Workload Distribution via Behavioral Modeling
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Solution Overview
Problem
Existing methods for distributing computational processes among shared resources are inefficient, as they fail to capture the behavioral patterns of workloads and resources, leading to suboptimal utilization and increased costs due to the need for excess resources to manage workload peaks and variability.
Innovation Solution
A method that determines capable shared resources, predicts workload characteristics accounting for multiple subsystems, and selects the most suitable resource based on these predictions to achieve balanced and efficient utilization, using fingerprinting and behavioral modeling to optimize resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional workload distribution methods are used to ensure service delivery to all users, then sufficient computing capacity is provided, but overhead of shared resources increases leading to increased costs
Solution Approach 1:
The system performs preliminary actions by continuously collecting runtime data and generating behavioral models before workload peaks occur. These models predict future resource needs, allowing the system to proactively allocate resources based on predicted behavior patterns rather than reacting to actual load, thereby reducing the overhead needed to handle unexpected peaks.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring runtime data from computational processes and using this information to update behavioral models. These updated models feed back into the workload distribution decisions, creating a closed-loop system that optimizes resource allocation based on actual observed behavior, reducing the need for excessive overhead resources.
2Reliability
If excess shared resources are provided to manage workload peaks and variability, then service delivery is ensured, but resource utilization efficiency decreases
Solution Approach 1:
By generating behavioral models in advance that capture the runtime behavior of computational processes, the system can predict future workload patterns. This allows for more accurate capacity planning and resource allocation, ensuring that resources are available when needed without permanently provisioning excess capacity that would reduce utilization efficiency.
Solution Approach 2:
The system dynamically adjusts resource allocation based on updated behavioral models that reflect actual runtime behavior. Rather than static over-provisioning, the system adapts resource distribution in real-time based on observed patterns, maintaining reliable service delivery while improving overall resource utilization efficiency through dynamic optimization.
3Device complexity
If traditional distribution methods are used without behavioral modeling, then implementation is simpler, but load distribution uniformity and resource optimization are suboptimal
Solution Approach 1:
The system performs preliminary behavioral analysis by collecting runtime data and generating models before actual workload distribution occurs. This upfront modeling work, while adding some complexity, enables significantly improved load distribution uniformity and resource optimization in the actual distribution phase, as decisions are based on predicted behavior patterns rather than simple heuristics.
Data Source
AI summary
A method for distributing at least one computational process amongst shared resources is proposed. At least two shared resources capable of performing the computational process are determined. According to the method, a workload characteristic for each of the shared resources is predicted. The workload characteristic accounts for at least two subsystems of each shared resource. One of the at least two shared resources is selected based on the predicted workload characteristics.