Contextual Bandit Workload Provisioning for Cloud Resource Trade-Offs
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Solution Overview
Problem
Current methods for managing workload resources in cloud computing services are inefficient, leading to either insufficient or excessive resource provisioning, resulting in reduced functionality, inoperability, or wasted costs.
Innovation Solution
Utilizing a contextual bandit machine learning model to proactively determine optimal processing configurations for workloads on remote processing systems, balancing exploration and exploitation to minimize resource usage while meeting workload requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If resources are reserved for workloads, then workload reliability is improved, but resource utilization efficiency deteriorates due to idle reserved capacity
Solution Approach 1:
The patent implements dynamic resource allocation by transitioning from static reserved capacity to on-demand provisioning. The system continuously monitors workload performance metrics and automatically adjusts resource allocation in real-time, allowing resources to be dynamically assigned to workloads that need them while being released when not needed, thus resolving the contradiction between reliability and utilization efficiency.
Solution Approach 2:
The system enables workloads to self-provision resources based on their actual needs through automated monitoring and adjustment mechanisms. The workload performance is continuously tracked, and resource allocation is automatically optimized without manual intervention, allowing the system to self-adjust between reserved and on-demand modes to maintain both reliability and efficiency.
2Loss of energy
If spot resources are used for workloads, then resource utilization efficiency is improved, but workload reliability deteriorates due to interruptions
Solution Approach 1:
The patent implements a feedback mechanism that continuously monitors workload performance and resource availability. When spot resources are being used, the system detects performance degradation or interruptions and automatically triggers a transition to more reliable resource types. This closed-loop feedback system allows the workload to benefit from efficient spot resources when available while maintaining reliability through automatic fallback to reserved or on-demand resources.
3Productivity
If auto-scaling is applied to workloads, then productivity is improved, but system complexity increases due to individual workload management
Solution Approach 1:
The patent implements a universal resource management system that handles multiple workload types and scaling requirements through a single centralized platform. Instead of managing auto-scaling individually for each workload, the system provides a unified interface and control mechanism that can manage diverse workloads collectively, reducing operational complexity while maintaining high productivity through automated scaling capabilities.
4Reliability
If resources are over-provisioned for workloads, then workload reliability is improved, but cost efficiency deteriorates due to wasted resources
Solution Approach 1:
The patent implements just-enough resource provisioning by continuously monitoring workload performance and allocating resources based on actual needs rather than over-provisioning. The system provisions resources partially, only to the extent necessary to meet workload requirements, and automatically adjusts allocation to prevent both over-provisioning and under-provisioning, thereby optimizing cost efficiency while maintaining adequate reliability.
Data Source
AI summary
Certain aspects of the disclosure provide systems and methods for receiving a request to process a workload on a remote processing system; determining one or more workload requirements associated with processing the workload; and processing the one or more workload requirements with a contextual bandit machine learning model to generate a processing configuration for the remote processing system. The remote processing system provisions resources based on the processing configuration and processes the workload.