Automating Capacity Management in Virtual, Cloud & Software Defined Datacenters

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1 Automating Capacity Management in Virtual, Cloud & Software Defined Datacenters Andrew Hillier CTO, CiRBA Inc.

2 Capacity Challenges Increasingly Dynamic Access to Alternatives Complex Low Utilization Application Demand Infrastructure Supply Compute Storage Network Software

3 Basic Challenges in Aligning Supply and Demand Workloads come in many different shapes and sizes, and have different requirements How they land in an environment can lead to inefficiency and operational risk Server Capacity Workload Demands

4 Getting the Real Answer Policy True spare If we analyze the shapes and sizes of the demand, and all the different ways they can be placed together, it is possible to defragment the capacity The problem: virtualization tools and cloud management platforms don t do this it requires more advanced analytics... Server Capacity

5 Properly Analyzing Supply and Demand Risks Inefficiencies Clusters Policy-Based Goal Posts Hosts Guests

6 Properly Analyzing Supply and Demand Eliminate Spare Capacity Reduce Operational Risk Is This Demand Management? No Actions to Move to Center

7 Different Types of Demand Self-Service Requests Enterprise App Deployments Migration & Consolidation Existing Applications Dynamic Low Lead Time Process-Centric High Lead Time Project-Driven Large Scale New Options Hybrid, Bursting, Capacity Supply

8 Self-Service and Demand Management Demand Capture, not Demand Management Input user requirements and start VMs Self-Service Requests Not Actually Demand At All Captures Supply that user thinks is needed Forces users to speak language of infrastructure Dynamic Low Lead Time Cloud Stacks are not equipped to cope with this Not good at managing allocations, placements Leads to downstream problems Capacity Supply

9 Real-World Example: The Impact of Self-Service Too Much Capacity Overall Imbalances in Workload Placement Under-Sized VMs Over-Sized VMs

10 How They Deal With This Request Console Is This Demand Management? No Control Console Provisioning Process Workload Routing API Placement Analytics Internal Cloud Infrastructure Create Start/Stop Destroy Intelligent Initial Placement VMM Operational Metrics Automated Rebalancing & Resizing VMM Intelligently Place VMs & Allocate Resources Optimize Density & Minimize Operational Risk

11 Expanding Demand, Expanding Supply Self-Service Requests Enterprise App Deployments Migration & Consolidation Existing Applications Dynamic Low Lead Time Process-Centric High Lead Time Project-Driven Large Scale New Options Hybrid, Bursting, Capacity Supply Enterprise Capacity Supply (on/off prem)

12 The Challenge This Creates New Application or Cloud Request EXCEL EXCEL Windows OS Requires SQL server Has customer data Needs Gold tier storage Must run on east coast EXCEL EXCEL RE-KEY VMware Hyper-V IBM Power

13 Scientifically Routing & Placing These Demands New Application or Cloud Request EXCEL EXCEL Windows OS Requires SQL server Has customer data Needs Gold tier storage Must run on east coast EXCEL EXCEL RE-KEY VMware Hyper-V IBM Power

14 Lesson Learned: It Isn t Just About Capacity

15 From a Process Perspective Request Console Reservation Console Control Console Complex Requests Provisioning Process Workload Routing API Placement Analytics Internal Cloud Infrastructure Create Start/Stop Destroy Intelligent Initial Placement VMM Operational Metrics Automated Rebalancing & Resizing VMM Intelligently Place VMs & Allocate Resources Optimize Density & Minimize Operational Risk Scientifically Manage Enterprise Demand

16 From an Organizational Perspective Capacity Management & Provisioning Demand Deployment planning Growth forecasting Business interaction Supply Provisioning Maintenance Procurement Establishing this boundary greatly simplifies the move to cloud operational models

17 From an Application Perspective

18 Conclusion Taking a scientific approach to aligning supply and demand yields huge savings Average Increase in VM Density Average Hardware Savings 48% 33% Average Software License Savings 55% Reduced Capacity related risk This must include a broad view of all application demand, where it comes from, and how it maps to internal & external infrastructure supply

19 Thank You

20 Abstract Getting Control Over Application Demand in Enterprise Cloud: Lessons Learned from Early Adopters The world s largest and most successful private cloud operations are revolutionizing their approach to demand management. These organizations have recognized that while self-service portals are a component in the overall cloud architecture, these tools do not enable demand management. In fact, in many cases the portals and end-user interfaces don t actually capture anything to do with demand, but instead force the user to enter the capacity supply requirements that they think will meet their demands. This is very different. Large enterprises have recognized the need to look beyond immediate requests to also model the pipeline of new demands that will be coming down the road. It is only by capturing new immediate requirements, an understanding of the pipeline and what is running in environments that organizations can possibly hope to accurately model demand and properly allocate compute, storage and network resources. Join Andrew Hillier as he discusses these topics, and covers important factors such as: Why self-service is not the same as demand management How to properly model inbound workload requirements Understanding spare capacity and hosting environment capabilities across compute, storage and network Why the fulfillment of cloud requests must factor in these capabilities and requirements How to provide LOB visibility and enable capacity reclamation

21 Conclusion Operational success in virtual, cloud and software defined data centers is completely dependent on the ability to intelligently balance capacity supply with application demand Decisions are becoming far too complex to leave to manual processes and simplistic/outdated technologies An analytics-driven approach is key as we move to cloud, SDx and other emerging technologies