Application-Aware Memory Deduplication in Cloud Computing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing deduplication techniques in cloud computing, such as KSM, are not application-aware and thus inefficient in identifying and eliminating duplicate memory regions across instances of different applications, leading to increased memory consumption and performance overhead.
Innovation Solution
An application-aware deduplication method that parses process information to group instances of the same application and uses binary search trees to identify and merge duplicate memory regions, eliminating the need for manual registration and reducing redundant memory allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing deduplication techniques like KSM are used, then memory deduplication is performed, but the techniques are not application-aware and thus inefficient in identifying duplicate memory regions across instances of different applications
Solution Approach 1:
The system segments memory regions by grouping them according to application identity. Each application's memory regions are organized separately, allowing the deduplication process to efficiently compare only relevant regions across instances of the same application, thereby improving deduplication efficiency without requiring complex cross-application analysis.
Solution Approach 2:
The system performs preliminary actions by parsing process information and grouping memory regions by application identity before the actual deduplication process. This pre-organization of data structures enables faster identification of duplicate regions and reduces the computational complexity during the deduplication phase.
2Reliability
If manual registration of memory regions is required, then deduplication can be performed, but it increases operational overhead and complexity
Solution Approach 1:
The system implements self-service by automatically parsing process information from the operating system to identify and group memory regions by application identity. This eliminates the need for manual registration or configuration by users, reducing operational overhead while maintaining accurate tracking of memory regions across application instances.
Solution Approach 2:
The system uses feedback mechanisms by continuously monitoring process information and dynamically updating the grouping of memory regions based on application identity. This automatic feedback loop ensures that the deduplication process remains accurate and up-to-date without requiring manual intervention or configuration.
3Quantity of substance
If duplicate memory regions are not identified across application instances, then memory consumption increases, but implementing application-aware deduplication requires additional processing overhead
Solution Approach 1:
The system applies local quality by focusing the deduplication process specifically on memory regions within the same application group. Rather than performing blanket deduplication across all memory, the system tailors the deduplication effort to local application contexts, reducing unnecessary processing overhead while effectively identifying and eliminating duplicates within each application's memory space.
Data Source
AI summary
Methods, apparatus, systems and articles of manufacture to deduplicate duplicate memory in a cloud-computing environment are disclosed herein. An example apparatus to deduplicate duplicate memory comprises a parser to parse process information corresponding to instances of an application, a group generator to group process information into application groups based on the process information indicating instances corresponding to the same directory paths and application names, a data structure generator to generate a pair of binary search trees for an application group, and a merge controller to deduplicate duplicate memory contents detected in the application group.