Omnichannel AI Support for Data Center Capacity Planning
Keywords:
Cyclic demand, Data center support, Demand shaping, Enterprise AI, Graph neural networks, Machine learning, Omnichannel, Operational logs, Resources, Simulation, Social media, System telemetry, Telemetry, Thermal support, Time-series forecasting, Traffic and latency, Traffic pattern, Two-sided marketplace.Abstract
The operation of global data centers as interconnected systems requires capacity planning that considers activity across all centers and supports across all channels. An omnichannel approach incorporates AI to support capacity planning in an integrated way, utilizing telemetry signals from the physical infrastructure and operational signals from the support systems that cover edge, on-premises, and cloud footprints. Anomalies in observed behavior are detected and analyzed using machine learning techniques. Elastic demand forecast models take into account external market influences and demand-shaping initiatives, while simulations support what-if analysis for capacity planning. Cross-channel support for capacity planning considers catastrophe preparedness and outage readiness, optimizes resource scheduling and queuing across centers, and leverages automation with a human-in-the-loop governance model. Bandwidth, power, and cooling are planned using sustainability metrics and renewable integration to contribute to business operations while enabling the organization’s net-zero commitment.
AI is prevalent across the business ecosystem and appears poised to disrupt every industry. The leadership of business enterprises across regions are beginning to embrace AI solutions and turn to partners for the skill sets and governance frameworks for securing maximum value. The synergy of harnessing omnichannel signals with AI techniques enables new use-case opportunities for the offer portfolio of partnerships across industries. Such signals have been used to develop AI techniques for operational elements of the data center ecosystem.
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