Predictive VM Preloading for Collaborative Activity Latency
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Solution Overview
Problem
Traditional virtualization technologies face challenges in efficiently loading large collaborative content, leading to unacceptably long delays for users, and inefficient resource utilization, while also requiring secure and fair access to resources among users.
Innovation Solution
A virtualization-based collaborative activity framework that predicts content access patterns to preloaded virtual machines, allowing for efficient resource allocation, secure access, and dynamic control transfer among users, while monitoring progress and managing resource usage to optimize user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If virtual machines are preloaded with collaborative content, then user access latency is reduced, but computing resource utilization becomes inefficient
Solution Approach 1:
The system performs preliminary actions by predicting which collaborative content will be accessed next and preloading the corresponding virtual machine images into memory before users actually request them. This predictive preloading reduces user-perceived latency while avoiding the waste of loading all possible content, thereby resolving the contradiction between fast access and efficient resource use.
2Speed
If all collaborative content is loaded into virtual machines, then user access speed is improved, but resource consumption increases excessively
Solution Approach 1:
Instead of uniformly loading all collaborative content for all users, the system applies local quality by selectively loading only the specific virtual machine images that individual users are predicted to need. Each user receives customized preloading based on their predicted access patterns, optimizing memory usage while maintaining fast access speeds for relevant content.
3Ease of operation
If predictive preloading is implemented, then user experience is improved, but system complexity increases
Solution Approach 1:
The system implements self-service by using machine learning models that automatically analyze user interaction data and predict future content access patterns without requiring manual configuration. The predictive preloading mechanism autonomously decides what to load and when, improving user experience while keeping the operational complexity manageable through automation rather than manual system management.
Data Source
AI summary
Methods, systems, and apparatus, including medium-encoded computer program products, for virtualization-based collaborative activity framework with predictive preloading of virtual machines. A collaboration orchestration system can obtain user interaction data describing interactions with a client device. In response to determining, at least in part from the user interaction data, that the client device is predicted to access an activity, and before receiving a request to access the activity, the collaboration orchestration system can: (i) provide an indication to create a virtual machine (VM) instance configured to provide the activity; and (ii) provide an indication to load the VM instance with the activity. The collaboration orchestration system can receive a reference to the VM instance. In response to obtaining, by the collaboration orchestration system, a request to access the activity from the client device, the collaboration orchestration system can provide to the client device, the reference to the VM instance.