Server Anomaly Detection for Resource Allocation
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Solution Overview
Problem
Existing server systems face challenges in dynamically reallocating resources to maintain high-quality service, as over- or under-allocation of resources can lead to data packet losses and degraded user experiences due to insufficient identification of optimal resource utilization.
Innovation Solution
An anomaly detection system that generates models based on recent and historical resource utilization data to identify abnormal behavior, allowing for timely reallocation of server resources by comparing current utilization with expected measures, thereby preventing packet loss and improving user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If server resources are statically allocated, then resource allocation is simple, but service quality degrades under varying load conditions
Solution Approach 1:
The patent implements dynamic resource reallocation by continuously monitoring server load and automatically adjusting resource distribution based on real-time conditions. The system transitions from static allocation to dynamic allocation mechanisms that respond to changing service demands, maintaining optimal service quality without manual intervention.
Solution Approach 2:
The system employs feedback loops that monitor service quality metrics and resource utilization patterns. This feedback information is used to automatically adjust resource allocation decisions, creating a closed-loop control system that continuously optimizes service delivery based on actual performance data.
2Reliability
If server resources are over-allocated, then service quality is maintained, but resource waste increases
Solution Approach 1:
The patent dynamically adjusts resource allocation parameters based on monitored service conditions. By changing allocation parameters in response to actual service demand and quality metrics, the system avoids both over-allocation (resource waste) and under-allocation (service degradation), optimizing the balance between service quality and resource utilization efficiency.
Solution Approach 2:
The system performs self-adjustment of resource allocation based on internal monitoring of service quality and load conditions. This self-service capability eliminates the need for manual resource management while automatically optimizing the balance between maintaining service quality and preventing resource waste through data-driven decision-making.
3Loss of energy
If server resources are under-allocated, then resource waste is reduced, but packet loss increases
Solution Approach 1:
The system performs preliminary monitoring and analysis of service quality trends and resource utilization patterns. By anticipating future resource needs based on historical data and predicted service demands, the system proactively adjusts allocation before packet loss occurs, preventing harmful effects rather than reacting to them after they manifest.
Solution Approach 2:
The system continuously monitors packet loss rates and service quality metrics, using this feedback to automatically adjust resource allocation. When under-allocation causes packet loss, the feedback mechanism triggers reallocation adjustments to restore optimal service levels, creating a responsive system that prevents resource inefficiency while maintaining service quality.
4Reliability
If real-time resource monitoring is implemented, then service quality is maintained, but system complexity increases
Solution Approach 1:
The system implements self-service monitoring and self-adjustment capabilities that automatically manage resource allocation without external intervention. This self-service approach consolidates monitoring and control functions within the system itself, reducing the need for separate complex management infrastructure while maintaining high service quality through automated decision-making.
Solution Approach 2:
The patent replaces manual resource management mechanisms with automated software-based monitoring and control systems. By substituting mechanical or manual resource allocation processes with digital monitoring and algorithmic decision-making, the system achieves real-time optimization while keeping the overall architecture manageable through software abstraction layers.
Data Source
AI summary
Methods and systems for performing operations comprising: accessing one or more data objects including a data set that has been collected over a given span of time, the data set representing a plurality of parameters corresponding to resource utilization of a given server; computing first and second statistical measures based on the plurality of parameters; obtaining current resource utilization corresponding to at least a subset of the plurality of parameters; determining a first condition in which values of the current resource utilization exceed a first threshold associated with the first statistical measure; determining a second condition in which values of the data set corresponding to a time period associated with the current resource utilization exceed a second threshold associated with the second statistical measure; and triggering an anomaly detection operation in response to determining the first and second conditions.