Cloud Platform Selection via Binary and Runtime Analysis
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Solution Overview
Problem
Application developers face challenges in selecting the appropriate platform (e.g., 32-bit or 64-bit) for cloud computing environments, impacting runtime performance and memory utilization, as they lack expertise in performance and memory optimization.
Innovation Solution
A two-phase process is employed to automatically select the optimal platform by analyzing application binaries, metadata, qualities of service, and application requirements before deployment, and validating using runtime metrics and garbage collection metrics post-deployment, to optimize runtime performance and memory utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If application developers manually select platform (32-bit or 64-bit) for cloud deployment, then they can control runtime performance and memory utilization, but it requires expert knowledge in performance and memory optimization which most developers lack
Solution Approach 1:
The system performs self-service by automatically analyzing application binaries and metadata to determine the optimal platform configuration without requiring developer intervention or expert knowledge. The cloud computing node autonomously evaluates application characteristics and selects whether 32-bit or 64-bit platform provides better runtime performance and memory utilization.
Solution Approach 2:
The system changes the parameter of platform selection from a manual developer decision to an automated system decision based on analyzed application characteristics. By examining binary properties and metadata, the system dynamically determines the appropriate platform configuration (32-bit vs 64-bit) that optimizes runtime performance and memory usage for each specific application.
2Quantity of substance
If 64-bit platform is selected for application deployment, then memory capacity is increased, but runtime performance may decrease for certain applications
Solution Approach 1:
The system dynamically selects between 32-bit and 64-bit platform parameters based on application-specific characteristics analyzed from binaries and metadata. This parameter selection optimizes the balance between memory capacity (64-bit advantage) and runtime performance (32-bit advantage) for each application, rather than using a fixed platform configuration for all applications.
3Quantity of substance
If 32-bit platform is selected for application deployment, then memory footprint is reduced, but runtime performance may decrease for applications requiring larger memory access
Solution Approach 1:
The system dynamically adjusts the platform parameter (32-bit vs 64-bit) based on analysis of application binaries and metadata characteristics. This enables optimization of memory footprint for applications benefiting from smaller 32-bit addresses while avoiding performance degradation for applications that require the larger memory capacity of 64-bit platforms.
Data Source
AI summary
A method, system and computer program product for selecting an appropriate platform to run an application deployed in a cloud computing environment. The appropriate platform is selected by employing a two phase process, where the first phase occurs prior to the deployment of the application and the second phase occurs after the application has been deployed. In the first phase, the cloud computing node selects a platform using various factors, such as application binaries, application metadata and artifacts, and qualities of service and application requirements. In the second phase, the cloud computing node determines whether an alternative platform needs to be implemented for subsequent deployments of the application using various factors, such as application runtime metrics and garbage collection metrics. In this manner, an appropriate platform is automatically selected thereby removing the requirement for the user to indicate the type of platform for the target environment.