Dynamic Resource Allocation for Cloud Database Workloads
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Solution Overview
Problem
Automating dynamic resource allocation for cloud-based database services is challenging due to complex workload prediction and varying resource requirements, making it difficult for tenants and service providers to efficiently manage resources and balance workloads in a cloud platform.
Innovation Solution
A method that analyzes traffic patterns and resource relationships to generate a consumption model, enabling dynamic allocation, reallocation, and release of resources in a cascading manner to meet processing requests, using modules like Client Traffic Analyzer, Resource Relation Analyzer, Multi Resource Usage Predictor, and Multi-Resource Manager.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If resources are pre-allocated to ensure sufficient capacity for processing requests, then service reliability is improved, but resource utilization efficiency deteriorates due to idle resources during low-demand periods
Solution Approach 1:
The patent implements dynamic resource allocation where the resource pool size and composition are adjusted in real-time based on actual workload demands. The system monitors traffic patterns and automatically scales resources up or down, transforming the static pre-allocation model into a dynamic adaptive model that maintains reliability while optimizing utilization efficiency.
Solution Approach 2:
The system employs self-service mechanisms through automated workload prediction and resource provisioning. The workload prediction module forecasts future demands and triggers automatic resource allocation without manual intervention, enabling the system to self-regulate resource levels to match actual needs, thereby maintaining service reliability while eliminating waste from over-provisioning.
2Loss of energy
If manual resource management is used to optimize resource allocation, then resource utilization efficiency is improved, but operational complexity and time consumption increase
Solution Approach 1:
The patent replaces manual mechanical resource management operations with an automated information processing system. The workload prediction module uses algorithms to analyze traffic patterns and generate resource allocation decisions automatically, substituting human manual planning and adjustment with computational automation that operates continuously without time loss.
Solution Approach 2:
The system implements feedback loops where resource allocation decisions are continuously monitored and adjusted based on actual performance metrics and workload changes. This automated feedback mechanism enables real-time optimization of resource utilization without requiring manual intervention, maintaining high efficiency while eliminating the time consumption associated with manual management cycles.
3Speed
If resources are allocated for every potential request to ensure immediate processing, then processing speed is improved, but resource waste increases during low-demand periods
Solution Approach 1:
The system performs preliminary actions by pre-warming the resource pool based on workload predictions before actual demand occurs. The workload prediction module forecasts upcoming processing needs and proactively allocates resources in advance, ensuring immediate processing capability when requests arrive while avoiding the waste of maintaining full resources during low-demand periods through predictive rather than static provisioning.
Data Source
AI summary
A method, computer system, and a computer program product is provided for dynamic allocation of resources. A database access pattern is determined by analyzing and monitoring traffic pattern between a pool of resources and a plurality of clients. The relationship between each of the resources is also determined. Access is enabled to a plurality of resources based on the database access and resource relationships, so that the plurality of resources can be accessed but not allocated until processing a request. A consumption model is generated that predicts resource need during a processing request based on the resource relationships, traffic pattern and resources availability. Upon receipt of a subsequent request for processing, consumption model is used to predict resource needs and to dynamically allocate, re-allocate and release the plurality of resources in a cascading manner until completion of subsequent processing request.