Cloud Workload Prediction via Calendar Data Analysis
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Solution Overview
Problem
Conventional approaches to managing resources in cloud computing are inefficient and costly, as they lack effective methods for predicting workload and dynamically scheduling resources based on user requests, leading to suboptimal resource allocation and increased operational expenses.
Innovation Solution
A method and system that collect calendar data from user devices to predict workload and dynamically schedule servers, dividing resources into parts for planned and unplanned requests, with power management techniques to optimize resource utilization and reduce costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional resource management approaches are used in cloud computing, then system simplicity is maintained, but resource allocation efficiency deteriorates and operational costs increase
Solution Approach 1:
The system performs preliminary actions by collecting calendar data from user devices and analyzing it to predict future workload patterns. This allows the cloud computing system to proactively allocate resources before requests arrive, rather than reactively allocating resources after demand is detected. The workload prediction component uses historical calendar data to forecast resource requirements, enabling advance preparation and optimization of resource allocation.
Solution Approach 2:
The system implements dynamic resource allocation by continuously adjusting server scheduling based on predicted workload patterns. The resource management component dynamically schedules servers to match forecasted demand, transitioning resources between active and standby states as needed. This dynamic approach allows the system to adapt resource allocation in real-time based on predicted user requests, improving efficiency while reducing unnecessary power consumption during low-demand periods.
2Productivity
If dynamic server scheduling based on calendar data is implemented, then resource allocation efficiency improves and costs reduce, but system complexity increases
Solution Approach 1:
The system segments the cloud computing infrastructure into distinct server groups that can be independently scheduled and managed. By dividing the server pool into manageable units, the system can apply different scheduling strategies to different segments based on workload predictions. This segmentation simplifies the overall management complexity while enabling fine-grained control over resource allocation and efficient load balancing across server groups.
Solution Approach 2:
The system introduces a workload prediction component as an intermediary between user devices and the cloud computing infrastructure. This intermediary analyzes calendar data from user devices and generates workload predictions that guide resource allocation decisions. By placing this intelligent mediator layer, the system automates complex scheduling decisions without requiring direct complex interactions between users and infrastructure, thereby improving service efficiency while managing system complexity through abstraction.
3Loss of energy
If resources are allocated based on predicted workload, then operational costs reduce, but the ability to handle unexpected requests may deteriorate
Solution Approach 1:
The system changes operational parameters by adjusting server scheduling based on predicted workload patterns. During periods of low predicted demand, servers are scaled back or placed in lower-power states to reduce operational costs. During periods of high predicted demand, servers are activated or scaled up to handle the anticipated load. This dynamic parameter adjustment allows the system to optimize costs while maintaining the flexibility to respond to actual demand variations, including unexpected requests, by modifying resource allocation parameters in real-time.
Data Source
AI summary
Technologies are generally described for systems and methods effective to efficiently schedule a workload in a cloud computing system. In one example, calendar data is collected from respective sets of devices associated with respective sets of subscribers and a workload to be performed at a specific time or range of time is predicted based in part on an analysis of calendar data. Moreover, timing data associated with a set of predicted requests is determined based on the analysis and at least a portion of cloud computing resources that service the set of predicted requests are dynamically scheduled based on timing data.