Server Availability Allocation Optimization for Cloud Workloads
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Solution Overview
Problem
Manually assigning server level availabilities to meet workload level availability requirements in a cost-effective manner is inconvenient and difficult, especially in cloud computing environments with multiple redundancy groups and varying resource requirements.
Innovation Solution
A method and system that automatically determine server level availabilities and resource allocations by receiving workload requirements, resource estimates, and server size and availability category options, using optimization techniques to minimize costs and achieve workload level availability requirements, with the aid of a High Availability (HA) optimizer and Pareto efficient solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual assignment of server level availabilities is used, then flexibility in customization is improved, but ease of operation deteriorates and time consumption increases
Solution Approach 1:
The system performs self-service by automatically determining server level availabilities through optimization algorithms without requiring manual user intervention. The computer receives workload level availability requirements and autonomously computes the optimal server level assignments, eliminating the operational burden while preserving the ability to meet specific availability targets.
Solution Approach 2:
The system performs preliminary action by pre-computing optimal server level availability assignments based on workload requirements before actual deployment. The optimization is performed in advance to determine the best configuration, allowing users to simply input requirements and receive ready-to-implement solutions without manual iteration.
2Adaptability or versatility
If manual assignment of server level availabilities is used, then flexibility in customization is improved, but productivity deteriorates
Solution Approach 1:
The patent replaces the mechanical manual assignment process with an automated computational system. The computer executes optimization algorithms to automatically determine server level availabilities, substituting human manual operations with automated processing that achieves the same customization goals at much higher speed and efficiency.
Solution Approach 2:
The system performs self-service by autonomously computing optimal server configurations without requiring manual user intervention. The computer receives workload level availability requirements and independently determines the best server level assignments, eliminating operational bottlenecks and significantly improving productivity.
3Ease of operation
If automatic determination of server level availabilities is implemented, then ease of operation is improved and productivity increases, but device complexity increases
Solution Approach 1:
The patent introduces an intermediary optimization system that bridges the gap between simple user input and complex computational requirements. The computer acts as an intermediary that receives simple workload level availability requirements from users and internally performs complex optimization calculations to generate server level assignments, shielding users from complexity while delivering automated solutions.
Solution Approach 2:
The system segments the complexity by separating the simple user-facing interface (receiving availability requirements) from the complex internal optimization engine. The optimization process itself is segmented into receiving inputs, computing optimal assignments, and outputting results, allowing the complex functionality to be managed in discrete, manageable components.
4Loss of energy
If optimization techniques are used to minimize costs, then loss of energy is reduced and cost-effectiveness is improved, but device complexity increases
Solution Approach 1:
The system applies dynamics by using optimization algorithms that dynamically adjust server level availability assignments based on workload requirements and cost parameters. The optimization process dynamically evaluates multiple possible configurations and selects the most cost-effective solution, allowing the system to adapt to different scenarios while minimizing resource costs.
Solution Approach 2:
The computer acts as an intermediary that manages the complexity of cost optimization internally while presenting a simple interface to users. The system receives availability requirements and cost parameters, then autonomously performs complex optimization calculations to minimize the cost of achieving workload level availability, shielding users from the underlying computational complexity.
Data Source
AI summary
An approach is provided for determining availabilities of servers in multiple tiers of a workload. Based on (1) a required availability of the workload, (2) resource requirements for redundancy groups (RGs) in the workload, (3) sets of server sizes, and (4) sets of availability categories, numbers of server(s) included in respective RGs are determined, allocations to the server(s) of one or more server sizes from a selected set of server sizes are determined, and allocations to the server(s) of one or more categories of availability from a selected set of categories is determined, so that a cost of achieving the required availability of the workload is minimized.