Capacity Manager for Automated Resource Allocation in Data Centers
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Solution Overview
Problem
Managing resource-on-demand systems, such as grid computing and utility data centers, is labor-intensive and inefficient due to the manual nature of resource allocation and monitoring, often resulting in ineffective resource utilization and costly management tasks.
Innovation Solution
A system and method for allocating resources using a capacity manager that simulates workloads to determine required capacity, considering constraints and objectives, and employs algorithms like objective attainment and scoring to optimize resource assignment, ensuring efficient utilization and compliance with service level agreements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual resource allocation is used, then resource assignment can be performed, but management time and labor increase significantly
Solution Approach 1:
The system enables self-service resource allocation by allowing users to automatically request, select, and assign resources from a pool without manual intervention from administrators. The automated resource manager handles the entire allocation process, including monitoring and reassignment, transforming a manual service into an autonomous system that reduces management time and labor requirements
2Ease of operation
If manual resource selection is performed, then resource assignment decisions can be made, but the complexity of management increases
Solution Approach 1:
The system introduces an automated resource manager as an intermediary between users and the resource pool. This intermediary handles the complex decisions about resource selection, allocation, and monitoring, shielding users from the underlying complexity while providing simple resource assignment operations. The intermediary automatically manages the complexity of resource management tasks
3Reliability
If conventional management tools are used, then resource monitoring can be performed, but they are not designed to handle resource-on-demand systems
Solution Approach 1:
The system implements dynamic resource allocation that adapts to changing demands in real-time. Resources are automatically reassigned based on current workload requirements, and the system dynamically adjusts allocation decisions to match evolving resource-on-demand characteristics. This dynamic approach enables the system to adapt to varying conditions without requiring manual reconfiguration
Data Source
AI summary
Constraints on assigning applications to resources are determined. The applications are divided into sets, wherein applications in a set are related by at least one of the constraints. Variations on assigning the applications in the sets to resources are determined. Feasible variations are determined from the variations for each set, wherein the feasible variations satisfy constraints on the applications for each set. The feasible variations are simulated.