Application Deployment Engine Search Space Pruning

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Solution Overview

Problem

Cloud technologies face challenges in deploying applications optimally across large data centers due to the vast number of potential deployment scenarios, leading to inefficient resource utilization and prolonged decision-making times.

Innovation Solution

An application deployment engine that analyzes network topology data and application templates to identify optimal placement plans within a given time frame, utilizing pruning functions and greedy algorithms to reduce the search space and ensure efficient resource utilization.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If cloud technologies deploy applications across large data centers with thousands or hundreds of thousands of resources, then application deployment flexibility and resource utilization improve, but the complexity of identifying optimal deployment scenarios increases dramatically

Engineering Contradiction:

Improveapplication deployment flexibilityVSAvoiddeployment scenario complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent segments the complex deployment problem into hierarchical levels: (1) application template analysis to identify placement requirements, (2) network topology data analysis to map available resources, (3) candidate placement plan generation based on segmented criteria, and (4) optimization evaluation. This segmentation transforms an overwhelming monolithic problem into manageable discrete steps.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs preliminary actions by pre-analyzing application templates to extract placement requirements before actual deployment decisions are made. Network topology data is pre-processed to identify available resources and constraints. This preliminary analysis creates a structured foundation that accelerates subsequent deployment decisions and reduces real-time computational complexity.

Inventive Principle:

Principle #10Preliminary action

2Manufacturing precision

If the system analyzes all possible deployment scenarios to ensure optimal resource utilization, then deployment optimality improves, but the decision-making time increases significantly

Engineering Contradiction:

Improvedeployment optimalityVSAvoiddecision-making time

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The patent changes parameters by introducing multiple evaluation criteria with different weights (resource utilization, load balancing, network proximity, energy efficiency) rather than optimizing for a single parameter. This allows the system to find satisfactory solutions faster by adjusting parameter priorities based on specific deployment contexts, rather than exhaustively searching for the single optimal solution.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system applies partial action by evaluating a representative subset of deployment scenarios rather than exhaustively analyzing all possible scenarios. The placement plan generator creates candidate plans based on prioritized criteria, and the optimization evaluator assesses these candidates using weighted parameters, providing sufficiently optimal solutions within acceptable timeframes without requiring complete scenario enumeration.

Inventive Principle:

Principle #16Partial or excessive action

3Productivity

If the application deployment engine performs comprehensive analysis of network topology and application templates, then resource utilization efficiency improves, but the computational load and processing time increase

Engineering Contradiction:

Improveresource utilization efficiencyVSAvoidcomputational load

Core Design Contradiction:

ProductivityVSPower

Solution Approach 1:

The system implements self-service by automatically analyzing application templates to extract placement requirements and autonomously evaluating candidate deployment scenarios against multiple optimization criteria. The placement plan generator and optimization evaluator work autonomously to produce optimized deployment plans without requiring manual intervention or external computational resources, reducing overall system computational overhead.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces traditional mechanical brute-force search methods with intelligent algorithms that substitute computational heuristics and evaluation functions. Instead of mechanically testing every possible deployment scenario, the system uses template-based analysis, topology-aware placement generation, and weighted parameter evaluation to efficiently identify optimal solutions with significantly reduced computational load.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS11012503B2Application deployment engine

Publication Date: 2021.05.18 AT&T INTELLECTUAL PROPERTY I L P

AI summary

Concepts and technologies are disclosed herein for an application deployment engine. A processor that executes an application deployment engine can receive an application request. The processor can obtain network topology data that indicates availability of resources of a data center, an application template associated with the application, and a running time during which an application placement plan is to be identified out of a large number of placement scenarios within the running time. The application template can describe an application flow path associated with the application. The processor can identify the application placement plan, where the application placement plan can include an optimal placement of the application at the data center, before a given running time expires by pruning the large search space. The processor can generate a command to effect deployment of the application in accordance with the application placement plan.