Auto-Tuning Compute Resource Allocation for Throttling Control
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Solution Overview
Problem
Existing cloud-based computing resource allocation methods are inefficient due to static allocation and manual adjustments, leading to resource wastage and throttling during fluctuating data processing needs.
Innovation Solution
Implementing a system that automatically and dynamically allocates compute resources based on user-specified thresholds and real-time load monitoring, adjusting resource allocation to match varying compute demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If static computing resources are allocated to each customer, then resource allocation is simple to manage, but resource utilization efficiency deteriorates during fluctuating data processing needs
Solution Approach 1:
The patent implements dynamic resource allocation by continuously monitoring compute load metrics and automatically adjusting resource allocation based on current demand. The system transitions from static to dynamic allocation, where resource thresholds are adjusted in real-time based on monitored compute load, ensuring optimal utilization during traffic fluctuations while maintaining automated management.
Solution Approach 2:
The system employs feedback mechanisms by monitoring compute load metrics and using this information to automatically adjust resource allocation. The monitored compute load feeds back into the allocation decision process, allowing the system to respond to changing conditions and optimize resource utilization dynamically without manual intervention.
2Stability of the object's composition
If static computing resources are allocated to each customer, then resource allocation is stable, but adaptability to changing data processing needs deteriorates
Solution Approach 1:
The system achieves both stability and adaptability through dynamic allocation. Resource allocation remains stable in the sense that automatic adjustment mechanisms are consistently applied, while adaptability improves as the system responds to changing compute load conditions. The monitored compute load triggers automatic adjustments, allowing the system to adapt to traffic patterns while maintaining operational stability.
Solution Approach 2:
The system enables self-service by automatically monitoring compute load and adjusting resource allocation without manual customer requests. The system serves itself by detecting when resources need adjustment and autonomously making allocation changes based on monitored metrics, eliminating the need for customers to manually request resource changes while maintaining stable automated management.
3Measurement precision
If manual resource requests are implemented, then resource allocation accuracy improves, but resource overhead increases
Solution Approach 1:
The system implements self-service by automatically monitoring compute load metrics and adjusting resource allocation without requiring manual customer requests. The system autonomously detects resource needs based on monitored compute load and automatically makes allocation adjustments, eliminating manual overhead while maintaining accurate resource allocation based on actual usage patterns.
Solution Approach 2:
The system uses feedback from monitored compute load to automatically adjust resource allocation. By continuously monitoring compute load metrics and using this feedback to trigger automatic resource adjustments, the system achieves accurate resource allocation based on actual needs without requiring manual measurement or customer requests, thereby reducing management overhead.
4Productivity
If more computing resources are allocated during high traffic periods, then service performance improves, but resource wastage increases during low traffic periods
Solution Approach 1:
The system dynamically adjusts resource allocation based on monitored compute load, allocating more resources during high traffic periods when service performance needs improvement and reducing allocations during low traffic periods to minimize wastage. This dynamic approach ensures resources match actual demand, improving performance when needed while reducing waste during lower utilization periods.
Solution Approach 2:
The system changes resource allocation parameters dynamically based on monitored compute load conditions. By adjusting allocation parameters in response to changing load conditions, the system optimizes service performance during high traffic while minimizing resource wastage during low traffic periods, adapting resource levels to match actual service needs.
Data Source
AI summary
Techniques are disclosed for automated and dynamic compute resource allocation in an infrastructure-as-a-service (IaaS) environment. A system may determine a load threshold value corresponding to a maximum throughput of allocated resources and an active load of processing occurring at those resources. The threshold and load are compared to determine if throttling is occurring at the allocated resources. A specified range of permissible resource allocations is determined. Based on the range of permissible resource allocations, the threshold load value and the active load, the allocated resources may be modified. The modification may be a ramp-up of allocated resources to handle a throttling load or a ramp-down to reduce inefficient resource utilization and processing overhead. The ramp-up or ramp-down may be performed in periodic increments over periodic increments of time to reduce system stress and handle dynamically changing loads. A recommended permissible allocation range may be suggested.