Cloud Resource Provisioning via Internet Trend Analysis
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Solution Overview
Problem
Cloud computing environments face challenges in dynamically managing computing resources to match fluctuating demand based on internet user activity, leading to potential service interruptions due to insufficient or excessive resource allocation.
Innovation Solution
A system that includes a data analysis module connected to a processor, which predicts demand for computing resources by analyzing internet user activity, such as web page mentions and search engine queries, and automatically adjusts resource capacity by provisioning or deprovisioning resources based on predefined thresholds to ensure optimal performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cloud computing environments allocate fixed computing resources, then system stability is maintained, but service quality deteriorates during demand surges due to insufficient resource allocation
Solution Approach 1:
The patent implements dynamic resource allocation by continuously monitoring internet user activity metrics (web page mentions, search engine queries, social network trends) and automatically adjusting cloud computing resources in real-time based on predicted demand, transforming the static resource allocation system into a dynamic one that adapts to fluctuating user activity patterns
Solution Approach 2:
The system performs preliminary actions by analyzing internet user activity trends and predicting future demand surges before they occur, allowing the cloud environment to pre-provision resources in advance of actual demand spikes, thereby preventing service interruptions rather than reacting after problems occur
2Reliability
If cloud computing environments increase resource capacity to handle demand surges, then service quality is maintained, but resource waste occurs during low demand periods
Solution Approach 1:
The patent implements feedback mechanisms by continuously monitoring internet user activity metrics and using this information to dynamically adjust resource allocation levels, creating a closed-loop system where resource capacity is automatically increased when demand indicators rise and decreased when demand subsides, preventing both service interruptions and resource waste
Solution Approach 2:
The system changes operational parameters by adjusting resource capacity levels based on varying internet user activity parameters (number of web pages mentioning select terms, search engine query volumes, social network trend intensities), allowing flexible adaptation to different demand conditions without fixed resource allocation
3Device complexity
If manual resource management is used in cloud computing environments, then system complexity is reduced, but responsiveness to demand changes deteriorates
Solution Approach 1:
The patent implements self-service by enabling the cloud computing environment to automatically monitor internet user activity, predict demand changes, and adjust resource allocation without human intervention, allowing the system to serve itself by responding autonomously to fluctuating demand patterns based on real-time internet activity analysis
Data Source
AI summary
An embodiment of the invention provides a system and method for managing computing resources in a cloud computing environment, wherein the system includes a data analysis module connected to a processor. The data analysis module predicts the demand for the computing resources, wherein the demand is predicted based on internet user activity. The internet user activity includes: the number of web pages that include at least one select term a predetermined amount of times, the number of times the web pages that include the at least one select term a predetermined amount of times are viewed, and the number of times the select term is entered into an internet search engine. The processor increases resource capacity in the cloud computing environment when the predicted demand is above a first threshold. The processor decreases resource capacity in the cloud computing environment when the predicted demand is below a second threshold.