Resource Aggregation Stack for Cloud Scheduling Scalability
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Cloud computing systems face inefficiencies due to unpredictable demand, leading to either underutilization or overutilization of computing resources, resulting in performance degradation and potential service interruptions, as existing technologies struggle to accurately schedule resources across various timescales.
Innovation Solution
The Resource Aggregation (RA) stack, a multi-layered architecture that analyzes historical utilization data to generate interval-scaled data, applies machine learning techniques for resource aggregation, and determines uniform activation determinations based on activation thresholds, enabling prescriptive analytical resource scheduling across multiple timescales and improving scalability and adaptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If computing resources are scheduled with high uncertainty to ensure service availability, then service reliability is improved, but resource utilization efficiency deteriorates due to over-scheduling
Solution Approach 1:
The patent segments resource scheduling into multiple timescales (short-term, medium-term, long-term) with different uncertainty characteristics. Each timescale is handled by specialized models that optimize for their specific time horizons, allowing the system to balance reliability and efficiency by applying appropriate scheduling strategies to different temporal dimensions of resource allocation
Solution Approach 2:
The system dynamically adjusts scheduling parameters including activation thresholds and resource allocation quantities based on predicted demand uncertainty. By changing these parameters adaptively across different timescales and uncertainty levels, the system optimizes both service reliability and resource utilization efficiency simultaneously
2Productivity
If computing resources are scheduled with low uncertainty to optimize resource utilization, then resource utilization efficiency is improved, but service reliability deteriorates due to insufficient resource allocation
Solution Approach 1:
The patent divides the scheduling problem into multiple timescale segments, each with its own uncertainty model and optimization objectives. Short-term scheduling focuses on immediate resource allocation for efficiency, while long-term scheduling addresses strategic resource provisioning for reliability, with intermediate timescales bridging the two objectives
Solution Approach 2:
The system performs preliminary resource allocation and activation decisions at longer timescales before actual demand occurs. By pre-positioning resources and setting activation thresholds in advance based on predictive models, the system ensures both efficient utilization and sufficient availability when demand materializes
3Measurement precision
If individual computing resources are managed separately with high granularity, then scheduling precision is improved, but system complexity increases making scalability difficult
Solution Approach 1:
The patent merges individual resource management into aggregate resource groupings that are handled at higher levels of the multi-timescale scheduling hierarchy. By combining similar resources into manageable units and applying unified scheduling policies at appropriate timescales, the system maintains scheduling precision while reducing overall system complexity and improving scalability
4Reliability
If computing resources are over-scheduled to prevent service interruptions, then service reliability is improved, but energy consumption increases due to unnecessary resource activation
Solution Approach 1:
The system dynamically changes activation thresholds and resource allocation parameters based on predicted demand and uncertainty levels. By adjusting these parameters adaptively, the system activates resources only when genuinely needed for reliability, avoiding unnecessary energy consumption from over-scheduling while maintaining service availability
Solution Approach 2:
The multi-timescale scheduling system incorporates feedback from actual resource utilization and demand patterns to continuously refine activation decisions. This feedback mechanism allows the system to learn from past over-scheduling errors and optimize future resource activation to balance reliability requirements with energy efficiency
Data Source
AI summary
A multi-layer resource aggregation (RA) stack may generate prescriptive activation timetables for controlling activation states for computing resources. To facilitate operator control and adjustment, the RA stack may, at an aggregation engine layer, aggregate the computing resource into one or more resource aggregates. The computing resources within the resource aggregates may have similar individual activation prescription patterns. Machine learning techniques may be used by the RA stack to identify these related individual activation prescription patterns and aggregate the computing resources accordingly. Once aggregated, the RA stack may make a uniform activation determination for the aggregates as single units. Therefore, the computing resources within the aggregate may be controlled and/or adjust together. Thus, the RA stack increases the scalability of implementation of prescriptive computing resource activation state determinations.