AIOps Myth Busters

The explosion of technology & data is impacting every aspect of business. While modern technologies have enabled transformational digitalization of enterprises, they have also infused tremendous complexities in infrastructure & applications. We have reached a point where effective management of IT assets mandates supplementing human capabilities with Artificial Intelligence & Machine Learning (AI/ML).      

AIOps is the application of Artificial Intelligence (AI) to IT operations (Ops). AIOps leverages AI/ML technologies to optimize, automate, and supercharge all aspects of IT Operations. Gartner predicts that the use of AIOps and digital experience monitoring tools for monitoring applications and infrastructure would increase by 30% in 2023. In this blog, we hope to debunk some common misconceptions about AIOps.

MYTH 1: AIOps mainly involves alert correlation and event management

AIOps can deliver enormous value to enterprises that harness the wide range of use cases it comes with. While alert correlation & management are key, AIOps can add a lot of value in areas like monitoring, user experience enhancement, and automation.  

AIOps monitoring cuts across infrastructure layers & silos in real-time, focusing on metrics that impact business outcomes and user experience. It sifts through monitoring data clutter to intelligently eliminate noise, uncover patterns, and detect anomalies. Monitoring the right UX metrics eliminates blind spots and provides actionable insights to improve user experience. AIOps can go beyond traditional monitoring to complete observability, by observing patterns in the IT environment, and externalizing the internal state of systems/services/applications. AIOps can also automate remediation of issues through automated workflows & standard operating procedures.

MYTH 2: AIOps increases human effort

Forbes says data scientists spend around 80% of their time preparing and managing data for analysis. This leaves them with little time for productive work! With data pouring in from monitoring tools, quite often ITOps teams find themselves facing alert fatigue and even missing critical alerts.

AIOps can effectively process the deluge of monitoring data by AI-led multi-layered correlation across silos to nullify noise and eliminate duplicates & false positives. The heavy lifting and exhausting work of ingesting, analyzing, weeding out noise, correlating meaningful alerts, finding the probable root causes, and fixing them, can all be accomplished by AIOps. In short, AIOps augments human capabilities and frees up their bandwidth for more strategic work.

MYTH 3: It is hard to ‘sell’ AIOps to businesses

While most enterprises acknowledge the immense potential for AI in ITOps, there are some concerns that are holding back widespread adoption. The trust factor with AI systems, the lack of understanding of the inner workings of AI/ML algorithms, prohibitive costs, and complexities of implementation are some contributing factors. While AIOps can cater to the full spectrum of ITOps needs, enterprises can start small & focus on one aspect at a time like say alert correlation or application performance monitoring, and then move forward one step at a time to leverage the power of AI for more use cases. Finding the right balance between adoption and disruption can lead to a successful transition.  

MYTH 4: AIOps doesn’t work in complex environments!

With Machine Learning and Big Data technologies at its core, AIOps is built to thrive in complex environments. The USP of AIOps is its ability to effortlessly sift through & garner insights from huge volumes of data, and perform complex, repetitive tasks without fatigue. AIOps systems constantly learn & adapt from analysis of data & patterns in complex environments. Through this self-learning, they can discover the components of the IT ecosystem, and the complex network of underlying physical & logical relationships between them – laying the foundation for effective ITOps.   

MYTH 5: AIOps is only useful for implementing changes across IT teams

An AIOps implementation has an impact across all business processes, and not just on IT infrastructure or software delivery. Isolated processes can be transformed into synchronized organizational procedures. The ability to work with colossal amounts of data; perform highly repetitive tasks to perfection; collate past & current data to provide rich inferences; learn from patterns to predict future events; prescribe remedies based on learnings; automate & self-heal; are all intrinsic features that can be leveraged across the organization. When businesses acknowledge these capabilities of AIOps and intelligently identify the right target areas within their organizations, it will give a tremendous boost to quality of business offerings, while drastically reducing costs.

MYTH 6: AIOps platforms offer only warnings and no insights

With its ability to analyze and contextualize large volumes of data, AIOps can help in extracting relevant insights and making data-driven decisions. With continuous analysis of data, events & patterns in the IT environment – both current & historic – AIOps acquires in-depth knowledge about the functioning of the various components of the IT ecosystem. Leveraging this information, it detects anomalies, predicts potential issues, forecasts spikes and lulls in resource utilization, and even prescribes appropriate remedies. All of this insight gives the IT team lead time to fix issues before they strike and enables resource optimization. Also, these insights gain increasing precision with time, as AI models mature with training on more & more data.

MYTH 7: AIOps is suitable only for Operations

AIOps is a new generation of shared services that has a considerable impact on all aspects of application development and support. With AIOps integrated into the dev pipeline, development teams can code, test, release, and monitor software more efficiently. With continuous monitoring of the development process, problems can be identified early, issues fixed, and changes rolled back as appropriate. AIOps can promote better collaboration between development & ops teams, and proactive identification & resolution of defects through AI-led predictive & prescriptive insights. This way AIOps enables a shift left in the development process, smarter resource management, and significantly improves software quality & time to market.  

Augmented Analytics with SAP Analytics Cloud

Augmented Analytics

In 2017, Gartner coined the term ‘augmented analytics’ and claimed it would be the future of data analytics. They predicted it would be a dominant driver of new purchases of analytics and business intelligence as well as data science and machine learning platforms, and of embedded analytics.

Here is the why and how.

Most organizations depend on data to back up its decision-making and strategy. Organizations collect data on all accounts of processes and events; thus, analyzing and effectively managing the breadth of this data is challenging yet significant for mining it for business insights.

Traditional business intelligence tools have given way to a new generation of business intelligence tools – Augmented Analytics technology.

Augmented Analytics is an approach of data analytics that employs machine learning (ML) and natural language processing (NLP) to automate and improve data access and data quality, uncover hidden patterns and correlations in data, pinpoint what’s driving results, predict future results and suggest actions to maximize or minimize desirable or undesirable outcomes.

Augmented Analytics is designed to conduct analyses and generate business insights automatically with little to no supervision and can be used without needing the assistance of a business analyst or data scientist. However, the focus of Augmented Analytics stays in its assistive role, where technology does not replace humans but supports them.

Evolution of Analytics

Business Intelligence (BI) and Analytics has evolved, increasing the demand for decision making through data analytics. It drives to unfold from traditional mirror reporting into self-service Business Intelligence and analytics.

Despite the advances in self-service analytics with agile discovery, many businesses demand assistance to uncover insights in data.

The next generation of BI and analytics products are augmented with artificial intelligence (AI) including ML, which automates complex analytics processes, and NLP makes it easier for users without knowledge of data science or query languages to obtain insights.

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Augmented analytics offer starting-point suggestions and guidance to the users. It also empowers businesses to leverage more of their data to make better decisions when compared to the traditional and self-service Business Intelligence.

SAP Analytics Cloud

SAP Analytics Cloud (SAC) is an analytical solution that features all the analytics functionalities like business intelligence, augmented analytics, predictive analytics, enterprise planning, and application building in one intuitive user interface. It is empowered with ML and built-in AI that helps discover in-depth insights, simplify access to critical information and enable adequate decision-making.

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Augmented SAP Analytics Cloud

Augmented analytics capabilities offered by SAP Analytics Cloud empowers business intelligence to reap the benefits of AI and ML.

SAP Analytics Cloud facilitates users to interact with the system using natural language to gather automatic insights, where Predictive Scenarios offer an accessible way into Predictive Analytics using the past data to foresee the future.

Let’s look at the Analytics features, and capabilities offered by SAP Analytics Cloud

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Search to Insight – Query search in Natural Language

The Search to Insight feature enables query search through natural language through conversational AI and NLP. No knowledge of query languages like SQL, R, or Python is required. Asking questions just like in a search engine or digital personal assistant fetches insightful answers represented by visualization or numeric values tailored based on the question type.

Search to Insight provides auto-complete suggestions to match words or phrases in questions for measures and dimensions in the data and includes auto spell-check.

AIOps Artificial Intelligence for IT Operations

Smart Insights – Instant explanations

The Smart Insights feature facilitates digging deeper into the data points. It analyzes the underlying dataset and runs various statistical algorithms to offer insights based on the current user context.

It helps to understand top contributors of specific data points without having to manually pivot or slice and dice the data. When a data point is selected, ML calculations run on information that is of the same nature as the selected data point. For example, if the selected data point is ‘Total Revenue’, the top contributors are based on ‘Total Revenue’. It analyzes the dimension in the selected data and looks for members in these dimensions that influence the selected value.

Smart Discovery – Easily reveal insights

The Smart Discovery feature identifies hidden patterns and statically relevant relationships in the data to discover how business factors influence performance. It helps to understand the business drivers behind the core KPIs.

Based on the selection of measure or dimension, smart discovery automatically generates interactive story pages as below –

Overview: It explains the data distribution, summary of trends, and the detected patterns for the target dimension or measure.

Key Influencers:  It explains the influence of the dimensions for the value of the target measures in the context of the selected model using classification and regression techniques, where the classification techniques are used to identify dimensions that segregate results into different groups of results and the regression techniques identify relationships between data points to predict future results.

Unexpected Values: It displays the details about outliers, where the actual values differ greatly from what the predictive model would expect. If an actual value diverges from the regression line it is categorized as unexpected.

Simulation: The simulation facilitates the ‘what-if’ analysis, users can change the values of the measures and dimensions to see the predicted change positively, negatively, or neutrally in the target measure.

Smart Predict – Answers the toughest questions

Smart Predict feature predicts the likelihood of different outcomes based on the historical data using techniques such as data mining, statistics, machine learning, and artificial intelligence.

Smart Predict, also referred as Predictive forecasting, considers different values, trends, cycles, and/or fluctuations in the data to make predictions that can be leveraged to aid business planning processes.

Smart Predict provides 3 different predictive scenario options for selection

Classification: It can be used to generate predictions for a binary event. For example, whether individual customers would be likely to buy the target product or not.

Time Series: It can be used to forecast values over a set period. For example, forecasting the sales of product by month or week, using historical data.

Regression: It can be used to predict values and explore key values behind them. For example, predicting the price of an imported product based on projected duties or shipping charges.

In the modern world of business Intelligence, SAP Analytics cloud’s ML technology augments the analytic process which assists from insights to actions and enables avoiding the agenda-driven and biased decision making by revealing the accurate patterns which drives the business.

References

MF Kashif

About the Author –

Kashif is a SAP Business objects consultant and a business analytics enthusiast. He believes that the “Ultimate goal is not about winning, but to reach within the depth of capabilities and to compete against yourself to be better than what you are today.”

Why is AIOps an Industrial Benchmark for Organizations to Scale in this Economy?

Business Environment Overview

In this pandemic economy, the topmost priorities for most companies are to make sure the operations costs and business processes are optimized and streamlined. Organizations must be more proactive than ever and identify gaps that need to be acted upon at the earliest.

The industry has been striving towards efficiency and effectivity in its operations day in and day out. As a reliability check to ensure operational standards, many organizations consider the following levers:

  1. High Application Availability & Reliability
  2. Optimized Performance Tuning & Monitoring
  3. Operational gains & Cost Optimization
  4. Generation of Actionable Insights for Efficiency
  5. Workforce Productivity Improvement

Organizations that have prioritized the above levers in their daily operations require dedicated teams to analyze different silos and implement solutions that provide the result. Running projects of this complexity affects the scalability and monitoring of these systems. This is where AIOps platforms come in to provide customized solutions for the growing needs of all organizations, regardless of the size.

Deep Dive into AIOps

Artificial Intelligence for IT Operations (AIOps) is a platform that provides multilayers of functionalities that leverage machine learning and analytics.  Gartner defines AIOps as a combination of big data and machine learning functionalities that empower IT functions, enabling scalability and robustness of its entire ecosystem.

These systems transform the existing landscape to analyze and correlate historical and real-time data to provide actionable intelligence in an automated fashion.

AIOps platforms are designed to handle large volumes of data. The tools offer various data collection methods, integration of multiple data sources, and generate visual analytical intelligence. These tools are centralized and flexible across directly and indirectly coupled IT operations for data insights.

The platform aims to bring an organization’s infrastructure monitoring, application performance monitoring, and IT systems management process under a single roof to enable big data analytics that give correlation and causality insights across all domains. These functionalities open different avenues for system engineers to proactively determine how to optimize application performance, quickly find the potential root causes, and design preventive steps to avoid issues from ever happening.

AIOps has transformed the culture of IT war rooms from reactive to proactive firefighting.

Industrial Inclination to Transformation

The pandemic economy has challenged the traditional way companies choose their transformational strategies. Machine learning powered automation for creating an autonomous IT environment is no longer a luxury. he usage of mathematical and logical algorithms to derive solutions and forecasts for issues have a direct correlation with the overall customer experience. In this pandemic economy, customer attrition has a serious impact on the annual recurring revenue. Hence, organizations must reposition their strategies to be more customer centric in everything they do. Thus, providing customers with the best-in-class service coupled with continuous availability and enhanced reliability has become an industry-standard.

As reliability and scalability are crucial factors for any company’s growth, cloud technologies have seen a growing demand. This shift of demand for cloud premises for core businesses has made AIOps platforms more accessible and easier to integrate. With the handshake between analytics and automation, AIOps has become a transformative technology investment that any organization can make.

As organizations scale in size, so does the workforce and the complexity of the processes. The increase in size often burdens organizations with time-pressed teams having high pressure on delivery and reactive housekeeping strategies. An organization must be ready to meet the present and future demands with systems and processes that scale seamlessly. This why AIOps platforms serve as a multilayered functional solution that integrates the existing systems to manage and automate tasks with efficiency and effectivity. When scaling results in process complexity, AIOps platforms convert the complexity to effort savings and productivity enhancements.

Across the industry, many organizations have implemented AIOps platforms as transformative solutions to help them embrace their present and future demand. Various studies have been conducted by different research groups that have quantified the effort savings and productivity improvements.

The AIOps Organizational Vision

As the digital transformation race has been in full throttle during the pandemic, AIOps platforms have also evolved. The industry did venture upon traditional event correlation and operations analytical tools that helped organizations reduce incidents and the overall MTTR. AIOps has been relatively new in the market as Gartner had coined the phrase in 2016.  Today, AIOps has attracted a lot of attention from multiple industries to analyze its feasibility of implementation and the return of investment from the overall transformation. Google trends show a significant increase in user search results for AIOps during the last couple of years.

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While taking a well-informed decision to include AIOps into the organization’s vision of growth, we must analyze the following:

  1. Understanding the feasibility and concerns for its future adoption
  2. Classification of business processes and use cases for AIOps intervention
  3. Quantification of operational gains from incident management using the functional AIOps tools

AIOps is truly visioned to provide tools that transform system engineers to reliability engineers to bring a system that trends towards zero incidents.

Because above all, Zero is the New Normal.

About the Author –

Ashish Joseph

Ashish Joseph is a Lead Consultant at GAVS working for a healthcare client in the Product Management space. His areas of expertise lie in branding and outbound product management.

He runs a series called #BizPective on LinkedIn and Instagram focusing on contemporary business trends from a different perspective. Outside work, he is very passionate about basketball, music and food.

Kappa (κ) Architecture – Streaming at Scale

We are in the era of Stream processing-as-a-service and for any data-driven organization, Stream-based computing has becoming the norm. In the last three parts https://bit.ly/2WgnILP, https://bit.ly/3a6ij2k,  https://bit.ly/3gICm88, I had explored Lambda Architecture and its variants. In this article let’s discover Streaming in the big data. ‘Real-time analytics’, ‘Real-time data’ and ‘Streaming data’ has become mandatory in any big data platform. The aspiration to extend data analysis (predictive, descriptive, or otherwise) to streaming event data has been common across every enterprise and there is a growing interest to find real-time big data architectures. Kappa (K) Architecture is one that deals with streaming. Let’s see why Real-Time Analytics matter more than ever and mandates data streaming and how streaming architecture like Kappa works. Is Kappa an alternative to lambda?

“You and I are streaming data engines.” – Jeff Hawkins

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Questioning Lambda

Lambda architecture fits very well in many real-time use cases, mainly in re-computing algorithms. At the same time, Lambda Architecture has the inherent development and operational complexities like all the algorithms must be implemented twice, once in the cold path, the batch layer, and another execution in the hot path or the real-time layer. Apart from this dual execution path, the Lambda Architecture has the inevitable issue of debugging. Because operating two distributed multi-node services is more complex than operating one.

Given the obvious discrepancies of Lambda Architecture, Jay Kreps, CEO of Confluent, co-creator of Apache Kafka started the discussion on the need for new architecture paradigm which uses less code resource and could perform well in certain enterprise scenarios. This gave rise to Kappa (K) Architecture. The real need Kappa Architecture isn’t about efficiency at all, but rather about allowing people to develop, test, debug, and operate their systems on top of a single processing framework. In fact, Kappa is not taken as competitor to LA on the contrary it is seen as an alternative.

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What is Streaming & Streaming Architecture?

Modern business requirements necessitate a paradigm shift from traditional approach of batch processing to real-time data streams. Data-centric organizations mandate the Stream first approach. Real-time data streaming or Stream first approach means at the very moment. So real-time analytics, either On-demand real-time analytics or Continuous real-time analytics, is the capability to process data right at the moment it arrives in the system. There is no possibility of batch processing of data. Not to mention, it enhances the ability to make better decision making and performing meaningful action on a timely basis. At the right place and at the right time, real-time analytics combines and analyzes data. Thus, it generates value from disparate data.

Typically, most of the streaming architectures will have the following 3 components:

  • an aggregator that gathers event streams and batch files from a variety of data sources,
  • a broker that makes data available for consumption,
  • an analytics engine that analyzes the data, correlates values and blends streams together.

Kappa (K) Architecture for Big Data era

Kappa (K) Architecture is one of the new software architecture patterns for the new Data era. It’s mainly used for processing streaming data. Kappa architecture gets the name Kappa from the Greek letter (K) and is attributed to Jay Kreps for introducing this architecture.

The main idea behind the Kappa Architecture is that both the real-time and batch processing can be carried out, especially for analytics, with a single technology stack. The data from IoT, streaming, and static/batch sources or near real-time sources like change data capture is ingested into messaging/ pub-sub platforms like Apache Kafka.

An append-only immutable log store is used in the Kappa Architecture as the canonical store. Following are the pub/sub or message buses or log databases that can be used for ingestion:

  • Amazon Quantum Ledger Database (QLDB)
  • Apache Kafka
  • Apache Pulsar
  • Amazon Kinesis
  • Amazon DynamoDB Streams
  • Azure Cosmos DB Change Feed
  • Azure EventHub
  • DistributedLog
  • EventStore
  • Chronicle Queue
  • Pravega

Distributed Stream processing engines like Apache Spark, Apache Flink, etc. will read the data from the streaming platform and transform it into an analyzable format, and then store it into an analytics database in the serving layer. Following are some of the distributed streaming computation systems

  • Amazon Kinesis
  • Apache Flink
  • Apache Samza
  • Apache Spark
  • Apache Storm
  • Apache Beam
  • Azure Stream Analytics
  • Hazelcast Jet
  • Kafka Streams
  • Onyx
  • Siddhi

In short, any query in the Kappa Architecture is defined by the following functional equation.

Query = λ (Complete data) = λ (live streaming data) * λ (Stored data)

The equation means that all the queries can be catered by applying Kappa function to the live streams of data at the speed layer. It also signifies that the stream processing occurs on the speed layer in Kappa architecture.

Pros and Cons of Kappa architecture

Pros

  • Any architecture that is used to develop data systems that doesn’t need batch layer like online learning, real-time monitoring & alerting system, can use Kappa Architecture.
  • If computations and analysis done in the batch and streaming layer are identical, then using Kappa is likely the best solution.
  • Re-computations or re-iterations is required only when the code changes.
  • It can be deployed with fixed memory.
  • It can be used for horizontally scalable systems.
  • Fewer resources are required as the machine learning is being done on the real-time basis.

Cons

Absence of batch layer might result in errors during data processing or while updating the database that requires having an exception manager to reprocess the data or reconciliation.

On finding the right architecture for any data driven organizations, a lot of considerations were taken in. Like most successful analytics project, which involves streaming first approach, the key is to start small in scope with well-defined deliverables, then iterate. The reason for considering distributed systems architecture (Generic Lambda or unified Lambda or Kappa) is due to minimized time to value.

Sources

About the Author

Bargunan Somasundaram

Bargunan Somasundaram

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

Customize Business Outcomes with ZIFTM

Zero Incident Framework™ (ZIF) is the only AIOps platform that is powered with true machine learning algorithms with the capability to self-learn and adapt to today’s modern IT infrastructure.

ZIF’s goal has always been to deliver the right business outcomes for the stakeholders. Return on investment can be measured based on the outcomes the platform has delivered. Users get to choose what business outcomes are expected from the platform and the respective features are deployed in the enterprise to deliver the chosen outcome.

Single Pane of Action – Unified View across IT Enterprise

The biggest challenge IT Operations teams have been trying to tackle over the years is to get a bird’s eye view on what is happening across their IT landscape. The more complex the enterprise becomes the harder it becomes for the IT Operations team to understand what is happening across their enterprise. ZIF solves this issue with ease.

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The capability to ingest data from any source monitoring or ITSM tool has helped IT organizations to have a real-time view of what is happening across their landscape. Enormous time can be saved by the IT engineers with ZIF’s unified view, who would otherwise be traversing between multiple monitoring tools.

ZIF can integrate with 100+ tools to ingest (static/dynamic) data in real-time via ZIF Universal Connector. This is a low code component of ZIF and dataflows within the connector can also be templatized for reuse. 

AIOps based Analytics Platform

Intelligence – Reduction in MTTR – Correlation of Alerts/Events

Approximately 80% of the time is lost by IT engineers in identifying the problem statement for an incident. This has been costing billions of dollars for enterprises. ZIF, with the help of Artificial Intelligence, can reduce the mean time to identify the probable root cause of the incident within seconds. The high-performance correlation engine that runs under the hood of the platform process millions of patterns that the platform has learned from the historical data and correlates the sequences that are happening in real-time and creates cases. These cases are then assigned to IT engineers with the probable root cause for them to fix the issue. This increases the productivity of the IT engineers resulting in better revenue for organizations.

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Intelligence – Predictive Analytics

AIOps platforms are incomplete without the Predictive Analytics capability. ZIF has adopted unsupervised machine learning algorithms to perform predictive analytics on the utilization data that is ingested into the platform. These algorithms can learn trends and understand the symptoms of an incident by analyzing tons of data that the platform had consumed over a period. Based on the analysis, the platform generates opportunity cards that help IT engineers take proactive measures on the forecasted incident. These opportunity cards are generated a minimum of 60 minutes in advance which gives the engineers a lead time to fix an issue before it strikes the landscape.

Visibility – Auto-Discovery of IT Assets & Applications

ZIF agentless discovery is a seamless discovery component, that helps in identifying all the IP assets that are available in an enterprise. Just not discovering the assets, but the component also plots a physical topology & logical map for better consumption of the IT engineers. This gives a very detailed view of every asset in the IT landscape. The logical topology gives in-depth insights into the workload metrics that can be utilized for deep analytics.

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Visibility – Cloud Monitoring

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In today’s digital transformation journey, cloud is inevitable. To have a better control over the cloud orchestrated application, enterprises must depend on the monitoring tools provided by cloud providers. The lack of insights often leads to the unavailability of applications for end-users. More than monitoring, insights that help enterprises take better-informed decisions are the need of the hour.

ZIF’s cloud monitoring components can monitor any cloud instance. Data that are generated from the provider provided monitoring tools are ingested into ZIF to further analyze the data. ZIF can connect to Azure, AWS & Google Cloud to derive data-driven insights.

Optimization – Remediation – Autonomous IT Operations

ZIF does not stop by just providing insights. The platform deploys the right automation bot to remediate the incident.

ZIF has 250+ automation bots that can be deployed to fast-track the resolution process by a minimum of 90%. Faster resolutions result in increased uptime of applications and better revenue for the enterprise.

Sample ZIF bots:

  • Service Restart / VM Restart
  • Disk Space Clean-up
  • IIS Monitoring App Pool
  • Dynamic Resource Allocation
  • Process Monitoring & Remediation
  • DL & Security Group Management
  • Windows Event Log Monitoring
  • Automated phishing control based on threat score
  • Service request automation like password reset, DL mapping, etc.
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For more information on ZIF, please visit www.zif.ai

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.

Addressing Web Application Performance Issues

With the use of hybrid technologies and distributed components, the applications are becoming increasingly complex. Irrespective of the complexity, it is quite important to ensure the end-user gets an excellent experience in using the application. Hence, it is mandatory to monitor the performance of an application to provide greater satisfaction to the end-user.

External factors

When the web applications face performance issues, here are some questions you need to ask:

  • Does the application always face performance issues or just during a specific period?
  • Whether a particular user or group of users face the issue or is the problem omnipresent for all the users?
  • Are you treating your production environment as real production environment or have you loaded it with applications, services, and background processes running without any proper consideration?
  • Was there any recent release to any of the application stack like Web, Middle Tier, API, DB, etc., and how was the performance before this release?
  • Have there been any hardware or software upgrades recently?

Action items on the ground

Answering the above set of questions would have brought you closer to the root cause. If not, given below are some steps you can do to troubleshoot the performance issue:

  • Look at the number of incoming requests, is the application facing unusual load?
  • Identify how many requests are delaying more than a usual level, say more than 5000 milliseconds to serve a request, or a web page.
  • Is the load getting generated by a specific or group of users – is someone trying to create intentional load?
  • Look at the web pages/methods/functions in the source code which are taking more time. Check the logs of the web server, this can be identified provided the application does that level of custom logging.
  • Identify whether any 3rd party links or APIs which are being used in the application is causing slowness.
  • Check whether the database queries are taking more time.
  • Identify whether the problem is related to a certain browser.
  • Check if the server side or client side is facing any uncaught exceptions which are impacting the performance.
  • Check the performance of the CPU, Memory, and Disk of the server(s) in which the application is hosted.
  • Check the sibling processes which are consuming more Memory/CPU/Disk in all servers and take appropriate action depending on whether those background processes need to be in that server or can be moved somewhere or can be removed totally.
  • Look at the web server performance to fine tune the Cache, Session time out, Pool size, and Queue-length.
  • Check for deadlock, buffer hit ratio, IO Busy, etc. to fine tune the performance.

Challenges 

  • Doing all these steps exactly when there is a performance issue may not be practically all the time. By the time you collect some of these, you may lose important data for the rest of the items unless the history data is collected and stored for reference.
  • Even if the data is collected, correlating them to arrive at the exact root cause is not an easy task
  • You need to be tech savvy across all layers to know what parameters to collect and how to collect.

And the list of challenges goes on…

Think of an ideal situation where you have metrics of all these action items described above, right in front of you. Is there such magic bullet available? Yes, Zero Incident FrameworkTM Application Performance Monitoring (ZIF APM), it gives you the above details at your fingertips, thereby makes troubleshooting a simple task.

ZIF APM has more to offer than other regular APM. The APM Engine has built-in AI features. It monitors the application across all layers, starting from end-user, web application, web server, API layers, databases, underlying infrastructure that includes the OS and performance factors, irrespective of whether these layers are hosted on cloud or on-premise or both. It also applies the AI for monitoring, mapping, tracing and analyze the pattern to provide the Observability and Insights. Given below is a typical representation of distributed application and its components. And the rest of the section covers, how ZIF APM provides such deep level of insights.

ZIF APM

Once the APM Engine is installed/run on portfolio servers, the build-in AI engine does the following automatically: 

  1. Monitors the performance of the application (Web) layer, Service Layer, API, and Middle tier and Maps the insights from User <–> Web <–> API <–> Database for each and every applications – No need to manually link Application 1 in Web Server A with API1 in Middle Tier B and so on.
  2. Traces the end-to-end user transaction journey for all transactions with Unique ID.
  3. Monitors the performance of the 3rd party calls (e.g. web service, API calls, etc.), no need to map them.
  4. Monitors the End User Experience through RUM (Real User Monitoring) without any end-user agent.

<A reference screenshot of how APM maps the user transaction journey across different nodes. The screenshot also gives the Method level performance insights>

Why choose ZIF APM? Key Features and Benefits

  1. All-in-One – Provides the complete insight of the underlying Web Server, API server, DB server related infrastructure metrics like CPU, Memory, Disk, and others.
  2. End-user experience (RUM) – Captures performance issues and anomalies faced by end-user at the browser side.
  3. Anomalies detection – Offers deeper insights on the exceptions faced by the application including the line number in the source code where the issue has occurred.
  4. Code-level insights – Gives details about which method and function calls within the source code is taking more time or slowing down the application.
  5. 3rd Party and DB Layer visibility – Provides the details about 3rd party APIs or Database calls and Queries which are delaying the web application response.
  6. AHI – Application Health Index is a scorecard based on A) End User Experience, B) Application Anomalies, C) Server Performance and D) Database performance factors that are applicable in the given environment or application. Weightage and number of components A, B, C, D are variables. For instance, if ‘Web server performance’ or ‘Network Performance’ needs to be brought in as new variable ‘E’, then accordingly the weightage will be adjusted/calculated against 100%.
  7. Pattern Analysis – Analyzes unusual spikes through pattern matching and alerts are provided.
  8. GTrace – Provides the transaction journey of the user transaction and the layers it is passing through and where the transaction slows down, by capturing the performance of each transaction of all users.
  9. JVM and CLR – Provides the Performance of the underlying operating system, Web server, and run time (JVM, CLR).
  10. LOG Monitoring – Provides deeper insight on the application logs.
  11. Problem isolation– ZIF APM helps in problem isolation by comparing the performance with another user in the same location at the same time.

Visit www.zif.ai for more details.

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.

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
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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.

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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.

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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.

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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.

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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.

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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.