Logic Scaling Sets for Cloud-Like Legacy Application Elasticity
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Solution Overview
Problem
Migrating enterprise applications from on-premise to cloud-computing environments often results in increased costs due to the need for constant availability, and traditional elasticity approaches struggle with selectively shutting down instances, especially those reserved for batch processes, on-demand tasks, or central databases, requiring detailed knowledge of system configurations and operation models.
Innovation Solution
Implementing logic scaling sets that decouple knowledge of system configurations from operation models, allowing for scalable instances to be identified and scaled independently without affecting overall system functionality, enabling automated and flexible resource management based on workload patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If elasticity is applied to shut down instances in cloud-computing environments, then cost savings are achieved, but system functionality is compromised when instances reserved for batch processes, on-demand tasks, or central databases are stopped
Solution Approach 1:
The system segments instances into different scalability categories by creating logic scaling sets that classify instances as scalable or non-scalable. This segmentation allows the elasticity mechanism to selectively shut down only those instances that can be scaled without affecting system functionality, while protecting instances critical for batch processes, on-demand tasks, and central database operations.
2Loss of energy
If traditional elasticity approaches are used to selectively shut down instances, then resource costs are reduced, but device complexity increases due to the need for detailed knowledge of system configurations and operation models
Solution Approach 1:
The system implements self-service by allowing instances to self-identify their scalability characteristics through the logic scaling set classification mechanism. Instances automatically indicate whether they can be scaled based on their operational role, eliminating the need for complex external configuration management and detailed knowledge of system configurations and operation models.
3Loss of energy
If instances are shut down to achieve elasticity, then cost savings are realized, but adaptability is reduced when instances need to be reserved for unpredictable on-demand tasks
Solution Approach 1:
The system implements dynamic adaptability by allowing the logic scaling set classifications to be adjusted based on changing operational requirements. Instances reserved for unpredictable on-demand tasks can be dynamically marked as non-scalable when needed, while maintaining cost savings through elasticity during periods when all instances are not required, thus balancing cost efficiency with adaptability to varying workload demands.
Data Source
AI summary
Methods, systems, and computer-readable storage media for determining, by an instance manager and from a pattern associated with a system executing within a landscape, that a status of the system is to change to scaled-in, the pattern being absent any reference to instances of systems executed within landscapes, in response, identifying, by the instance manager and from a logic scaling set that is associated with the system, one or more instances of the system that are able to be scaled-in, selecting, by the instance manager, at least one instance of the one or more instances, and executing, by the instance manager, scaling of the system based on the at least one instance.