How AIOps Solutions with the Right Foundation Can Help Reduce the IT Blame Game
May 17, 2023

Joseph George

Share this

In the news last year, a core network change at a large service provider resulted in a major outage that not only impacted end consumers and businesses, but also critical services like 911 and Interac. At home, phone and internet service were down for the workday as millions were without service.

Modern distributed and ephemeral systems have connected us better than ever before, and the latest ChatGPT phenomenon has opened the possibility for new and mind-blowing innovations. However, at the same time, our dependency on this connected world, along with its nonstop innovations, challenges our ethos with important questions and concerns around privacy, ethics, and security, and challenges our IT teams with outages of often unknown origin.

When it comes to system outages, AIOps solutions with the right foundation can help reduce the blame game so the right teams can spend valuable time restoring the impacted services rather than improving their MTTI score (mean time to innocence). In fact, much of today's innovation around ChatGPT-style algorithms can be used to significantly improve the triage process and user experience.

In the monitoring space, impact analysis for services spanning application to network or cloud to mainframe has known gaps that, if solved, can have a big impact on service availability. Today, these gaps require human intervention and result in never-ending bridge calls and the blame game where each siloed team responsible for applications, infrastructure, network, and mainframe are in a race to improve their MTTI score. This, unfortunately, also has a direct impact on customer experience and brand quality.

The challenge faced by teams is a layering issue, or "Layeritis." For each layer, different kinds of monitoring solutions are used. Each solution, in turn, has its own team and applies different techniques like code injection, polling, or network taps. This wide spectrum of monitoring techniques eventually generates key artifacts like metrics, alarms, events, and topology that are unique and useful in the given solution but operate in silos and do not provide an end-to-end impact flow.

Tool spam leads to a noise reduction challenge, which many AIOps tools solve today with algorithmic event noise reduction using proven clustering algorithms. However, in practice, this has not been proven to get to root cause. The hard problem is root cause isolation across the layers, which requires a connected topology (knowledge graph) that spans the multiple layers and can deterministically reconcile devices/configuration items (CIs) across the different layers.

Challenge of root cause isolation across the siloed application, infrastructure, network, and mainframe layers.

Seven Steps to Cure Layeritis

The solution to an AIOps Layeritis challenge requires planning and multiple iterations to get right. Once steps 1-3 are in a good state, steps 4-7 are left to AI/machine learning (ML) algorithms to decipher the signal from the noise and provide actionable insights. The seven steps are as follows:

1. Data ingestion from monitoring tools representing the different layers to a common data lake that includes metrics, events, topology, and logs

2. Automatic reconciliation across the different layers to establish end-to-end connectivity.

■ Since end user experience is tied to service health score, include key browser performance or voice quality metrics.

■ Application topology to underlying virtual and physical infrastructure for cloud, containers, and private data centers (e.g., APM tools may connect to the virtual host, but will not provide visibility to the underlying physical infrastructure used to run the virtual hosts).

■ Infrastructure connectivity to the underlying virtual and physical network devices like switches, routers, firewalls, and load balancers.

■ Virtual and physical infrastructure connectivity to the mainframe services like DB2, MQ, IMS, and CICS.

3. Dynamic service modeling to draw boundaries and build business services based on reconciled layers.

4. Clustering algorithm for noise reduction of events from metrics, logs, and alarms within a service boundary.

5. Page ranking and network centricity algorithms for root cause isolation using the connected topology and historical knowledge graph.

6. Large Language Model (LLM)/Generative AI (GPT) algorithm to build human-readable problem summaries. This helps less technical help desk resources quickly understand the issue.

7. Knowledge graph updated with the causal series of events (aka a fingerprint). Fingerprints are compared with historical occurrences to help make informed decisions on root cause, determine the next best action, or take proactive action on issues that could become major incidents.

For algorithms to give positive results with a high level of confidence, good data ingestion is required. Garbage data will always give bad results. For data, organizations rely on proven monitoring tools for the different layers to provide artifacts like topology, metrics, events, and logs. Additionally, with metrics and logs, it's possible to create meaningful events based on anomaly detection and advanced log processing.

Below are three use cases that focus on common issues today's IT teams face, which can be resolved using AIOps in a single consolidated view to identify the root cause and automate the next steps:

Use Case 1: Application issue where infrastructure and network are not impacted. Here, AIOps will only identify the impacted application software components.

Use Case 2: Network issue where infrastructure and application are impacted, but not at fault.

Use Case 3: Mainframe database issue where connected application running on distributed infrastructure is impacted.

In each use case above, AIOps removes the need for time-intensive investigation and guesswork so your team can see and respond to issues — even before they affect the business — and focus on higher-value projects. 

Overall, AIOps solutions can provide visibility and generate proactive insights across the entire application structure, from end user to cloud to data center to mainframe.

Joseph George is VP of Product Management for Digital Service and Operations Management at BMC
Share this

The Latest

May 25, 2023

Developers need a tool that can be portable and vendor agnostic, given the advent of microservices. It may be clear an issue is occurring; what may not be clear is if it's part of a distributed system or the app itself. Enter OpenTelemetry, commonly referred to as OTel, an open-source framework that provides a standardized way of collecting and exporting telemetry data (logs, metrics, and traces) from cloud-native software ...

May 24, 2023

As SLOs grow in popularity their usage is becoming more mature. For example, 82% of respondents intend to increase their use of SLOs, and 96% have mapped SLOs directly to their business operations or already have a plan to, according to The State of Service Level Objectives 2023 from Nobl9 ...

May 23, 2023

Observability has matured beyond its early adopter position and is now foundational for modern enterprises to achieve full visibility into today's complex technology environments, according to The State of Observability 2023, a report released by Splunk in collaboration with Enterprise Strategy Group ...

May 22, 2023

Before network engineers even begin the automation process, they tend to start with preconceived notions that oftentimes, if acted upon, can hinder the process. To prevent that from happening, it's important to identify and dispel a few common misconceptions currently out there and how networking teams can overcome them. So, let's address the three most common network automation myths ...

May 18, 2023

Many IT organizations apply AI/ML and AIOps technology across domains, correlating insights from the various layers of IT infrastructure and operations. However, Enterprise Management Associates (EMA) has observed significant interest in applying these AI technologies narrowly to network management, according to a new research report, titled AI-Driven Networks: Leveling Up Network Management with AI/ML and AIOps ...

May 17, 2023

When it comes to system outages, AIOps solutions with the right foundation can help reduce the blame game so the right teams can spend valuable time restoring the impacted services rather than improving their MTTI score (mean time to innocence). In fact, much of today's innovation around ChatGPT-style algorithms can be used to significantly improve the triage process and user experience ...

May 16, 2023

Gartner identified the top 10 data and analytics (D&A) trends for 2023 that can guide D&A leaders to create new sources of value by anticipating change and transforming extreme uncertainty into new business opportunities ...

May 15, 2023

The only way for companies to stay competitive is to modernize applications, yet there's no denying that bringing apps into the modern era can be challenging ... Let's look at a few ways to modernize applications and consider what new obstacles and opportunities 2023 presents ...

May 11, 2023
Applications can be subjected to high traffic on certain days, which, if not taken into account, can lead to unpredictable outcomes and customer dissatisfaction. These may include slow loading speeds, downtime, and unpredictable outcomes, among others ... Hence, applications must be tested for load thresholds to improve performance. Businesses that ignore load performance testing and fail to continually scale these applications leave themselves open to service outages, customer dissatisfaction, and monetary losses ...
May 10, 2023

As online penetration grows, retailers' profits are shrinking — with the cost of serving customers anytime, anywhere, at any speed not bringing in enough topline growth to best monetize even existing investments in technology, systems, infrastructure, and people, let alone new investments, according to Digital-First Retail: Turning Profit Destruction into Customer and Shareholder Value, a new report from AlixPartners and World Retail Congress ...