WORKLOAD LIFECYCLE MANAGEMENTTM for CLOUD
is a human intelligence amplification decision platform that combines human decisions gut feel with hybrid artificial intelligence (AI) data science as a control platform providing for the optimization of cloud compute workloads.
JanusIQTM Analytics Platform
As an aggregator of data from various tools such as cost management, usage analytics, auditing or logging tools among a few, all this data is ingested into our Workload Business ModelingTM tool, as well as additional data into our Service Path ModelingTM tool, creating data models for recommendations specifically on how to use and buy services from Cloud Service Providers better.
In less than 60 days an organization can have their cloud environment fully optimized with the best match per workload and the best price for that workload. Our proprietary processes, technologies and services are part of our tactical approach to discovering issues not typically visible to our clients.
JanusIQTM Broker Platform
With a combination of technologies and professional services, the JanusIQTM Broker Platform validates the architecture for deployment, builds a service work order, generates a final supplier requirements document, creates sourcing options, and supports the execution of the final supplier agreement. The cloud options comprise of current cloud provider, provider partner (if any), hybrid / multi-cloud provider , and two alternate cloud providers, this provides for cloud marketplace comparisons versus current provider.
JanusIQTM Smart System Technology
JanusIQTM Smart System Technology (SST) is an ongoing optimization technology that uses multiple sensors (agents) allowing for the self-managing, self-healing and self-brokering of workloads for a client.
The Smart System Technology is a intelligent multi-agent system that comprises a number of intelligent software agents that can autonomously function and interact in a complex dynamic environment. The agents are intelligent software entities that have the abilities to perceive the environment, reason about own and other agents' goals, strategies and actions, and make decisions about the best course of actions in order to achieve desired outcomes.
Each agent is autonomous as it controls its internal states, decision-making processes, behavior and interactions. The agents can adapt to changes in the environment, behavior of other agents and learn from experience, thus becoming cognitive. They interact with other agents through direct communication or indirectly through their actions on the environment. The overall behavior and performance of the system depends on the individual agents' behaviors and interactions that can often result in an emergent preferred state and behavior of the system.
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