Could predictive insight and prescriptive action through artificial intelligence (AI) and machine learning (ML) drive positive business outcomes versus historically used capacity and cost optimization methods? Probably!
Here’s why.
The emergence of new technologies such as multi-cloud and hybrid cloud, along with the increased deployment of applications based on Kubernetes, micro-services, containers, and Pods, have dramatically increased IT complexity. The result is a myriad of capacity management and cost tools that are not meant to work together. That makes it difficult to get a clear picture of how current resources are performing to support applications and business services, and how to align them with business initiatives.
A recent IDG survey1 of IT decision makers documents how IT professionals must juggle multiple tools to manage the capacity and cost of their hybrid cloud environments, inhibiting end-to-end visibility, slowing remediation response time, and making it harder to accurately anticipate demand. In fact, 76 percent of those surveyed admitted they were using a combination of cloud-based and on-premises capacity management solutions, with 63 percent using both on-premises and cloud-based IT cost-management solutions.
Organizational disruption is also increasing IT complexity. According to BMC Software product manager Dennis Newberry, “Over the last several years, there is more development being done within the lines of business and not within centralized IT. Where your long-term planning functions and infrastructure optimization occurred in IT, now it is happening outside of that core function. That amplifies the challenge of obtaining comprehensive visibility into those environments…because you have to work across a larger organization.”
What’s the answer?
To get there, you need a comprehensive, integrated, solution that increases visibility and facilitates effective planning for changing business demands. The ideal solution leverages input from across the enterprise, integrated with best-in-class optimization tools driven by AI, machine learning (ML), predictive analytics, and business key performance indicators (KPIs) to help IT and lines of business align IT resources with increases in business demand.
“What if” modeling can help. For example, simulations for cloud workload migrations and Kubernetes workload placement provide prescriptive insights into resource configurations, locations, and cost to inform decision-making and prevent application performance slowdowns or failures.
The benefits of predictive insight and prescriptive action using AI, ML, and advanced analytics include:
- Accurate and efficient rightsizing of resources aligned with demand and budget
- Reduced capital and operating expenditures (Capex/OpEx)
- Increased service performance, service level agreements (SLAs), innovation, and agility
To learn more, download the e-book, Continuous Optimization: The Next Evolution in Capacity, Resource and Cost Optimization.
Thanks for reading.
1 IDG Whitepaper: Meeting the Challenges of Optimizing IT Cost and Capacity Management ↩
