Social Distance Workload Placement in Cloud Nodes
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Solution Overview
Problem
Current workload placement techniques in cloud computing environments do not adequately consider social aspects, such as user interactions and sentiments, when determining the geographical location of workloads, leading to suboptimal performance and resource utilization.
Innovation Solution
A method that analyzes social media data to compute a social distance objective function and sentiment analysis, selecting nodes that align with user interactions and sentiments to optimize workload placement, ensuring geographical proximity and positive user sentiment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional workload placement techniques are used, then deployment simplicity is maintained, but user sentiment and social interactions are not considered leading to suboptimal performance
Solution Approach 1:
The patent introduces social media data and sentiment analysis as intermediary elements between workload placement decisions and user interactions. The system analyzes social media data to compute sentiment scores and uses these as additional criteria for selecting optimal node locations, thereby mediating between technical workload requirements and social user preferences without directly modifying the core workload execution mechanisms
Solution Approach 2:
The patent adds a new dimension to workload placement by incorporating social sentiment analysis alongside traditional technical metrics. Instead of considering only computational resources and network topology, the system now evaluates social media data, user sentiments, and interaction patterns as additional dimensions in the placement decision-making process, creating a multi-dimensional optimization framework
2Ease of operation
If workload placement considers geographical proximity to users, then user experience improves, but computational complexity increases due to real-time social media data analysis
Solution Approach 1:
The patent performs preliminary sentiment analysis by continuously monitoring and pre-processing social media data to compute baseline sentiment scores before workload placement decisions are made. This preliminary action creates pre-computed sentiment metrics that can be quickly referenced during placement decisions, avoiding the need for real-time analysis at the moment of deployment
Solution Approach 2:
The system employs self-service mechanisms by automatically scraping, analyzing, and interpreting social media data without requiring manual input or configuration. The workload placement system autonomously computes sentiment scores from public social media data, selects relevant features, and makes placement decisions based on these self-generated insights, reducing the operational burden on users
3Productivity
If social media data analysis is integrated into workload placement, then resource utilization optimizes, but measurement and detection difficulty increases
Solution Approach 1:
The patent transforms unstructured social media text data into structured quantitative parameters by computing sentiment scores, polarity values, and emotional intensity metrics. These transformed parameters can be directly integrated with traditional workload metrics like CPU usage and network bandwidth, creating a unified set of measurable parameters for optimization
Solution Approach 2:
The patent replaces manual or rule-based sentiment assessment with automated computational analysis using natural language processing and machine learning algorithms. Instead of relying on human judgment or simple keyword matching, the system uses computational models to automatically detect, measure, and quantify sentiments from social media data, substituting mechanical human analysis with automated electronic processing
Data Source
AI summary
A content of a social media data is analyzed. The social media data relates to a workload that is to be located on a node. A location corresponding to the social media data is computed. The social media data is regarded as originating from the location. A set of nodes is selected by computing a social distance objective function, the set of nodes includes the node. Each node in the set of nodes is located within a range of distances specified by the social distance objective function. A first subset of nodes is removed from the set of nodes, where the first subset of nodes fails to satisfy another objective function. In response to a second subset of nodes satisfying the social distance objective function and the other objective function, the node is selected from the second subset and the workload is deployed on the node.