Device Cluster Reassignment via Benchmark Scoring
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Solution Overview
Problem
Existing systems for managing virtual computing instances (VCIs) face challenges in efficiently assigning devices to clusters based on service level agreements (SLAs), as aging devices may no longer meet SLA policies, and new devices' capabilities and popularity scores are not effectively utilized to optimize resource allocation.
Innovation Solution
A system that utilizes a benchmark engine to maintain device metrics, a score engine to calculate device specification and popularity scores, and a cluster engine to reassess and reassign devices to clusters based on SLA policies, ensuring that devices are optimally allocated according to their capabilities and usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If devices are assigned to clusters based on static initial configuration, then device allocation is simple and quick, but aging devices continue to occupy cluster slots even when they no longer meet SLA policies, reducing overall system efficiency
Solution Approach 1:
The system implements periodic reassessment of devices in clusters by scheduling regular evaluation cycles. The cluster engine periodically retrieves benchmark data and recalculates device scores to determine if reassignment is needed, balancing the need for optimal resource utilization with operational efficiency by not requiring continuous monitoring
Solution Approach 2:
The system establishes a feedback loop where benchmark data continuously flows into the score engine, which updates device scores and feeds this information back to the cluster engine. This feedback mechanism enables automatic detection of aging devices that no longer meet SLA requirements and triggers appropriate reassignment actions
2Reliability
If comprehensive benchmark data collection and scoring is implemented for all devices, then optimal device placement according to SLA policies is achieved, but system complexity and computational overhead increase
Solution Approach 1:
The system divides the device management functionality into distinct modular components: a benchmark engine for data collection, a score engine for calculation, and a cluster engine for decision-making. Each component has a specific responsibility and can be independently configured and maintained, reducing overall system complexity while enabling comprehensive device assessment
Solution Approach 2:
The score engine acts as an intermediary between the benchmark data and the cluster assignment decisions. It processes raw benchmark data, applies scoring algorithms, and produces standardized device scores that the cluster engine can use for SLA-compliant placement, simplifying the overall decision-making process
3Productivity
If devices are frequently reassigned based on changing benchmark data, then resource optimization is maximized, but operational stability and service continuity may be compromised
Solution Approach 1:
The system implements dynamic cluster assignments that adapt to changing device capabilities while maintaining operational stability. Devices are reassigned based on updated benchmark scores when they cross predefined thresholds or during scheduled reassessments, allowing the system to optimize resource utilization without requiring constant reconfiguration
Solution Approach 2:
The system buffers against excessive reassignment by implementing threshold-based triggers and scheduled reassessment cycles. Devices are not reassigned for minor score fluctuations but only when they significantly deviate from SLA requirements or during predetermined evaluation periods, maintaining cluster stability while still achieving optimization goals
Data Source
AI summary
The present disclosure is related to methods, systems, and machine-readable media for assigning devices to clusters. The system includes a benchmark engine to collect and store the benchmark data for a device that is assigned to a first cluster of devices. The system includes a score engine to create a device specification score based on the benchmark data and a popularity score based on the benchmark data. The system includes a cluster engine to determine, whether to reassign the device to a second cluster of devices based on a policy of the first cluster of devices, a policy of the second cluster of devices, and at least one of the device specification score and the popularity score.