Service Level Management for Real-Time Collaboration Sessions
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Real-time collaboration sessions face inefficiencies due to manual provisioning of collaboration services, which can disrupt the session and incur unnecessary server resource costs from pre-provisioning all services, even if not used.
Innovation Solution
The Service Level Management (SLM) process identifies use data associated with a collaboration session, using multi-factor regression analysis to pre-provision only the necessary collaboration services based on participant data, duration of use, and group associations, thereby optimizing resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If all collaboration services are pre-provisioned for a collaboration session, then the services are immediately available for use, but server resources are wasted on services that may not be used
Solution Approach 1:
The system performs preliminary provisioning of collaboration services based on predictive analysis of use data before the collaboration session begins. This allows services to be ready for immediate use when needed, while avoiding the waste of provisioning all possible services regardless of actual usage patterns.
Solution Approach 2:
The system changes the provisioning parameter from a static 'all-or-nothing' approach to a dynamic approach based on calculated probabilities. Services are provisioned based on their likelihood of being used, derived from historical use data, participant information, and session characteristics.
2Loss of energy
If collaboration services are manually provisioned on an as-needed basis, then server resources are optimized, but the session fluidity is disrupted and provisioning takes time
Solution Approach 1:
The system performs preliminary provisioning of collaboration services based on predictive analysis of use data before the collaboration session begins. This allows services to be ready for immediate use when needed, while avoiding the waste of provisioning all possible services regardless of actual usage patterns.
Solution Approach 2:
The system automatically provisions services based on predictive algorithms without requiring manual intervention. The provisioning process is self-managing, using historical data and session characteristics to determine which services to provision, eliminating the time loss associated with manual provisioning decisions.
3Reliability
If pre-provisioning is performed for all possible collaboration services, then no service is unavailable when needed, but the cost and resource consumption increase significantly
Solution Approach 1:
The system changes the provisioning parameter from a static 'all-or-nothing' approach to a dynamic approach based on calculated probabilities. Services are provisioned based on their likelihood of being used, derived from historical use data, participant information, and session characteristics.
Solution Approach 2:
The system performs preliminary provisioning of collaboration services based on predictive analysis of use data before the collaboration session begins. This allows services to be ready for immediate use when needed, while avoiding the waste of provisioning all possible services regardless of actual usage patterns.
Data Source
AI summary
A method, computer program product, and computer system for launching a collaboration session between a plurality of participants. Use data associated with the collaboration session may be identified. One or more collaboration services may be pre-provisioned with the collaboration session based upon, at least in part, the use data.


