Job Operation Program Cloud Autoscale Host List Sync
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Solution Overview
Problem
Existing job management systems struggle to efficiently manage host groups in a timely manner when linked with cloud autoscale functions, leading to wasted resources and delayed adjustments in the number of execution servers.
Innovation Solution
A job operation program that extracts host information and autoscale types using a cloud service's function service during autoscaling events, allowing the schedule server to dynamically change the host list and manage host groups in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the job management system is linked with cloud autoscale function, then the system can utilize elasticity and construct highly reliable system, but the host group management cannot be performed in timely manner leading to resource waste
Solution Approach 1:
The patent implements a feedback mechanism where the schedule server continuously monitors the host list from the cloud autoscale function and automatically updates its internal host group management. When the cloud autoscale function adds or removes execution servers, this information is fed back to the schedule server, which then synchronizes its host group configuration accordingly. This closed-loop feedback ensures that the job management system always operates with up-to-date host information, resolving the timing delay between autoscale events and host group updates.
Solution Approach 2:
The schedule server performs self-service by automatically extracting host information from the cloud autoscale function and updating its own host group configuration without requiring external intervention. The system autonomously manages the synchronization between cloud infrastructure changes and job scheduling configuration, enabling timely adaptation to scale changes while maintaining system reliability.
2Productivity
If the number of execution servers is increased to handle batch processing, then processing capacity is improved, but resources are wasted when servers are not fully utilized
Solution Approach 1:
The patent implements dynamic host group management where the schedule server automatically adjusts the host list based on real-time cloud autoscale changes. Instead of using a static host group configuration, the system dynamically synchronizes with cloud infrastructure changes, adding or removing execution servers from the managed host group as scaling events occur. This dynamic adaptation enables the system to optimize resource utilization by maintaining the appropriate number of active servers according to actual processing demand, improving both productivity and energy efficiency.
Solution Approach 2:
The system changes the parameter of host group composition dynamically by extracting and applying host information from cloud autoscale events. The schedule server modifies its internal host list configuration in response to scale-in and scale-out events, changing the set of active execution servers to match current operational requirements. This parameter change mechanism ensures that processing capacity scales with demand while preventing resource waste from idle servers.
3Device complexity
If manual management of host groups is used, then system complexity is reduced, but the system cannot adapt quickly to cloud autoscale changes
Solution Approach 1:
The patent introduces an intermediary function within the schedule server that automatically extracts host information from cloud autoscale events and translates it into host group configuration updates. This intermediary mechanism bridges the cloud infrastructure layer and the job management layer, enabling automatic synchronization without requiring complex manual configuration or intervention. The intermediary handles the complexity of cloud-autoscale integration internally, maintaining simple operation for users while achieving rapid adaptation to scale changes.
Solution Approach 2:
The schedule server performs self-service by automatically managing host group updates in response to cloud autoscale events. The system extracts host information, processes scale-in and scale-out events, and updates its internal configuration autonomously without requiring manual administrative actions. This self-service capability enables rapid adaptation to cloud infrastructure changes while keeping the user interface simple and the operational complexity low.
Data Source
AI summary
A non-transitory computer-readable recording medium stores a job operation program for causing a computer to execute processing including: in a case of linking a schedule server that distributes batch processing of executing a plurality of jobs in a predetermined order to a plurality of machines and executes the batch processing with a cloud service equipped with an autoscale function, extracting host information to be autoscaled and an autoscale type using a function service included in the cloud service at a moment of autoscaling of one of the machines; and changing a host list in the schedule server on the basis of the extracted host information and autoscale type.