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I will use this talk to present a holistic Chaos Engineering framework that:
* Highlights the context of chaos engineering and performance testing, esp. as they both relate to
Service/Site reliability engineering (SRE).
* Describes how SLO, SLI (Service Level Objectives and Indicators) and Observability relate to
and impact SRE, chaos engineering and performance engineering?
* Depicts the key relationships between chaos engineering and performance testing including test
case generation and management, test data management, overlap in testing, among others.
* Provides views on ways of working & collaboration between the performance engineering
team and chaos engineering teams using value streams & collaboration tools.
* Present organization models with roles and responsibilities for performance engineering and
chaos engineering teams focused on business outcomes.

More:
* What is SRE or Site Reliability Engineering?
* What is SLO and SLI (Service Level Objectives and Indicators)?
* What are the key pillars of an SRE practice?
* What are Application, Platform and Service Performance and Resiliency Metrics?
* How does performance testing and chaos testing overlap and relate to each other?
* Why and how should performance & chaos engineering teams collaborate?

* How should I organize SRE, Chaos and Performance engineering teams in today’s agile, Dev-
Ops enabled and Cloud-Native world?

This is a hands-on workshop that demonstrates using AI to develop more sustainable and improved food supply chains. #softwareForHumanity

In the Sustainable Development Goals report for 2022, the United Nations found that nearly 1 in 3 people lacked regular access to adequate food in 2020. At the same time, nearly 13% of food is lost in the food supply chain from harvesting to transport to storage to processing. When food is wasted, so are the energy, land, and resources that were used to create it. We can use emerging technology to develop more sustainable food chains.
Autonomous robots, artificial intelligence and remote sensing technology can optimize farm operations using precision farming, automate harvesting and grading, and monitor food quality during transportation. Reducing waste at each of these stages increases throughput.

In today’s hyper-connected world, digital ecosystems are the lifeblood of businesses and organizations. From cloud-based applications to complex networks, ensuring that these digital landscapes operate at peak performance while efficiently managing capacity is crucial for success. This topic delves into the art and science of optimizing digital ecosystems. Explore cutting-edge strategies, best practices, and innovative techniques that empower businesses to deliver seamless user experiences, handle ever-increasing workloads, and stay agile in an evolving tech landscape. Discover how effective performance and capacity management can be a game-changer in achieving digital excellence and maintaining a competitive edge.

About 80% of data within organizations is considered being unstructured data that is locked up inside text, emails, PDFs and scanned documents. In this session, you will learn how organizations can take advantage of AWS Intelligent document processing in combination with Generative AI to enhance document processing capabilities, improve ROI and delight customers.

Learning Objectives:

Learn how to power up your Intelligent Document Processing (IDP) pipeline with Generative AI capabilities, using services such as Amazon Textract, Amazon Comprehend, and Amazon Kendra.

Find out about real-world challenges in automating document-intensive business processes such as those in insurance claims processing, loan and mortgage application processing, and others by utilizing Generative AI.

Learn how IDP can help drive up business process efficiency, improve accuracy, and reduce costs.

An “end-to-end performance monitoring” view of an enterprise, is based on the “End User Perspective”, as proposed by the Apdex Alliance*. This includes the “application” perspective, and it means that “Performance Is The User Experience”.
Since the 80/20 Rules Have Flipped, there is a new approach to overall performance monitoring. The old rule said that 80% of your users are in your primary offices and that 80% of you traffic is inside your network. Therefore, if you deliver good service to the 80% you know, then you are well ahead of the game.
The new rule says that 80% of the users are outside your primary offices and that 73% of application service problems are reported by end users, not by the IT department.
This session will show the flow of data, where it gets impacted (cloud, distributed and Mainframe) and how to monitor performance from the “End User Experience” – thus avoiding silo monitoring with a “war room” type analysis.

Join ISG’s Chief Strategy Officer – Prashant Kelker – for a straightforward look at the latest advancements in Generative and Industrial AI. This session, based on a recent research study of over 50 enterprises and technology platforms, will deliver key insights into successful applications and evolving practices in the field. We’ll also discuss emerging architectures and governance patterns in this emerging technology area.

Gain a clear, focused insight into the present status and future possibilities of Generative and Industrial AI, with access to practical strategies and information sourced directly from recent research and application.

Almost ten years ago I started exploring the possibility of moving away from homegrown mainframe performance analytics at the Bank of Montreal. Over a few years I built a long list of possible products and vendors, put together a cross disciplinary team who shared my interest, and eventually selected a vendor and tool to work with. This is (mostly) the story of how we went from long list, to short list, to selecting a single vendor.

Presentation includes the following discussion themes.
– What to scale: servers, databases, containers, load balancers.
– How to scale: horizontally/rightsizing, vertically, manually, automatically, ML based, predictive, serverless.
– Where to scale: AWS (ASG,ECS, EKS, ELB), AZURE, GCP, K8s.
– Why to scale: cost optimization, incidents avoidance, seasonality.
– When to scale: auto-scaling policies and parameters, pre-warming to fight latency, correlating with business/app drivers.

Presentation includes a user case study of scaling parameters optimization: monitoring, modeling and balancing vertical and horizontal scaling, calculating optimal initial/desired cluster size and more.

This session is about best practices and lessons learned after building a cloud-agnostic multi-tenant SaaS application. It will cover topics related to tenant provisioning, passing context in microservices, tenant onboarding with AuthN and AuthZ, data partitioning, DevOps strategies, and cross-cutting concerns.

While ServiceNow has a comprehensive and robust ecosystem for distributed data/components, there are still many challenges for organizations trying to marry IBM Z and ServiceNow. Enterprises infrastructure teams need the ability to discover and map IBM Z resources into ServiceNow to unlock better incident remediation, better visibility across teams, and reduced mean time to repair. In this session you will learn how IBM Z Discovery for ServiceNow CMDB works and why “ChatOps” is becoming so pervasive for AIOps. We’ll discuss how ChatOps can be used for incident management in reducing the mean time to resolution, and why it can be a useful practice for Z shops wanting to leverage ServiceNow as a central source of truth tied to events stemming from mainframe.