Service Migration Planning Using Log Similarity for Cloud Load Balance
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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, creating a migration plan that balances load distribution across multiple destination cloud environments, and verifies successful migration using checksums.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If online services are migrated to destination cloud environments without considering similarity, then migration speed increases, but resource imbalance and overloading occur
Solution Approach 1:
The system performs preliminary analysis of service similarity using edit distance calculations on transaction logs before migration. This advance preparation enables the migration system to pre-determine optimal destination environments for each service, ensuring balanced resource distribution from the start rather than correcting imbalances after migration occurs.
Solution Approach 2:
The system uses transaction log data to continuously monitor and analyze service behavior patterns. By comparing edit distances between service logs, the system receives feedback on service similarity and adjusts migration decisions accordingly, creating a closed-loop system that optimizes both migration speed and resource balance.
2Reliability
If services are distributed across multiple destination environments without similarity analysis, then load balancing improves, but functional disruptions increase
Solution Approach 1:
The system performs preliminary similarity analysis using edit distance calculations on transaction logs before migration. This advance preparation enables the migration system to pre-determine optimal destination environments for each service, ensuring balanced resource distribution from the start rather than correcting imbalances after migration occurs.
Solution Approach 2:
The system transforms service behavior patterns into quantifiable edit distance metrics by analyzing transaction logs. This parameter transformation allows the system to objectively measure service similarity and make data-driven migration decisions, reducing functional disruptions caused by subjective or arbitrary service distribution.
3Device complexity
If migration is performed without using edit distance metrics, then system complexity decreases, but migration accuracy deteriorates
Solution Approach 1:
The system replaces complex manual service similarity assessment with automated edit distance calculations on transaction logs. This substitution of mechanical analysis with algorithmic processing maintains high measurement precision while actually reducing operational complexity, as the automated system handles what would otherwise require sophisticated manual evaluation.
Solution Approach 2:
The system introduces transaction logs as an intermediary medium to capture service behavior patterns. By analyzing edit distances in these logs, the system obtains precise similarity measurements without directly comparing complex service configurations, simplifying the measurement process while maintaining accuracy.
4Device complexity
If all services are migrated to a single destination environment, then migration process simplifies, but resource overloading occurs
Solution Approach 1:
The system segments services into different migration groups based on edit distance similarity analysis. Services with similar transaction log patterns are grouped together and migrated to appropriate destination environments, preventing any single environment from becoming overloaded while maintaining manageable migration processes through structured categorization.
Solution Approach 2:
The system transforms service behavior patterns into quantifiable edit distance metrics by analyzing transaction logs. This parameter transformation allows the system to objectively measure service similarity and make data-driven migration decisions, reducing functional disruptions caused by subjective or arbitrary service distribution.
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.


