IT Excellence

OVERVIEW

AIOps brings together service management, performance management and automation to realize continuous insights and improvement. It manages and analyzes data from an application using machine learning analytics technology to simplify IT operations management and automate problem resolution.

AIOps seeks to address a quickly evolving IT landscape using the convenience of machine learning, automation and big data.

AIOps harnesses big data from operational appliances and has the unique ability to detect and respond to issues instantaneously.

AIOps gives anyone across DevOps, SRE, platform engineering, ITOps and development the data they want with the context they need. It is not limited to a select set of power users.

REAL-TIME OBSERVABILITY FOR EVERYONE AND ANYONE

Instana

IBM Instana Observability is an Enterprise Observability Platform powered by fully automated Application Performance Management. It ingests all observability metrics, traces every request and profiles every process, correlating and analyzing the data so DevOps teams can optimize their application performance, especially microservices and cloud-native applications, and accelerate pipelines.

IBM Instana is democratizing observability with an automated monitoring, comprehensive data model and powerful semantics, coupled with deep analytics,which is giving stakeholders the actionable insights needed to keep services running at optimum levels.

BENEFITS

WHY INSTANA?

All application stakeholders, from DevOps and SRE to ITOps, Platform Engineering, Dev and even business side users - get the data they want with the context they need.
Applications are automatically discovered and monitored (no reboots, no labels, no tagging)
Full-stack context is automatically discovered with 1-second granularity and an end-to-end trace of every call
Automatic rollbacks and incident remediation can be triggered by intelligent action (before incidents impact end users).
Real-time detection and mapping of all interdependencies reduces risk and decreases MTTR (Mean Time to Restore) by ensuring that you’re always looking at accurate information.
Intelligent action,resolve issues faster with an understanding of contributing factors. Analyze every user request from any perspective to quickly resolve bottlenecks and optimize performance.

AUTOMATE CLOUD APPLICATION RESOURCING

Turbonomic

With IBM Turbonomic you can continuously automate cloud application resourcing and see exactly how it improves end-user experience… bridging the gap between App and Cloud Teams.

What if you could?

Pay for what you need, not just what you (over) allocate?

Prevent performance issues before they impact user experience?

Close operational gaps between App and Cloud Teams?

Achieve cloud elasticity with automation?

WHAT CAN YOU DO WITH IBM TURBONOMIC?

Optimize the performance and cost of your IT infrastructure, including public, private and hybrid cloud environments.

Improve IT staff productivity by simplifying complex tasks, automating decision-making, and gaining time to innovate.
Optimize public cloud spend by consuming and paying only for what the business really needs, while assuring application performance
Accelerate and de-risk multicloud projects by safely and efficiently migrating workloads between premises and the public cloud to reduce cost and increase efficiency.
Safely increase infrastructure utilization by eliminating infrastructure as the cause of application performance degradation, without overprovisioning.
Accelerate consolidation and de-risk IT transformation projects across heterogeneous estates.

BENEFITS

Why Turbonomic?

Public cloud optimization	Kubernetes optimization	Data center optimization	Sustainable IT
VM rightsizing	Container rightsizing	Continuous compute placement	Container rightsizing
Storage volume configuration	Pod moves	VM rightsizing	Pod moves
Database configuration	Cluster scaling	Continuous storage placement	Cluster scaling
Maximize RI coverage	Container platform planning	Superclusters	Container platform planning
Optimize RI purchasing	SLO-driven scaling	Initial placement	SLO-driven scaling