Cloud Service Migration Planning with Edit-Distance Similarity
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Cloud environments face issues such as overloading and improper migration of online services, leading to functional disruptions and resource imbalances due to unsuitable distribution of services across destination cloud environments.
Innovation Solution
A system computes edit distance values based on transaction log data to determine similarity between online services and migrates them to destination cloud environments based on a calculated migration plan, ensuring balanced resource distribution and successful service continuity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If online services are migrated to destination cloud environments without considering similarity, then migration can be performed quickly, but destination cloud environments may become overloaded or improperly distributed
Solution Approach 1:
The system performs preliminary analysis by computing edit distance values from transaction log data before migration occurs. This preliminary similarity assessment enables the migration system to pre-determine optimal destination assignments, avoiding overloading while maintaining migration efficiency. The similarity metric is calculated in advance based on historical transaction patterns.
2Reliability
If services are migrated based on detailed similarity analysis, then resource distribution is optimized, but migration time and complexity increase
Solution Approach 1:
The system replaces complex manual or rule-based migration decision-making with an automated computational approach. Edit distance algorithms automatically analyze transaction log data and compute similarity metrics, substituting mechanical similarity assessment processes with efficient computational methods that reduce both time and complexity.
3Productivity
If similar services are grouped together in the same destination environment, then resource utilization improves, but destination environments may become overloaded
Solution Approach 1:
The system applies different distribution strategies to different destination environments based on their specific capacities and characteristics. Rather than uniformly distributing all similar services to the same destination, the system considers local quality factors of each destination environment to determine optimal service placement, preventing overloading while maintaining resource utilization efficiency.
Data Source
AI summary
Techniques for dynamically migrating services based on similarity are disclosed. In some embodiments, a computer system may, for each online service in a plurality of online services of a source cloud environment, compute a corresponding edit distance value based on a stream of transaction log data of the online service. The edit distance value may comprise a minimum number of edit operations required to change a first log entry in the stream of transaction log data to a second log entry in the stream of transaction log data. Next, the computer system may determine a migration plan based on a measure of similarity between the edit distance values of the online services, where the migration plan specifies a distribution of the online services amongst a plurality of destination cloud environments, and then migrate the online services from the source cloud environment to the destination cloud environments using the migration plan.