Cloud Server Resource Allocation for Macroscopic and Microscopic Traffic
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Solution Overview
Problem
Cloud server operators face challenges in setting appropriate resource specifications due to difficulties in managing macroscopic and microscopic traffic fluctuations, leading to increased costs and service interruptions.
Innovation Solution
An apparatus and method for managing transactions on cloud servers that include a communication module, storage module, access management server, resource optimization server, and operation server to collect, analyze, and control resource allocation based on traffic patterns, using scale-up, scale-down, scale-out, and scale-in operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If resource specifications are increased to handle traffic fluctuations, then service stability is improved, but operating costs increase
Solution Approach 1:
The patent implements dynamic resource allocation by continuously monitoring traffic patterns (macroscopic and microscopic) and automatically adjusting cloud server resource specifications in real-time. This replaces static resource allocation with a dynamic system that adapts to changing traffic conditions, ensuring service stability during peak periods while reducing resources during low-traffic periods to minimize costs.
Solution Approach 2:
The system employs feedback mechanisms by collecting and analyzing traffic status information, detecting traffic increase/decrease patterns, and using this information to automatically adjust resource allocation. The feedback loop continuously monitors service performance and traffic conditions, then adjusts resource specifications accordingly to maintain stability while optimizing cost efficiency.
2Loss of energy
If resource specifications are decreased to reduce costs, then operating costs are reduced, but service stability deteriorates
Solution Approach 1:
The system dynamically adjusts resource allocation based on real-time traffic analysis, automatically increasing resources when traffic patterns indicate potential service degradation risks and decreasing resources when conditions allow. This dynamic approach prevents both over-provisioning and under-provisioning, maintaining service stability at optimal cost levels.
Solution Approach 2:
Through continuous monitoring of traffic status and service performance, the system provides feedback to automatically adjust resource specifications. When feedback indicates stable service performance with reduced traffic, resources are decreased to save costs; when feedback shows traffic patterns that may compromise stability, resources are increased proactively.
3Ease of manufacture
If manual resource configuration is used, then initial setup is simple, but adaptability to traffic changes is poor
Solution Approach 1:
The system implements self-service automation where the cloud server automatically monitors its own traffic conditions, analyzes patterns, and adjusts resource specifications without manual intervention. This self-managing capability maintains initial setup simplicity while providing continuous automatic adaptation to traffic changes, eliminating the need for manual reconfiguration.
Solution Approach 2:
The system performs preliminary actions by pre-configuring automatic resource adjustment mechanisms during initial setup. Traffic monitoring and analysis capabilities are established in advance, enabling the system to proactively detect traffic patterns and automatically adjust resources before performance degradation occurs, maintaining both simplicity and adaptability.
4Device complexity
If resources are allocated based on overall trends only, then configuration is simple, but response to specific traffic spikes is delayed
Solution Approach 1:
The patent segments traffic analysis into two distinct components: macroscopic traffic trends and microscopic traffic patterns. By analyzing both segments simultaneously, the system achieves comprehensive traffic understanding without excessive configuration complexity. This segmented approach enables rapid detection of specific traffic spikes within the broader context of overall trends.
Solution Approach 2:
The system performs preliminary configuration of dual-layer traffic monitoring (macroscopic and microscopic) during initial setup. This pre-established monitoring framework enables immediate detection and response to traffic changes of any scale, achieving fast response speed without requiring complex on-demand configuration adjustments.
Data Source
AI summary
The present disclosure relates to an apparatus and method for managing a transaction for resource optimization of a cloud server, which can manage transactions for resource optimization by simultaneously considering both macroscopic and microscopic traffic of the cloud server.