Optimizing Virtual Machines with Embedded and Edge AI

To reduce investments in expensive hardware, IT operators are increasingly adopting virtual machines (VMs) as integral components within the IT infrastructure. VMs also allow operators get more output from a reduced number of physical servers. This trend will continue into the foreseeable future.

Virtual Machine Challenges

The proliferation of VMs has presented significant challenges within the IT segment. To maximize VM advantages, the following issues must be addressed:

• Observability: Many VMs deployed today lack the AI-enabled observability required to produce deep insights into performance and trend analysis.
• Performance: VM performance is often sub-optimized due to limitations in power and memory and crippling data latency.
• Data Costs: VM data transmission cost can be prohibitive due to the necessity to transmit all data to the cloud for, synthesis, analysis, and reporting.
• Security: VM security can be a daunting challenge due to:
  • Ransomware vulnerability that can cripple an entire VM ecosystem
  • Poor access control that exposes VMs to malicious actors
  • VM proliferation that exceeds existing monitoring mechanisms
  • Heavy reliance on cloud support that exposes sensitive data to cyber-attack during cloud transmission

Optimizing with Artificial Intelligence

MicroAI brings its AtomML™ and AtomML+™ products to IT operators looking to utilize embedded (AtomML) or edge (AtomML+) intelligence to optimize the performance and security of their VM infrastructures. MicroAI’s breakthrough technology provides a host of new capabilities:

• Tailored for Low-Resource Devices: Machine learning that is designed specifically to run on VMs with limited memory and compute capacity.
• Agentless Intelligence: AtomML+ is an agentless technology that collects and streams asset data from multiple endpoints into one centralized location.
• VM Ecosystem Observability: AtomML+ has the unique ability to provide real-time performance observability into an individual VM or into large groups of VMs within the IT environment.
• Predictive Analytics: Multivariate analysis is performed across large volumes of input channels to generate predictive analytics related to VM performance and security.
• Reduced Cloud Dependence: The amount of data sent to the cloud is reduced by ~ 80%, significantly reducing transmission cost and latency disruptions.
• Hardened Security: Enhanced cyber security that eliminates external software support systems, reduces exposure of data to peripheral functions, provides real-time intrusion alerts, and centralizes the monitoring of VM assets.

A New VM Paradigm

By leveraging the power of MicroAI’s embedded and edge AI solutions, IT operators will experience transformational improvements in the performance of their VM ecosystems. Improvements that were once incremental can now be realized in weeks. Benefits will include:

• Maximized performance of VMs via AI/ML-enabled behavioral learning, personalized observability, anomaly detection, and predictive analytics.
• Improved VM infrastructure efficiencies via asset-specific and asset ecosystem intelligence that enables consolidation of VMs to eliminate redundant or poor-performing assets.
• Lowering of VM data transmission costs by reducing the amount of data that must be transmitted to the cloud (savings in the hundreds of thousands of dollars).
• Next-level cyber security that provides hardened protection against the debilitating effects of todays cyber-attacks (react in seconds instead of days).
• All the above will combine to provide IT operators with differentiating performance, improved operational efficiency, reduced costs, improved bottom lines, improved security, and reduced risk.