step is to find them using a specialized application for discovery. Chandra further elaborated on the expectations from the ‘Discovery’ application. It should be able to detect all applications, the relationships of those applications with the rest of the environment and the users using those applications. It should give complete visibility into applications and infrastructure components to analyze the dependencies.

Shadow IT

Shadow IT, the use of technology outside the IT purview is becoming a tacitly approved aspect of most modern enterprises. As many as 71% of employees across organizations are using unsanctioned apps on devices of every shape and size, making it very difficult for IT departments to keep track. The evolution of shadow IT is a result of technology becoming simpler and the cloud offering easy connectivity to applications and storage. Because of this, people have begun to cherry-pick those things that would help them get things done easily.

Shadow IT may not start or evolve with bad intentions. But, when employees take things into their own hands, it is a huge security and compliance risk, if the sprawling shadow IT is not reined in. Gartner estimates that by next year (2020), one-third of successful attacks experienced by enterprises will be on their shadow IT resources.

The Discovery Tool

IT organizations should deploy a tool that gives complete visibility of the landscape, discovers all applications – be it single tenant or multi-tenant, single or multiple instance, native or virtually delivered, on-premise or on cloud and map the dependencies between them. That apart, the tool should also indicate the activities on those applications by showing the users who access them and the response times in real-time. The dependency map along with user transactions captured over time will paint a very clear picture for IT Managers and might bring to light some applications and their dependencies, that they probably never knew existed!

Discover, is a component of GAVS’ AIOps Platform,Zero Incident Framework™ (ZIF). Discover can work as a stand-alone component and also cohesively with the rest of the AIOps Platform. Discover provides Application Auto Discovery and Dependency Mapping (ADDM). It automatically discovers and maps the applications and topology of the end to end deployment, hop by hop. Some of its key features are:

  • Zero Configuration

The auto-discovery features require no additional configuration upon installation.

  • Discovers Applications

It uniquely and automatically discovers all Windows and Linux application in your environment, identifies it by name, and measures the end-to-end and hop-by-hop response time and throughput of each application. This works for applications installed on physical servers, in virtualized guest operating systems, applications automatically provisioned in private or hybrid clouds, and those running in public clouds. It also works irrespective of whether the application was custom developed or purchased.

  • Discovers Multitenant Applications

It auto-discovers multitenant applications hosted on web servers and does not limit the discovery to the logical server level.

  • Discovers Multiple Instances of Application

It auto-discovers multiple instances of the same application and presents them all as a group with the ability to drill down to the details of each instance of the application.

  • Discovers SaaS Applications

It auto-discovers any requests directed to SaaS applications such as Office 365 or Salesforce and calculates response time and throughput to these applications from the enterprise.

  • Discovers Virtually Delivered Applications or Desktops

It automatically maps the topology of the delivered applications and VDIs, hop-by-hop and end-to-end. It provides extensive support for Citrix delivered applications or desktops. This visibility extends beyond the Citrix farm into the back-end infrastructure on which the delivered applications and VDIs are supported.

  • Discovers Application Workload Topologies

The architecture auto-discovers application flow mapping topology and user response times to create the application topology and update it in near real-time — all without user configuration. This significantly reduces the resources required to configure service models and operate the product.

  • Discovers Every Tier of Every Multi-Tiered Application

It auto-discovers the different tiers of every multi-tiered application and displays the performance of each tier. Each tier is discovered and named with the transactional throughput and response times shown for each tier.

  • Discovers All Users of All Applications

It identifies each user of every application and the response time that the user experiences for each use of a given application.

  • Discovers Anomalies with Applications

The module uses a sophisticated anomaly detection algorithm to automatically assess when a response time excursion is valid, then if a response exceeds normal baseline or SLA performance expectations, deep diagnostics are triggered to analyze the event. In addition, the hop-by-hop segment latency is compared against the historical norms to identify deterministically which segment has extended latency and reduced application performance.

For more detailed information on GAVS’ Discover, or to request a demo please visit

Discover

About the Authors:

Chandra Mouleswaran S:

Chandra heads the IMS practice at GAVS. He has around 25+ years of rich experience in IT Infrastructure Management, enterprise applications design & development and incubation of new products / services in various industries. He has also created a patent for a mistake proofing application called ‘Advanced Command Interface”. He thinks ahead and his implementation of ‘disk based backup using SAN replication’ in one of his previous organizations as early as in 2005 is a proof of his visionary skills.

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.

AIOps Demystified

IT Infrastructure has been on an incredibly fascinating journey from the days of mainframes housed in big rooms just a few decades ago, to mini computers, personal computers, client-servers, enterprise & mobile networks, virtual machines and the cloud! While mobile technologies have made computing omnipresent, the cloud coupled with technologies like virtual computing and containers has changed the traditional IT industry in unimaginable ways and has fuelled the rise of service-oriented architectures where everything is offered as a service and on-demand. Infrastructure as a Service (IaaS), Platform as a Service (PaaS), DBaaS, MBaaS, SaaS and so on.

As companies try to grapple with this technology explosion, it is very clear that the first step has to be optimization of the IT infrastructure & operations. Efficient ITOps has become the foundation not just to aid transformational business initiatives, but even for basic survival in this competitive world.

The term AIOps was first coined by Gartner based on their research on Algorithmic IT Operations. Now, it refers to the use of Artificial Intelligence(AI) for IT Operations(Ops), which is the use of Big Data Analytics and AI technologies to optimize, automate and supercharge all aspects of IT Operations.

Why AI in IT operations?

The promise behind bringing AI into the picture has been to do what humans have been doing, but better, faster and at a much larger scale. Let’s delve into the different aspects of IT operations and see how AI can make a difference.

Visibility

The first step to effectively managing the IT landscape is to get complete visibility into it. Why is that so difficult? The sheer variety and volume of applications, users and environments make it extremely challenging to get a full 360 degree view of the landscape. Most organizations use applications that are web-based, virtually delivered, vendor-built, custom-made, synchronous/asynchronous/batch processing, written using different programming languages and/or for different operating systems, SaaS, running in public/private/hybrid cloud environments, multi-tenant, multiple instances of the same applications, multi-tiered, legacy, running in silos! Adding to this complexity is the rampant issue of shadow IT, which is the use of applications outside the purview of IT, triggered by the easy availability of and access to applications and storage on the cloud. And, that’s not all! After all the applications have been discovered, they need to be mapped to the topology, their performances need to be baselined and tracked, all users in the system have to be found and their user experiences captured.

The enormity of this challenge is now evident. AI powers auto-discovery of all applications, topology mapping, baselining response times and tracking all users of all these applications. Machine Learning algorithms aid in self-learning, unlearning and auto-correction to provide a highly accurate view of the IT landscape.

Monitoring

When the IT landscape has been completely discovered, the next step is to monitor the infrastructure and application stacks. Monitoring tools provide real-time data on their availability and performance based on relevant metrics.

The problem is two-fold here. Typically, IT organizations need to rely on several monitoring tools that cater to the different environments/domains in the landscape. Since these tools work in silos, they give a very fractured view of the entire system, necessitating data correlation before it can be gainfully used for Root Cause Analysis(RCA) or actionable insights.

Pattern recognition-based learning from current and historical data helps correlate these seemingly independent events, and therefore to recognize & alert deviations, performance degradations or capacity utilization bottlenecks in real-time and consequently enable effective Root Cause Analysis(RCA) and reduce an important KPI, Mean Time to Identify(MTTI).

Secondly, there is colossal amounts of data in the form of logs, events, metrics pouring in at high velocity from all these monitoring tools, creating alert fatigue. This makes it almost impossible for the IT support team to check each event, correlate with the other events, tag and prioritize them and plan remedial action.

Inherently, machines handle volume with ease and when programmed with ML algorithms learn to sift through all the noise and zero-in on what is relevant. Noise nullification is achieved by the use of Deep Learning algorithms that isolate events that have the potential to become incidents and Reinforcement Learning algorithms that find and eliminate duplicates and false positives. These capabilities help organizations bring dramatic improvements to another critical ITOps metric, Mean Time to Resolution(MTTR).

Other areas of ITOps where AI brings a lot of value are in Advanced Analytics- Predictive & Prescriptive- and Remediation.

Advanced Analytics

Unplanned IT Outages result in huge financial losses for companies and even worse, a sharp dip in customer confidence. One of the biggest value-adds of AI for ITOps then, is in driving proactive operations that deliver superior user experiences with predictable uptime. Advanced Analytics on historical incident data identifies patterns, causes and situations in the entire stack(infrastructure, networks, services and applications) that lead to an outage. Multivariate predictive algorithms drive predictions of incident and service request volumes, spikes and lulls way in advance. AIOps tools forecast usage patterns and capacity requirements to enable planning, just-in-time procurement and staffing to optimize resource utilization. Reactive purchases after the fact, can be very disruptive & expensive.

Remediation

AI-powered remediation automates remedial workflows & service actions, saving a lot of manual effort and reducing errors, incidents and cost of operations. Use of chatbots provides round-the-clock customer support, guiding users to troubleshoot standard problems, and auto-assigns tickets to appropriate IT staff. Dynamic capacity orchestration based on predicted usage patterns and capacity needs induces elasticity and eliminates performance degradation caused by inefficient capacity planning.

Conclusion

The beauty of AIOps is that it gets better with age as the learning matures on exposure to more and more data. While AIOps is definitely a blessing for IT Ops teams, it is only meant to augment the human workforce and not to replace them entirely. And importantly, it is not a one-size-fits-all approach to AIOps. Understanding current pain points and future goals and finding an AIOps vendor with relevant offerings is the cornerstone of a successful implementation.

GAVS’ Zero Incident Framework TM (ZIF) is an AIOps-based TechOps Platform that enables organizations to trend towards a Zero Incident Enterprise TM. ZIF comes with an end-to-end suite of tools for ITOps needs. It is a pure-play AI Platform powered entirely by Unsupervised Pattern-based Machine Learning! You can learn more about ZIF or request a demo here.

READ ALSO OUR NEW UPDATES

AIOps Demystified

IT Infrastructure has been on an incredibly fascinating journey from the days of mainframes housed in big rooms just a few decades ago, to mini computers, personal computers, client-servers, enterprise & mobile networks, virtual machines and the cloud! While mobile technologies have made computing omnipresent, the cloud coupled with technologies like virtual computing and containers has changed the traditional IT industry in unimaginable ways and has fuelled the rise of service-oriented architectures where everything is offered as a service and on-demand. Infrastructure as a Service (IaaS), Platform as a Service (PaaS), DBaaS, MBaaS, SaaS and so on.

As companies try to grapple with this technology explosion, it is very clear that the first step has to be optimization of the IT infrastructure & operations. Efficient ITOps has become the foundation not just to aid transformational business initiatives, but even for basic survival in this competitive world.

The term AIOps was first coined by Gartner based on their research on Algorithmic IT Operations. Now, it refers to the use of Artificial Intelligence(AI) for IT Operations(Ops), which is the use of Big Data Analytics and AI technologies to optimize, automate and supercharge all aspects of IT Operations.

Why AI in IT operations?

The promise behind bringing AI into the picture has been to do what humans have been doing, but better, faster and at a much larger scale. Let’s delve into the different aspects of IT operations and see how AI can make a difference.

Visibility

The first step to effectively managing the IT landscape is to get complete visibility into it. Why is that so difficult? The sheer variety and volume of applications, users and environments make it extremely challenging to get a full 360 degree view of the landscape. Most organizations use applications that are web-based, virtually delivered, vendor-built, custom-made, synchronous/asynchronous/batch processing, written using different programming languages and/or for different operating systems, SaaS, running in public/private/hybrid cloud environments, multi-tenant, multiple instances of the same applications, multi-tiered, legacy, running in silos! Adding to this complexity is the rampant issue of shadow IT, which is the use of applications outside the purview of IT, triggered by the easy availability of and access to applications and storage on the cloud. And, that’s not all! After all the applications have been discovered, they need to be mapped to the topology, their performances need to be baselined and tracked, all users in the system have to be found and their user experiences captured.

The enormity of this challenge is now evident. AI powers auto-discovery of all applications, topology mapping, baselining response times and tracking all users of all these applications. Machine Learning algorithms aid in self-learning, unlearning and auto-correction to provide a highly accurate view of the IT landscape.

Monitoring

When the IT landscape has been completely discovered, the next step is to monitor the infrastructure and application stacks. Monitoring tools provide real-time data on their availability and performance based on relevant metrics.

The problem is two-fold here. Typically, IT organizations need to rely on several monitoring tools that cater to the different environments/domains in the landscape. Since these tools work in silos, they give a very fractured view of the entire system, necessitating data correlation before it can be gainfully used for Root Cause Analysis(RCA) or actionable insights.

Pattern recognition-based learning from current and historical data helps correlate these seemingly independent events, and therefore to recognize & alert deviations, performance degradations or capacity utilization bottlenecks in real-time and consequently enable effective Root Cause Analysis(RCA) and reduce an important KPI, Mean Time to Identify(MTTI).

Secondly, there is colossal amounts of data in the form of logs, events, metrics pouring in at high velocity from all these monitoring tools, creating alert fatigue. This makes it almost impossible for the IT support team to check each event, correlate with the other events, tag and prioritize them and plan remedial action.

Inherently, machines handle volume with ease and when programmed with ML algorithms learn to sift through all the noise and zero-in on what is relevant. Noise nullification is achieved by the use of Deep Learning algorithms that isolate events that have the potential to become incidents and Reinforcement Learning algorithms that find and eliminate duplicates and false positives. These capabilities help organizations bring dramatic improvements to another critical ITOps metric, Mean Time to Resolution(MTTR).

Other areas of ITOps where AI brings a lot of value are in Advanced Analytics- Predictive & Prescriptive- and Remediation.

Advanced Analytics

Unplanned IT Outages result in huge financial losses for companies and even worse, a sharp dip in customer confidence. One of the biggest value-adds of AI for ITOps then, is in driving proactive operations that deliver superior user experiences with predictable uptime. Advanced Analytics on historical incident data identifies patterns, causes and situations in the entire stack(infrastructure, networks, services and applications) that lead to an outage. Multivariate predictive algorithms drive predictions of incident and service request volumes, spikes and lulls way in advance. AIOps tools forecast usage patterns and capacity requirements to enable planning, just-in-time procurement and staffing to optimize resource utilization. Reactive purchases after the fact, can be very disruptive & expensive.

Remediation

AI-powered remediation automates remedial workflows & service actions, saving a lot of manual effort and reducing errors, incidents and cost of operations. Use of chatbots provides round-the-clock customer support, guiding users to troubleshoot standard problems, and auto-assigns tickets to appropriate IT staff. Dynamic capacity orchestration based on predicted usage patterns and capacity needs induces elasticity and eliminates performance degradation caused by inefficient capacity planning.

Conclusion

The beauty of AIOps is that it gets better with age as the learning matures on exposure to more and more data. While AIOps is definitely a blessing for IT Ops teams, it is only meant to augment the human workforce and not to replace them entirely. And importantly, it is not a one-size-fits-all approach to AIOps. Understanding current pain points and future goals and finding an AIOps vendor with relevant offerings is the cornerstone of a successful implementation.

GAVS’ Zero Incident Framework TM (ZIF) is an AIOps-based TechOps Platform that enables organizations to trend towards a Zero Incident Enterprise TM. ZIF comes with an end-to-end suite of tools for ITOps needs. It is a pure-play AI Platform powered entirely by Unsupervised Pattern-based Machine Learning! You can learn more about ZIF or request a demo here.

READ ALSO OUR NEW UPDATES

What you need to know about AIOps?

Emergence of AIOps

There has been a gigantic growth of AIOps in the last two years. It has successfully transitioned from an emergent category to an inevitability. Companies adopted AIOps to automate and improve IT operations by applying big data and machine learning (ML). Adoption of such technologies compelled IT operations to adapt a multi-cloud infrastructure. According to Infoholic Research, the AIOps market is expected to grow at a CAGR of 33.08% during the forecast period 2018–2024.

What is AIOps?

AIOps broadly stands for Artificial Intelligence for IT Operations. With a combination of big data and ML, AIOps platform improvises IT operations and also replaces certain tasks including tracking availability, event correlation, performance monitoring, IT service management and automation. Most of these technologies are well-defined and matured.

AIOps data originates from log files, metrics, monitoring tools, helpdesk ticketing and other sources. It sorts, manages and assimilates these data to provide insight in problem areas. The goal of AIOps is to analyze data and discover patterns that can predict potential incidents in future.

Focus areas of AIOps

  • AIOps helps with open data access without letting organizational silos play a part in it.
  • AIOps upgrades data handling ability which also impacted on the scope of data analysis.
  • It has a unique ability to stay aligned to organizational goals.
  • AIOps increases the scope of risk prediction.
  • It also reduces response time.

Impact of AI in IT operations

  • Capacity planning: AIOps can support in understanding workloads and plan configuration appropriately without allowing a scope for speculation.
  • Resource utilization: AIOps allows predictive scaling where auto-scale feature of cloud IaaS can adjust itself based on historical data.
  • Storage: AIOps helps in storage activity through disk calibration, reconfiguration and allocation of new storage volumes.
  • Anomaly detection: It can detect anomalies and critical issues faster with accuracy more than humans, reducing potential threats and system downtime.
  • Threat management: It helps to analyze breaches in both internal and external environments.
  • Root-cause analysis: AIOps is effective in root-cause analysis, through which it reduces response time and creates remedy after locating the issue.
  • Forecasting outages: Outage prediction is essential for the growth of IT operations. Infact, the market of forecasting outages through AIOps, is expected to grow from $493.7 to $1.14 billion between 2016 and 2021 based on industry reports.
  • Future innovation: AIOps has played a key role in automating a major chunk of IT operations in a massive way. It frees resources to focus on crucial things aligned to strategy and organizational goals.

Problems AIOps solved

The common issues AIOps solves to enable IT operations’ adoption of digitization are as follows:

  • It has the ability to gain access over large data sets across environments while maintaining data reliability for comprehensive analysis.
  • It simplifies data analysis through automation empowered by ML.
  • Through accurate prediction mechanism, it can avoid costly downtime and improve customer satisfaction.
  • Through implementation of automation, manual tasks can be eliminated.
  • AIOps can improve teamwork and workflow activities between IT groups and other business units.

Peeping into the future

AIOps platform acts as a foundation stone in projecting future endeavors of organizations. It uses real-time analysis of data to provide insights to impact business decisions. Successful implementation of AIOps depends on key parameters index (KPIs). It can also deliver a predictive and proactive IT operation by reducing failure, detection, resolution and investigation.

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Impact of network analytics in IT ops

Role of network analytics

Looking at the pace in which our world is moving towards digitization, one has to admit, that network analytics will play an important part in paving the way how IT would operate in future. Network analytics for an enterprise is complex, the AI and automation technologies in use help achieve intelligent and effective ways towards future IT operations.

Network analytics improves user experience in IT operations by analyzing network data. It compares and correlates data to address a problem or trend. It manages IT operations by channelizing the below mentioned data inputs.

  1. Real network traffic generated by client.
  2. Synthetic network traffic created by virtual clients.
  3. Metrics from infrastructure.
  4. System logs.
  5. Data flow.
  6. Application program interface (API) from application server.

Scope of network analytics

A user can face poor network performance or disruption in service due to either, OS problem, Wi-Fi or LAN issue, DHCP, WAN problem or application failure. To locate the actual cause for interruption is essential for smooth functioning of IT operations. Network analytics operate with the help of big data analytics along with cloud computing and machine learning to examine data and create a holistic perspective. Proactive IT Operations Led by predictive insights enhance 90% data accuracy. It can also interpret data in a visual format to develop an elaborate understanding. Here, network analytics plays an important role in redefining IT operations.

  • Network analytics uses proactive analytical tools such as; Sisense, Azure, R Open, GoodData etc. for a deeper understanding of issues and to locate the source of error which can make IT operation seamless. Sisense helps processing data 10 times faster, Azure’s 100 modules per experiment or 10 GB storage space is cost effective. GoodData allows 360-degree overview for customer insights.
  • Earlier, the task to fix a network issue was relatively simple, now, with the increasing usage of virtual and mobile devices and cloud computing, detecting an issue within a network and fixing the same has become complex. Without network analytics, IT Ops will not be able to sustain the wrath of disruption.
  • There has been huge diversification lately in the field of hardware, operating systems, application and services. Understanding network problems within these landscapes, can be challenging. Network analytics plays an important role here by easing the task through user performance management (UPM).
  • Network analytics also minimizes the issue with access network in IT operations starting from getting Wi-Fi access to authentications, obtaining IP addresses or resolving DHCP requests.
  • Network analytics tool can help reduce network traffic through alteration in facilities. It can use network event correlation to understand the impact on devices and customer’s experience on bandwidth latency.
  • Network analytics assists a great deal in network capacity planning and deployment opportunity for an improved network ROI by up to 15% as per market research.

Difference between monitoring and analytics network solution

To analyze the impact network analytics has on IT Ops, it is essential to understand the difference between monitoring and analytics solution. Monitoring refers to collecting and interpreting data in a passive form and sharing potentially actionable information to the network manager. Hence, it focuses on spotting problems without fixing them.

Analytics is more prescriptive where, recorded historical data is understood, learnt and analyzed paving a pattern to be followed. Data collected from Wi-Fi, devices, applications and WAN create trends that impact IT operations.

Advanced analytics

Along with pinpointing the area of concern, advanced analytics tries to automate new solutions to the detected problem. Advanced network analytics help to understand if the issue is with a client operating system, application, network services or Wi-Fi access. This enhances the scope of IT Ops by improving infrastructure by providing insights to take the overall operations to the next level. The new generation of network analytic tools and solutions can reduce outages, upgrade systems and applications, improve customer experience and simplify the process of operations in IT.

Benefits of network analytics in IT ops

  1. Network analytics can help IT Ops analyze the requirement and create a balance so that, the available resources can be optimally utilized to enhance network performance and lower the cost structure of IT Ops.
  2. Network analytics help with data mining insights for identification of revenue and enabling a data-driven and action-oriented IT operation.
  3. Network analytics can help in capacity planning where both resources and services can be calculated in advance for an apt provisioning.

Impact of network analytics in brief

Network analytics, with its analytics tool, can predict future down time, allowing necessary action to be taken on time. It also increases awareness of the root cause of the problem to remediate faster and eventually prevent and result in reducing MTTR by 95%. This can reduce organizational disruption and operational costs while increasing customer satisfaction.

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AIOps – IT Infrastructure Services for the Digital Age

The IT infrastructure services landscape is undergoing a significant shift, driven by digitalization. As focus shifts from cost efficiency to digital enablement, organizations need to re-imagine the IT infrastructure services model to deliver the necessary back-end agility, flexibility, and fluidity. Automation, analytics, and Artificial Intelligence (AI) – comprising the “codifying elements” for driving AIOps – help drive this desired level of adaptability within IT infrastructure services. Automation, analytics, and AI – which together comprise the “codifying elements” for driving AIOps– help drive the desired level of adaptiveness within IT infrastructure services. Intelligent automation, leveraging analytics and ML, embeds powerful, real-time business and user context and autonomy into IT infrastructure services. Intelligent automation has made inroads in enterprises in the last two to three years, backed by a rapid proliferation and maturation of solutions in the market.

Artificial Intelligence Operations (AIOps) . Everest Group 2018 Report . IT Infrastructure

Benefits of codification of IT infrastructure services

Progressive leverage of analytics and AI, to drive an AIOps strategy, enables the introduction of a broader and more complex set of operational use cases into IT infrastructure services automation. As adoption levels scale and processes become orchestrated, the benefits potentially expand beyond cost savings to offer exponential value around user experience enrichment, services agility and availability, and operations resilience. Intelligent automation helps maximize value from IT infrastructure services by:

  1. Improving the end-user experience through contextual and personalized support
  2. Driving faster resolution of known/identified incidents leveraging existing knowledge, intelligent diagnosis, and reusable, automated workflows
  3. Avoiding potential incidents and improving business systems performance through contextual learning (i.e., based on relationships among systems), proactive health monitoring and anomaly detection, and preemptive healing

Although the benefits of intelligent automation are manifold, enterprises are yet to realize commensurate advantage from investments in infrastructure services codification. Siloed adoption, lack of well-defined change management processes, and poor governance are some of the key barriers to achieving the expected value.  The design should involve an optimal level of human effort/intervention targeted primarily at training, governing, and enhancing the system, rather than executing routine, voluminous tasks.  A phased adoption of automation, analytics, and AI within IT infrastructure services has the potential to offer exponential business value. However, to realize the full potential of codification, enterprises need to embrace a lean operating model, underpinned by a technology-agnostic platform. The platform should embed the codifying elements within a tightly integrated infrastructure services ecosystem with end-to-end workflow orchestration and resolution.

The market today has a wide choice of AIOps solutions, but the onus is on enterprises to select the right set of tools / technologies that align with their overall codification strategy.

Click here to read the complete whitepaper by Everest Group

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Can automation manage system alerts?

System alerts and critical alerts

One of the most important and critical roles of an IT professional is to handle incoming alerts efficiently and effectively. This will ensure a threat-free environment and reduce the chances of system outages. Now, not all incoming alerts are critical; an alert can pop up on a window screen for a user to act on, blocking the underlying webpage. One can configure the setting to automatic alert resolution where an alert will be closed automatically after a number of days.

Can automation manage system alerts?

Gradually, many companies are incorporating automation in the field of managing system alerts. The age-old technology of monitoring system for both, internal and external alerts is not effective in streamlining the actual process of managing these incoming alerts. Here, IT process automation (ITPA) can take incident management to a whole new level. Automation in collaboration with monitoring tools can identify, analyze and finally prioritize incoming alerts while sending notification to fix the issue. Such notifications can be customized depending on the selected mode of preference. Also, it is worth mentioning here that automated workflows can be created to open, update and close tickets in the service desk, minimizing human intervention while electronically resolving issues.

Integration of a monitoring system with automation

Automation of system alerts happen with the following workflow. It highly improved the incident management system, reducing human intervention and refining the quality of monitoring.

  1. The monitoring system detects an incident within the IT infrastructure and triggers an alert.
  2. The alert is addressed by automation software and a trouble ticket is generated thereafter in service desk.
  3. Then the affected lot is notified via preferred method of communication.
  4. Network admin is then notified by ITPA to address the issue and recover.
  5. The service ticket is accordingly updated through implementation of automation.

Benefits of automation to manage system alerts

Relying on a process that is manually performed especially, while dealing with critical information in a workflow can be difficult. In such a scenario, automation of monitoring critical data in business systems like accounting, CRM, ERP or warehousing can improve on consistency. It can also recognize significant or critical data changes immediately triggering notification for the same. With this 360-degree visibility of critical information, decision making can happen a lot faster which in the long run can forestall serious crisis. It also improves the overall performance of the company and customer service and reduces financial risk due to anomalies and security threats. Hence, it can be aptly mentioned that automation of system alerts can effectively reduce response and resolution time. It can also lessen system downtime and improve MTTR.

BPA platform’s role to manage system alerts

The business process automation (BPA) platform enables multi-recipient capabilities so that notification can be sent to employees across different verticals. This will increase their visibility on real-time information that is relevant to their organizational role. This platform also provides escalation capabilities where notification will be sent to higher management if an alert is not addressed on time.

Conclusion

For large-scale organizations, the number of alerts spotted by detection tools are growing in number with time. This inspired IT enterprises to automate security control configurations and implement responsive security analysis tasks. Through automation of security control and processes, a new firewall rule can be automatically created or deleted based on alerts. Once a threat is detected, automated response is created. We can conclude that automation can manage system alerts efficiently and effectively. And a pre-built workflow often helps to jump-start an automation process of addressing a system alert.

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AIOps Trends in 2019

Adoption of AIOps by organizations

Artificial Intelligence in IT operations (AIOps) is rapidly pacing up with digital transformation. Over the years, there has been a paradigm shift of enterprise application and IT infrastructure. With a mindset to enhance flexibility and agility of business processes, organizations are readily adopting cloud platforms to provision their on-premise software. Implementation of technologies like AIOps and hybrid environment has facilitated organizations to gauge the operational challenges and reduced their operational costs considerably. It helps enterprises in:

  • Resource utilization
  • Capacity planning
  • Anomaly detection
  • Threat detection
  • Storage management
  • Cognitive analysis

Infact, if we look at Gartner’s prediction, by 2022, 40% of medium and large-scale enterprises will adopt artificial intelligence (AI) to increase IT productivity.

AIOps Market forecast

According to Infoholic Research, the AIOps market is expected to reach approximately $14 billion by 2024, growing at a CAGR of 33.08% between 2018–2024. The companies that will provide AIOps solutions to enhance IT operations management in 2019 include BMC Software, IBM, GAVS Technologies, Splunk, Fix Stream, Loom System and Micro Focus. By end of 2019, US alone is expected to contribute over 30% of growth in AIOps and it will also help the global IT industry reach over $5,000 billion by the end of this year. Research conducted by Infoholic also confirmed that AIOps has been implemented by 60% of the organizations to reduce noise alerts and identify real-time root cause analysis.

Changes initiated by enterprises to adopt AIOps

2019 will be the year to reveal the true value of AIOps through its applications. By now, organizations have realized that context and efficient integrations with existing systems are essential to successfully implement AIOps.

1. Data storage

Since AIOps need to operate on a large amount of data, it is essential that enterprises absorb data from reliable and disparate sources which, then, can be contextualized for use in AI and ML applications. For this process to work seamlessly, data must be stored in modern data lakes so that it can be free from traditional silos.

2. Technology partnership

Maintaining data accuracy is a constant struggle and in order to overcome such complexity, in 2019, there will be technology partnership between companies to deal with customer demands for better application program interface (APIs).

3. Automation of menial tasks

Organizations are trying to automate menial tasks to increase agility by freeing up resources. Through automation, organizations can explore a wide range of opportunities in AIOps that will increase their efficiency.

4. Streamling of people, process and tools

Although multi-cloud solutions provide flexibility and cost-efficiency, however, without proper tools to monitor, it can be challenging to manage them. Hence, enterprises are trying to streamline their people, process and tools to create a single, siloed-free overview to benefit from AIOps.

5. Use of real-time data

Enterprises are trying to ingest and use real-time data for event correlation and immediate anomaly detection since, with the current industrial pace, old data is useless to the market.

6. Usage of self-discovery tools

Organizations are trying to induce self-discovery tools in order to overcome the challenge of lack of data scientists in the market or IT personnel with coding skills to monitor the process. The self-discovery tools can operate without human intervention.

Conclusion

Between 2018 to 2024, the global AIOps market value of real time analytics and application performance management is expected to grow at a rapid pace. Also, it is observed that currently only 5% of large IT firms have adopted AIOps platforms due to lack of knowledge and assumption about the cost-effectiveness. However, this percentage is expected to reach 40% by 2022. Companies like CA Technologies, GAVS Technologies, Loom Systems and ScienceLogic has designed tools to simplify AIOps deployment and it is anticipated that over the next three years, there will be sizable progress in the AIOps market.

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Cost effective solutions on AIOps platforms

Digital transformation in IT operations

The global market value of AIOps is predicted to increase from $2.24 billion in 2017 to $9.90 billion by 2023, as per industry reports. IT organizations, globally, are focusing on digital transformation aggressively. Technologies like AI, Big Data, ML are compelling IT operations’ platforms to modify and adapt to multi-cloud infrastructure. With a vision to explore new arena of opportunities, AIOps can monitor, analyze, correlate and automate, easing IT operations. The focus areas where AIOps plays a key role in enabling digital transformation includes:

  1. Open data access, where data can be recorded from various authentic sources and can be freed from organizational silos for repetitive analysis
  2. Big data was initially thought to increase efficiency and decision-making capabilities of enterprises. However, with the expansion of data, things became complex. Here the intervention of AIOps improved the ability to handle huge data thus, expanding the scope of data analysis
  3. ML can access data from various sources and can modify or create new algorithms without human intervention. AIOps enhances ML’s ability to handle enormous data and at the same time stay aligned to organizational goals
  4. Data analytics can solve major data related problems in IT domain and on top of that, AIOps could leverage competitive advantage by promising richer business context, short response time and ability to predict potential risk

Scope of AI in IT Ops – are they cost effective?

  • With an intent to study time and labor management, any organization will end up spending massively on both, time and money. For that matter, an application programming interface (API), can help a company complete, its reports in no time. This can ramp up the pace of report creation, thus opening a scope for real-time analysis of compliance. Now, that is definitely cost-effective.
  • A global recruitment firm increased its hiring ratio by about 8%, through implementation of AI. It helped the firm to identify and match the right skill set along with the prediction for attrition per resource. This proved cost effective since attrition costed the organization up to $25,000 per resource.
  • From the operational perspective, in a 24/7 environment, if there is an outage, it will result in a series of logged complaints, which then will become difficult for an individual to manually transcribe. This is where AI plays an important part in identifying the main issues through log analytics.
  • Technology like cognitive insight, creates a data pool of wide range of solutions on critical issues. AI bridges the gap between big data and humans through operational intelligence, accuracy and speed, thus making it cost-effective to a great extent.
  • Enterprises like Dyn and British Airways suffered Distributed Denial of Service (DDoS) attacks post which they implemented cognitive insight which secured their operations.

Cost effective solution of AIOps

Analyzing and managing cost is essential. Doing a cost analysis of cloud with components like IOPs, VMs, storage capacity, bandwidth, API can be tricky and complex. AI implementation can help here to segregate the cost of securing a more accurate IT budget.

  • AI and root-cause analysis
    AI is very effective in the area of root-cause analysis. It is efficient in locating an issue and creating a remediation for the same, thus solving complex problems in a short span. AIOps helped a US Bank to automate root cause correlation to gather data on customer dissatisfaction and thus, enhancing customer experience.
  • Threat detection is now a cakewalk
    Through machine learning algorithms, AIOps can learn to detect anomalies and critical issues. GAVS’ security division designed a remedial platform combining ML algorithms and AI’s self-learning capabilities to reduce risk and predict future anomalies on an IT platform, ensuring a secured environment for GAVS’ customers.
  • AIOps and its outage forecasting competences
    AIOps can forecast outages through data prediction and also increase resource utilization through identifying areas of cross training. The market of forecasting outages through AIOps, is expected to grow from $493.7 million in 2016 to $1.14 billion by 2021, as per industry reports.
  • Combining tools for an innovative future
    Automation and collaboration of tools can enhance productivity and accuracy. AIOps powered with big data and ML helps in process automation and is used more as a strategic than operational tool. With this merger, data could be analyzed, optimized, and transformed efficiently. In GAVS, the focus is on a “Zero Incident” platform where GAVS can help enterprises to reach Zero Incident state through the above-mentioned collaboration of tools. This will definitely prove cost-effective and enhance the end-user experience.

Solutions built with innovation and cost-efficiency is the key

In their zeal to enter the digitized innovation area, organizations are aggressively trying to locate cost-effective and reliable solutions. Although many companies still rely on age old machines and processes which require constant monitoring and human intervention, however, automation of IT operations is a boon, ensuring cost-efficiency across levels.